Every number has a story to tell. In the first essay in this series, I argued that the agentic foundation is taking shape and the knowledge graph holds the plot. (New to knowledge graphs? I wrote a primer on what they are and why the most valuable thing your company knows has never been written down.) This essay states the central claim of the series plainly, because burying it would be a disservice: the learning organization is finally buildable. The enterprise that understands its customers better every day, that compounds what it learns instead of merely recording what it did, has moved from leadership aspiration to engineering decision. Here’s the story of why that’s suddenly true.

Start with the assumption that has to go: that your company understands its customers because it measures them. We have superb mathematics for operations. Supply chains, logistics, labor productivity, same-store sales, cash conversion: fifty years of enterprise systems made the what of business precise to the decimal. None of that precision explains why a customer trusted you in March and hesitated in June. We measure everything that happens and almost nothing about why.

That’s not a technology gap. It’s a cognitive one, and behavioral economics spent fifty years documenting it. Daniel Kahneman and Amos Tversky showed that we reason from whatever information is most available, and that when a question is hard, we quietly substitute an easier one and never notice the swap. ‘Why are customers leaving?’ is hard. ‘What did conversion do last week?’ is easy. So the Monday review answers the easy question and files it under the hard one. In Thinking, Fast and Slow, Kahneman gave the pattern a name: what you see is all there is. The dashboard becomes the whole story precisely because it’s the story on the screen. Humans are bad at causality, brilliantly bad, in predictable directions. Marketing didn’t decide to ignore why. It inherited instruments that made the bias structural. We industrialized System 1 and called it analytics.

Watch the bias play out in a company you’d recognize. In March, the onboarding refresh ships. The copy is cleaner, more confident, more feature-led. Completion rates hold, the dashboard stays green, the team moves on. But the new language has quietly stopped answering a question first-time customers need resolved before they commit. A cause is now in motion, and no instrument in the building can see it. Through April, that missing question surfaces again and again in support conversations. In May, customer language in those conversations turns less certain. In June, activation dips. In September, renewals miss, and the room does exactly what Kahneman would predict: it reaches for the most available explanation. Pricing pressure. A competitor’s launch. Seasonality. The March decision is six months old and three handoffs away, and because it was never measured, it’s never suspected. The company doesn’t learn the lesson. It buys a better churn model.

Multiply that story across every page, campaign, and conversation the enterprise ships in a year, and you have the real cost of counting: not one soft quarter, but an organization that repeats its mistakes with increasing efficiency. That’s the quiet tragedy of modern marketing. The discipline didn’t lose its craft. It buried the craft under a MarTech stack that automates activity without understanding it, and the newest AI content tools are making it worse, regressing every brand toward the statistical mean, faster and cheaper than ever.

Now run the same story with instruments that can read. Same company, same March refresh. This time the copy ships into an agentic infrastructure: AI agents, built on frontier models like Claude, that read every asset the way your best brand mind would, and a knowledge graph that keeps content, customers, conversations, and outcomes connected across time. Within days, semantic analysis flags that the new copy has drifted from the questions first-time customers actually ask. That isn’t intuition. It’s a measured distance between two mental models. By April, the system reads sentiment decaying in support conversations and scores it with enough mathematical precision to alert on: customers are measurably less confident, on specific pages, about specific promises the brand made. The Monday review doesn’t open with an argument over attribution. It opens with a causal thread the junior analyst can trace as easily as the CMO, because the thread lives in the graph, not in a veteran’s head. The team forms a hypothesis, tests a revision, and the enterprise remembers the answer either way. Marketing stops performing certainty and starts practicing curiosity.

This is the company Peter Senge described a generation ago in The Fifth Discipline: the learning organization, where understanding compounds. Executives loved the idea, and once you’ve seen both versions of March, you know why. Almost nobody could build it, because you can’t run a learning organization on instruments that record without compounding and obscure causality by design. That’s the decision in front of leadership now, and it’s genuinely new. Keep optimizing the counting, or build the reading. Keep the stack that measures motion, or add the layer that measures meaning.

The rest of this essay shows what that layer reads and how the loop closes. The techniques exist today, grounded in decades of research, from computational linguistics to semiotics to the behavioral science above. For the first time, we have a genuinely granular understanding of language, and we can see its effect on customers at ever finer segmentation. There’s still a great deal to learn about how humans experience content, what moves them, and what they find pleasing. That’s not a caveat. It’s the point: those questions are finally answerable by instrument instead of argument. And the argument of this essay needs only one sentence: that organization isn’t measuring more. It’s measuring differently.

Motion, not motive

For seventy years we’ve lived under the most quoted line in management: what gets measured gets managed. The problem is what we chose to measure. We counted impressions, clicks, bounce rates, and conversions because they were easy to count, which is the availability bias wearing a lanyard. So marketing became exceptionally good at describing motion and stayed surprisingly weak at explaining motive.

Attribution tells you which door the customer came through. It doesn’t tell you why they knocked. A conversion rate hands you the last sentence of a story with every page before it torn out. And because activity metrics arrive fast while changes in trust and comprehension arrive slow, the available numbers drag every decision toward the short term.

That’s how the flywheel starts spinning backward. Trust erodes quietly for three quarters while the dashboards stay green. By the time a metric turns red, the decision that caused it is six months old and three handoffs away.

A learning organization can’t run on instruments that only see the last click. It needs instruments that preserve context, surface patterns, and help the enterprise test why.

Content is an intervention, not inventory

Start with the asset the enterprise measures worst: its own voice. Most companies manage content as inventory, a production queue of pages, campaigns, and assets to ship and eventually replace. But every message, image, and interaction is also an intervention in the customer relationship. It shapes emotion, understanding, intent, and action, right alongside price, product, and service.

Which makes every piece of content a live experiment, whether the company designed it as one or not. Most organizations record what the content cost and how many people clicked it. Almost none measure what changed after exposure: comprehension, confidence, trust, the customer’s sense of what the brand stands for. For decades that was fair, because meaning couldn’t be measured consistently at scale. AI doesn’t make meaning perfectly measurable. It makes meaning observable, comparable, and monitorable, and that’s enough to change the job.

Five ways to read

AI can now read content across dimensions that used to live in expert judgment, and it can do it across thousands of touchpoints at once.

Style and tone. Style isn’t decoration. It’s a credibility signal. Measured across channels, tone reveals whether the brand sounds like one company or twelve. Consistency of voice doesn’t create trust by itself. Inconsistency quietly spends it.

Sentiment and emotional framing. Emotion leads. Behavior lags. Excitement, confusion, skepticism, and frustration show up in customer language before they show up in retention or revenue, which makes sentiment the earliest tripwire you can set.

Semantic clarity. Does the content match the customer’s mental model or the org chart’s? A customer never reports that your taxonomy is confusing. They just fail to find the next step, abandon the journey, or call support. Confusion doesn’t file a complaint. It leaves.

Semiotic signals. Customers read symbols: color, imagery, category cues, ritual. A brand is a set of expectations customers learn to predict, and every asset either keeps that promise or breaks it. Semiotic drift is how a brand blurs while every individual asset still looks good.

Behavioral correlation. The payoff dimension: connecting content attributes to downstream outcomes. Did the warmer tone precede more qualified demo requests? Did the revised onboarding precede higher activation? Correlation doesn’t settle the question. It tells you where to look and what to test.

These aren’t replacements for financial or behavioral metrics. They’re explanatory signals, and they can’t live in another disconnected spreadsheet. They belong in the knowledge graph from the first essay, where a content version, a customer segment, a support theme, and a renewal decision stay connected across time. The graph holds the plot. These dimensions are how you read it.

What reading looks like

Go back to the March story and look at the mechanics. The connection between the content version, the language shift, the affected cohorts, and the later outcomes produced a causal hypothesis, not a verdict. Correlation opened the investigation. The test closed it. The team ran the revised sequence against the original, controlled for customer mix, and watched whether comprehension, activation, and retention moved together. The result fed back into the graph and sharpened the next diagnosis.

Notice what happened to the people in that story. Nobody got blamed for a red number. A hunch became a hypothesis, the hypothesis became a test, and the test became institutional memory. That’s Senge’s discipline made operational: the system learned, and so did everyone who touched it. Analytics reports what happened. Reading builds an explanation, tests it, and remembers.

From campaign metrics to system health

This changes marketing’s job description. Campaign measurement asks whether a burst of activity worked. System measurement asks whether the relationship is getting stronger, clearer, and more coherent over time. The first question produces reports. The second produces learning.

Read this way, marketing becomes the sensory system of the learning organization. It detects a change in customer confidence while it’s still a whisper. It catches the gap between the promise, the experience, and the outcome. It feeds what the market is teaching back into product, service, and strategy instead of trapping the lesson inside a campaign recap nobody reads. And it gives marketing’s craft knowledge, the instincts about language, story, and emotion dismissed as soft for decades, a way to become observable, testable, and scalable.

That’s the promotion marketing has been waiting for. Not a bigger budget. A bigger job: teaching the enterprise what its communication changes, for whom, and with what consequences.

What happens next

What gets measured gets managed. That won’t change, and it shouldn’t. What’s changing is what the enterprise can observe. Trust, clarity, resonance, and coherence are still imperfect constructs. They no longer have to be invisible ones.

The timing matters. Generative AI is flooding every channel with more content, made faster and cheaper. Production is becoming abundant. Understanding isn’t. The companies producing the most content and the companies learning the most from their content are about to become very different companies.

The sequence only runs in one direction. Measure meaning. Find the pattern. Form the hypothesis. Test it. Feed the result back. Skip the test and correlation becomes certainty theater. Skip the feedback and the enterprise repeats the lesson without learning it. Complete the loop and understanding compounds.

The executive question

There’s a test you can run this week, and it costs nothing. In your next marketing review, pick any number on the dashboard and ask why it moved. Then ask three more questions. What evidence connects the proposed cause to the outcome? What competing explanation fits the same facts? What test would tell them apart?

If the conversation stops at attribution, you have counting instruments. If the team can trace the relationship across content, context, behavior, and time, and can say how it would test the explanation, you’re building reading instruments.

That’s the decision in front of leadership. Not whether to measure, but whether to design the organization to learn: from every customer interaction, in real time, with causation you can trace instead of guess. I use a structured assessment to evaluate whether a company’s content, data, semantic, and feedback architecture can support that design, and where the learning loop is breaking.

Because the payoff compounds. Run the loop end to end, interaction after interaction, and the advantage isn’t a model or a platform a competitor can license. It’s an enterprise that understands its customers a little better every day, and people who get smarter along with it.

The learning organization was never a fantasy. It was waiting for its instruments. They’re here.

Next in the series: The Enterprise Learning Loop, how observation becomes hypothesis, evidence, institutional memory, and better action.

Disclosure: I have a commercial interest in this space. I build and advise on semantic AI, knowledge graph technology, and customer experience measurement systems.

Executive summary

Peter Drucker gave us the term knowledge worker in 1959, and the emphasis was always on the first word. Knowledge workers did more than produce reports. They interpreted what systems recorded, reconciled conflicting evidence and supplied the meaning that made the output useful.

A controller handed a margin figure doesn’t stop at the figure. She examines the contracts behind it, removes a one-time concession and reaches a conclusion she can defend to the board: the underlying trend is flat, not falling. The report is the artifact. What she knows about the business is the product.

Much of that context was never recorded, because the person who held it was in the meeting. As parts of knowledge work are automated, the unresolved question is who, or what, will preserve the definitions, relationships, exceptions and evidence behind the numbers.

The number was in the system. The story was in the person.

This is why many internal AI deployments disappoint in a similar way. A capable model is connected to the company’s documents and systems, yet some answers remain fluent, plausible and wrong: the cited policy was superseded, two divisions use different definitions of active customer, or two figures should never have been combined. The model can retrieve what the company recorded. It cannot use context that was never made explicit.

A knowledge graph is where that context gets made explicit, connected and reusable. It won’t hold everything a person knew, and no formal system should be expected to, but it holds the part that repeatedly drives decisions: the entities, the relationships, the definitions and the evidence. Google’s 2012 Knowledge Graph demonstrated the distinction at enormous scale. Traditional search matched strings, while the graph added entities and relationships that let search interpret many queries as questions about things. Google described the shift as things, not strings. The enterprise lesson follows directly. Finding documents about your business is not the same as modeling it well enough to answer questions about it.

The investment therefore has a different shape from a conventional technology project. Models can perform much of the mechanical work, including proposing taxonomies, extracting entities and drafting semantic models, but they cannot authoritatively decide what the business means. Whether a trial account counts as a customer, or which definition of margin applies in a given context, remains a business judgment. The enduring requirement is knowledge stewardship: people with the authority to make, document and maintain those decisions.

Automation may reduce staffing in some knowledge-work activities, and it creates a smaller and different need for stewardship. The work doesn’t vanish; it changes form and location. Most large enterprises have someone who owns the data. Few clearly own what it means.

One point belongs in a summary because it decides whether any of this pays. Enterprise AI programs rarely stall because the model lacks capability. They stall because nobody can show that the answers are right. What high-stakes functions require is narrower and harder than accuracy in the abstract: answers that are complete, current, and traceable to their evidence. A model grounded in a well-governed knowledge graph can return not only an answer, but the entities, relationships and sources used to construct it. That traceability is what separates a limited pilot from a system the organization is prepared to trust.

What follows provides an executive working knowledge of the subject: what a knowledge graph is, how its parts fit together, why identity and open standards matter, where ordinary retrieval falls short, and why governance remains a business responsibility. Enough to direct your teams, test vendor claims and understand what you are funding. For the argument this primer supports, see "Your Analytics Measure Motion, Not Meaning."Someone always knew why

Picture a quarterly business review from a few years back, before anyone in the room had said the words artificial intelligence out loud. The deck shows enterprise revenue down four percent. The number is correct, produced by a system, reconciled and signed off, and incomplete on its own.

What makes it useful is the woman who stands up and explains that the decline is two accounts, both of which moved to a new billing schedule, that one has already recovered in the current quarter, and that the trend underneath is flat rather than falling. She had pulled the contracts, called the account team, remembered a conversation from March, weighed two explanations and thrown one away. By the time she stood up, the number had stopped being a number and become an explanation the business could act on.

Systems count. People find out why. The finding out is where the value sits, and it has always been done by people across finance, operations, marketing and service whose real job, whatever their title said, was working out what the numbers meant and telling everyone else.

Michael Polanyi gave this a name in 1966. “We can know more than we can tell,” he wrote, describing the tacit dimension of expertise. Your organization runs on it: the veteran who knows which report to trust, the account manager who calls a customer before an invoice goes out, the correction made in a meeting that never reaches a document.

No knowledge graph can capture all tacit knowledge, and no formal system should be expected to. It can, however, preserve the entities, relationships, definitions, evidence and explicit judgments that repeatedly shape decisions. Systems of record were designed to capture outcomes, not the reasoning behind them, because the reasoning was usually standing in the room. That work was part of the job but rarely part of the job description, which is why it can disappear before anyone notices what was lost.

What happens when you point a model at everything

IT stands up an internal model with access to the whole enterprise: document stores, wikis, ticket history, the data warehouse and the intranet nobody has pruned since 2016. They add retrieval, put a chat window on it and open it to the business. The effort is serious and usually well engineered. Yet the answers are often not accurate or complete enough for consequential decisions.

The answers are not absurd, which is the difficulty. They’re fluent, they cite real documents, and they’re subtly wrong in ways only the person who used to do the job can catch. The system quotes a procedure that was superseded. It counts customers using a definition marketing abandoned two years ago. It adds two figures that were never meant to be added, because nothing in the data said they couldn’t be. Each answer is defensible on its face and wrong in a way that costs something.

The usual response is to reach for a better model, a longer context window or a different vendor. That response fails because the diagnosis is incomplete. The system may have everything the company recorded while still lacking what the analyst knew, what the account manager remembered or what the compliance officer would have caught. Context that was never represented cannot be retrieved reliably.

Artificial intelligence didn’t create this problem, and it won’t solve it on its own. What it does is distribute conclusions at scale without the context that originally made them meaningful. A knowledge graph is how an organization makes the reusable portion of that context explicit and machine-readable.

Four names, one customer

Start with a problem common in large enterprises. In the customer relationship management system the account is Acme. In the signed contract it is Acme Corporation. In finance it is ACME Holdings Inc. In the board deck it is our largest Midwest account. Four names may refer to one legal entity, several related entities or different concepts altogether, and a language model cannot resolve that reliably from text alone.

This is the distinction Google emphasized when it introduced its Knowledge Graph. A string is text. A thing has an identity. A knowledge graph addresses the enterprise version of the problem by assigning a persistent identity and recording relationships to other identified things. Acme is a Customer. Acme holds a Contract. That Contract covers a Service. That Service runs on a Platform. That Platform failed on Tuesday.

Nobody wrote a document saying Tuesday’s outage put our largest Midwest account at contractual risk, so ordinary document search can’t retrieve that sentence. But the relationship path exists, and a graph can traverse it. That is the core idea: a knowledge graph is a machine-readable model of the things a business cares about, the relationships between them, and the constraints and evidence that give those relationships meaning.

What a knowledge graph is made of

Writing down organizational meaning requires a language precise enough for a machine to use. A knowledge graph is built from four layers that stack: the words, the categories, the grammar and the statements.

Figure 1. The four building blocks. Each layer adds meaning the layer below can’t express.

The words

A controlled vocabulary is an agreed list of terms, with one preferred label per idea and the synonyms people actually use. Legal writes counterparty. Sales says client. Support says account holder. Nobody has to change their language, because the vocabulary records that these are three names for one concept. A glossary tells humans what words mean. A controlled vocabulary tells machines that two documents using different words are discussing the same thing.

The categories

A taxonomy arranges those terms so that broader and narrower relationships are explicit. A Managed Services Agreement is a kind of Service Contract, which is a kind of Contract. A question about contracts now finds the managed services ones without anyone listing every subtype, which matters more than it sounds: most enterprise searches fail because the person asking didn’t know the right vocabulary to ask with.

The grammar

An ontology is a formal model of what kinds of things exist, how they may relate and which constraints apply. A Customer may hold a Contract. A Contract may not hold a Customer. The model may specify that every Contract has exactly one governing jurisdiction. A Subsidiary may inherit a master agreement’s terms unless explicitly overridden. The ontology doesn’t contain every business fact; it defines the structure within which facts can be represented and checked.

An ontology does not enforce policy by itself. It makes relevant concepts and constraints explicit and machine-readable. When those constraints are connected to validation logic, permissions or operational controls, a system can test a proposed action before completing it and reject actions that violate the encoded rules.

This is also where these programs become genuinely difficult, and the hardest part is often not technical. To define Customer as one legal entity under one master agreement, someone must decide how to treat divisions that have historically counted regional subsidiaries separately. To formalize revenue recognition, someone must resolve conflicting practices. Each decision affects reporting, incentives and authority, so ambiguity usually has a constituency.

This is why the work fails when it is delegated too far down the organization. It crosses divisions, and a director may not have the authority to settle a definition disputed by peers. Drafting the semantic model is increasingly tractable. Adjudicating contested business meaning is not, and it requires an accountable owner with authority across the affected domains.

An ontology formalizes business meaning. Deciding that meaning is not an IT decision.

The statements

The populated statements about your actual customers, contracts, products, policies and people, connected according to the model. The first three layers define the language and structure. This layer fills that structure with identified entities, relationships and supporting evidence. In practice the layers evolve together: populating the graph exposes gaps in the vocabulary and errors in the ontology.

Put the four together and the roles become clear. The vocabulary defines preferred labels and synonyms. The taxonomy organizes broader and narrower categories. The ontology defines permissible relationships and constraints. The knowledge graph records statements about the actual business using that shared structure. Everything that follows concerns what this makes possible and what is required to keep it trustworthy.

Whose number is right?

A semantic layer is a governed model of meaning that sits between data and the applications that use it. Sometimes it establishes one enterprise definition. Sometimes it records several legitimate definitions and the contexts in which each applies. The goal is not artificial uniformity; it is explicit, reusable meaning. A knowledge graph can provide the structure for that layer.

Most enterprises have fragments of such a layer rather than one coherent model. The reporting tool has one definition of active customer. Marketing has another. Forecasting has a third, buried in a query written by someone who has left. The definitions may each be valid for their purpose, but the differences are rarely explicit, which is why leadership teams spend meetings arguing about whose number is right instead of deciding what to do.

That disagreement used to be resolved by a person, one meeting at a time, through experience and institutional memory. A semantic layer makes the definitions, contexts and sources explicit, so systems and models can apply them consistently, or explain why different definitions produce different answers.

Why standards turn this into an asset

A company can build a knowledge graph with proprietary technology, open standards published by the World Wide Web Consortium, or a combination of both. The choice has technical and economic consequences. Executives don’t need to memorize the acronyms, but they should understand what each standard contributes.

IRI A permanent web address for every concept and every thing. Not a label, an identifier.

RDF The format for stating a single fact: subject, verb, object. The smallest possible unit of meaning.

SKOS The standard for vocabularies and taxonomies. Preferred terms, synonyms, broader and narrower, across languages.

OWL The standard for rules and inference. States what must be true, and lets machines derive facts nobody typed.

SPARQL The query language. One way to ask questions across everything the graph knows.

Why one identifier changes the economics

Persistent identity carries most of the commercial weight. Giving every concept and entity a durable identifier sounds like a technical detail. It is the mechanism that allows information from different systems to accumulate around the same thing.

Shared identity reduces repeated reconciliation. Once an entity has a persistent identity, finance’s view of Acme, support’s view, the contract, the shipping history and last week’s escalation can attach to the same entity without requiring every pair of systems to be reconciled independently. A more complete customer view becomes a property of the shared model rather than another isolated integration project.

New sources connect rather than fully merge. After an acquisition, the acquired customer list can be linked to existing entities by asserting where identities match, while the source systems remain intact. This reduces repeated reconciliation; it does not eliminate source integration or data-quality work.

Answers become more auditable. When the graph preserves provenance, a system can show which facts it used, which entity each fact concerns and where the fact originated. That is more defensible than pointing to a paragraph that happened to contain a similar name.

Institutional memory survives reorganization. Departments are renamed, systems replaced and vendors changed, while stable identifiers preserve continuity. What the company recorded about a customer in 2019 can remain attached to the same entity in 2029, provided identity and provenance are maintained.

Taken together, these mechanisms change the integration cost curve. In a conventional point-to-point architecture, each new source requires new reconciliation against systems already connected. Shared identifiers and reusable semantics reduce that repeated work, because a source can connect once to the common model and become available to multiple consumers. The cost doesn’t disappear, and poor source data still has to be addressed, but integration becomes more cumulative and less repetitive. Over a decade of acquisitions and platform changes, that difference compounds.

The remaining standards support that accumulation. Common fact formats make datasets easier to combine. Controlled vocabularies connect synonyms such as churn and attrition. Ontologies support constraint checking and inference when the necessary relationships are represented. Open specifications also improve portability across tools, although they do not guarantee vendor independence. A model of the business may be a twenty year asset. The platform hosting it may not be a twenty year company.

Two graph models, and why the choice isn’t the decision

Your technical teams will raise a distinction between two kinds of graph. Property graph and Resource Description Framework (RDF) models emphasize different concerns. Property graphs are commonly optimized for efficient traversal and operational workloads. RDF graphs emphasize global identifiers, standards-based integration, semantic interoperability and inference. The distinction is real, and modern products increasingly blur it.

Many mature enterprises use both patterns. The executive question is not which graph model is universally superior, but which business questions, interoperability requirements and governance needs the architecture must support. The practical risk is not choosing wrong. It is spending a year choosing. A team still debating graph models has not started building, and the business questions will settle the matter faster than the debate will.

Why enterprise AI often stalls at the compliance door

Enterprise AI programs rarely stall because the model lacks capability. They stall because nobody can show that the answers are right. What high-stakes functions require is narrower and harder than accuracy in the abstract: answers that are complete, current, and traceable to their evidence.

A common deployment pattern is retrieval-augmented generation, or RAG. Rather than relying only on what the model learned during training, the system retrieves relevant text from enterprise sources and asks the model to answer from that material. It is a sound and useful technique. Its quality depends heavily on what is retrieved, how complete and authoritative the sources are, and how permissions and versions are handled. Ordinary similarity retrieval has four structural limitations that tuning alone does not remove.

Similarity is not the same as authority. A superseded 2019 travel policy may resemble a question about the current travel policy as strongly as the current document does, unless version, status and authority are represented and enforced.

Similarity retrieval alone does not guarantee complete aggregation. How many active contracts have automatic-renewal clauses expiring in the third quarter requires an exhaustive set, a governed definition of active, and a counting operation. Retrieving a few similar contracts and asking the model to generalize is not the same task.

A text retriever does not automatically follow multi-step relationships. Which customers are exposed to Tuesday’s outage may require a chain from outage to platform, platform to services, services to contracts and contracts to customers. Each relationship has to be represented, or discovered with additional tools.

Identity is not inherent in text retrieval. Documents that say Acme and documents that say ACME Holdings may be treated as unrelated unless entity resolution establishes that they refer to the same legal entity, or records that they do not.

These limitations create a broader problem: the system may not know that its evidence is incomplete. It presents the best material it found with the same fluency whether retrieval was comprehensive or partial. Fluency is a weak signal of evidentiary quality.

The arithmetic that kills pilots

Put an economic frame around that. A system that is often right but cannot identify its exceptions still requires a qualified person to verify every consequential answer. Verification erases the expected savings, because the work has moved from producing the answer to checking it, and checking usually takes the same expertise the program was intended to free.

The constraint becomes sharper in high-stakes functions. Where answers cannot be traced to their evidence, compliance is unlikely to approve, legal is unlikely to rely on the output, and human resources and regulated business units tend to restrict use altogether. The exclusions land on the highest-value work, which leaves a pilot confined to lower-stakes tasks and returns that do not support the original business case.

The model may be capable. The deployment is still missing the evidence architecture required to use it safely.

What changes when the graph is underneath

Figure 2. The same question, two paths. The difference isn’t the model. It’s what the model is handed.

With graph-grounded retrieval, often grouped under the label GraphRAG although implementations vary, the sequence changes. A question can be resolved to identified entities rather than only to words. The system can then traverse relevant relationships, apply recency, type and permission constraints, and retrieve a smaller set of structured facts with provenance. The model’s task narrows from finding and interpreting an open-ended corpus to explaining evidence selected by an explicit model, which reduces the opportunities for unsupported inference.

Andreas Blumauer, who founded the Semantic Web Company in 2004 and built PoolParty into one of the longest running enterprise knowledge graph platforms, illustrates the distinction with a medical example. Search a document store for insomnia and you get documents that mention insomnia. In a knowledge graph where the relevant relationships have been represented, the system can traverse from insomnia to possible causes, treatments, related conditions and interactions. One approach finds text about a topic. The other follows explicit relationships among identified concepts.

A search-and-chat system can produce a plausible answer. A graph-grounded system can produce an answer with an evidence path and chain of custody.

The two approaches belong together rather than in competition, and treating them as alternatives is an expensive mistake. The model supplies language understanding and explanation. The graph supplies identity, governed relationships, permissions and provenance. Without the model, the graph is difficult for most users to query. Without a semantic and evidentiary layer, consequential answers remain difficult to audit. Auditability is the price of admission to the enterprise use cases worth funding.

Two standard objections

“The models are good enough”

The case for knowledge graphs as it was often made in 2018 assumed models that could read little, reason only over short contexts and use few tools. Those assumptions are much less true today, and part of the objection lands. Retrieval is inexpensive and useful for a large class of questions, and organizations no longer have to structure everything before deriving value from anything. At the same time, models can now propose taxonomies, extract entities and draft ontologies for expert review, sharply reducing the effort required to produce a first version.

What has not changed is the need for authoritative business context. A model may infer that ACME Holdings and Acme Corporation are related, but it cannot authoritatively decide that they are the same legal entity under a particular agreement. Access rules attach to entities and concepts rather than to paragraphs. The origin of a number has to be traceable. Whether a trial user counts as a customer remains a governed business decision. Capable models didn’t remove the need for organizational context. They made its absence easier to see.

“This is knowledge management, and knowledge management failed”

Executives who lived through the 1990s often raise this objection before technologists do, and the history matters. Nonaka and Takeuchi published The Knowledge-Creating Company in 1995, a decade of enterprise knowledge-management programs followed, and many ended in abandoned portals, unmaintained taxonomies and a lasting allergy to the phrase.

The difference now is structural rather than a matter of better intentions. Earlier programs faced three persistent constraints: capture was largely manual and competed with people’s real jobs; the consumer was a person who had to remember the portal existed and search it; and taxonomies were maintained by hand and decayed as the business changed. Each constraint can now be reduced. Models extract candidate entities and relationships from existing material. Machines consult the model as part of a workflow rather than waiting for a person to search a portal. Maintenance can be assisted by machine-generated proposals that people review and approve.

What has not moved is governance. Someone still has to decide what the words mean. That was the part knowledge management got wrong by treating it as a librarian’s task rather than an executive’s, and it is the part this paper insists on hardest.

The end of the fifth version

Enterprise content is often written once, filed inconsistently, found rarely and reused less than it should be. Many large organizations have the same product explanation written five or six times by teams that did not know the other versions existed. A semantic layer changes the operating model at three points.

Creation. Before writing, an author can see what already exists on the concept, in every language and every format. The fifth version doesn’t get written. What gets written is the piece that’s actually missing, and the gaps become visible instead of assumed.

Curation. Tagging against a controlled vocabulary can be done automatically and consistently across the whole corpus, including the archive nobody was ever going to touch by hand. Consistency at that volume is a machine job. People should be deciding what the categories are, not applying them ten thousand times.

Dissemination. Content can shift from fixed pages toward reusable components assembled for a particular context. This is the argument I have been making about personalization for a decade: the relevant variables are the customer, the content and the context. Many organizations identify the customer but underuse the signals that explain what the person responds to and why the person is here now.

Then there is the reinforcing loop. What people ask reveals what the graph is missing, turning failed queries and abandoned sessions into a curation work list. Models become better at using the graph, and the graph improves through governed feedback from actual use. The asset becomes more useful over time, provided the feedback is reviewed rather than accepted automatically.

Agents act, so they need the rules in writing

Everything above matters more once systems stop answering questions and start taking actions. An agent differs from a chatbot in the one respect that changes the risk profile entirely: it does things. It files, orders, schedules, escalates, updates and commits. A wrong answer is embarrassing. A wrong action is expensive and sometimes cannot be undone.

Acting safely requires at least three capabilities, and a knowledge graph supports each. The first is a shared map: what exists, what it’s called, how it relates. The second is constraints, represented so that authorization, validation or workflow controls can check them before an action completes. The third is an audit record at the level of entities, facts and actions rather than chat transcripts, which requires provenance and decision logging designed in from the start.

Underneath all three sits a constraint that predates artificial intelligence: you cannot safely automate a process whose entities, decisions and boundaries cannot be described. An ontology formalizes part of that description where a machine can use it. Deploying agents against unmodeled data means allowing automation to rely on assumptions that were never made explicit. That works in a demonstration and fails in operation.

Automation cuts headcount, curation adds some back

Which brings us back to the question this paper opened with. Once the people who knew are gone, who knows? The answer is nobody, unless the organization makes it somebody’s job.

Models now handle a substantial share of the mechanical labor: proposing taxonomies, extracting candidate entities, drafting ontologies, tagging archives no team would process by hand. What once took months can produce a first draft in weeks. That shift is one reason the economics are more favorable than they were in 2018.

What models cannot do is decide. Whether a trial account is a customer. Whether a distributor is a channel, a customer, or both depending on the contract. Which of two divisions’ definitions of margin survives contact with the other. These are judgments about what the business means, made by people with the standing to make them stick, and then maintained as the business changes, because the business always changes.

Organizations that automate knowledge work therefore take on a requirement many have not formally staffed: knowledge stewardship. At enterprise scale it is not a project with an end date but an ongoing capability responsible for keeping the company machine-readable, with an owner, a budget, a governance forum and enough authority to resolve definitions across divisional lines. It sits between the data organization, which owns the infrastructure, and the business, which owns the meaning.

Most large companies have someone who owns the data. Few clearly own what it means.

The direction here is clear and the scale isn’t. Nobody can yet tell you how large this function needs to be relative to the roles being automated, and anyone quoting a ratio is guessing. What isn’t in doubt is that the work exists, that it doesn’t disappear when the knowledge workers do, and that on most organization charts it currently has no home. For anyone building a business case, that means some of the savings from automating knowledge work has to be spent writing down what those knowledge workers knew. Skip that step and you haven’t automated the function. You’ve deleted it.

What a knowledge graph won’t do

• It isn’t a database replacement. A knowledge graph doesn’t store everything. It stores meaning and identity and points at the systems holding the rest. Anyone proposing to migrate your transactional data into a graph should be asked why.

• It will not survive without an owner. Vocabulary and definition decisions affect incentives, reporting and authority. Someone must be accountable for resolving disputes and maintaining the result as the business changes. Governance failures are at least as consequential as technology failures.

• Enterprise-wide first attempts carry high failure risk. Attempts to model the whole business before demonstrating value have a long history of expense and delay. A narrower scope reduces uncertainty and exposes governance problems before they become enterprise-wide commitments.

• It doesn’t improve the underlying quality of your information. It makes the state of it legible, which many organizations experience as a problem rather than a benefit, at least in the first month.

The underlying technologies are mature. Graph databases, World Wide Web Consortium standards and automated enrichment are used in production across life sciences, financial services, publishing and other sectors. What remains unsettled is which graph-grounded retrieval patterns will prove most durable as models and tools improve. The architecture is moving quickly. The underlying requirement is more stable: a machine can reason consistently only about the aspects of a business that are represented, governed or retrievable through reliable tools.

The advantage that compounds

Peter Senge described the learning organization in 1990 as an enterprise that gets smarter through experience, where insight compounds instead of evaporating. Executives wanted to build that company and mostly struggled, not because the ambition was wrong but because the instruments were limited. We could account for money moving through the enterprise to the penny, while organizational understanding remained informal and difficult to preserve. We built organizations that count brilliantly and learn inconsistently.

The instruments are improving. As leading model capabilities become broadly available at comparable prices, much of the advantage in the model layer will be temporary and rented. The durable difference comes from organizational context: systems that understand the entities, relationships, definitions and evidence specific to the business. Organizations that represent and govern that context can reuse it across every model and application that follows. Organizations that don’t will keep buying more powerful engines and pointing them into the same fog.

None of this is glamorous. It is the work of deciding what the organization’s words mean, making relationships explicit and recording the evidence behind important claims. That is less fashionable than selecting a model, and more consequential to whether the resulting system can be trusted. A reasonable place to begin is smaller than most people expect: take one question your executives ask repeatedly that currently takes three people a week to answer, and model only the things that question touches.

The work of knowing what your numbers mean didn’t stop being necessary. It stopped being anybody’s job. Writing down the part that can be made explicit is how you get it back, and this time it doesn’t leave when the person does.

Sources and further reading

Related work by the author

Every Number Has a Story to Tell. First in the Every Number Has a Story series. bradheidemann.com.

Counting Isn’t Reading. Second in the Every Number Has a Story series, on why analytics capture motion and not meaning. bradheidemann.com.

Most AI Strategies Are Just Efficiency Theater. On automating the old model rather than changing it. bradheidemann.com.

AI Didn’t Create the Marketing Crisis. bradheidemann.com.

Semantic Segmentation to Improve Personalization. Earlier treatment of the customer, content and context argument. tahzoo.com.

Referenced in this paper

Peter Drucker, Landmarks of Tomorrow. Harper, 1959. Origin of the term knowledge worker.

Michael Polanyi, The Tacit Dimension. Routledge, 1966. Source of the observation that we can know more than we can tell.

Peter Senge, The Fifth Discipline: The Art and Practice of the Learning Organization. Doubleday, 1990.

Ikujiro Nonaka and Hirotaka Takeuchi, The Knowledge-Creating Company. Oxford University Press, 1995.

Andreas Blumauer, Semantic AI: Bringing Machine Learning, NLP and Knowledge Graphs Together. KMWorld, 2018.

Andreas Blumauer and Helmut Nagy, The Knowledge Graph Cookbook: Recipes That Work. Edition mono, Vienna, 2020.

Amit Singhal, Introducing the Knowledge Graph: things, not strings. Google, 2012.

W3C standards referenced: RDF, SKOS, OWL and SPARQL. Specifications are public at w3.org.

Thirty-six years ago, Peter Senge described one of management’s most admired organizational ideals: the learning organization. An enterprise that sees whole systems instead of isolated events. That senses cause and effect across months, not moments. That gets smarter with every customer interaction. Generations of executives read The Fifth Discipline, nodded, and went back to their dashboards. The tools existed in fragments. The integrated learning loop did not.

The integrated tooling exists now.

Picture it in practice. A company that hears a complaint while it’s still a whisper, before it becomes churn. That remembers why each customer arrived, not just what they clicked last week. That can trace this quarter’s revenue to last year’s decision and feed the lesson into the next one. A company where every interaction teaches the system, and the system makes every next interaction better. That’s not a metaphor for good management. It’s an architecture, and for the first time, you can build it.

The foundation is necessary. It is not sufficient.

Databricks just published its executive playbook for the agentic era, The New Architecture of Agentic AI, and it’s worth your time. More than twenty executives, one consistent message: outcome first, data layer before everything, governance as the engine rather than the brake. I’ve spent thirty years building digital experience platforms for global enterprises, and I agree with nearly all of it. The industry is converging on the foundation, and that’s genuinely good news. A meaningful enterprise consensus has formed.

But a foundation is a floor, not a finish line. The ebook’s own diagnosis points at what comes next: the models are smart enough; what they lack is context. Correct. So where does context come from? Not from a setting you configure or a layer you license. Context is meaning, accumulated over time. And meaning begins with a claim your dashboards were never built to handle.

Every number has a story to tell

Bounce rate tells you someone left, not what went wrong. Conversion tells you someone bought, not whether they’ll come back. Two decades of analytics have taught the enterprise to count everything and explain almost nothing. We’ve been taking temperatures and calling it diagnosis.

Senge saw why. Humans are poor at reading causality in complex systems. When cause and effect are separated by months, we judge decisions by the events nearby instead of the chain that produced them. A company trims brand investment to make a quarter. Sales soften two quarters later, so it trims again. Each cut is rational. Together they’re a flywheel spinning backward, and nobody in the room can see it, because the delay defeats intuition.

A number is the last sentence of a story that started somewhere else, some time ago. Today’s dip in Net Promoter Score may trace to a clumsy onboarding flow from last spring, or to advertising that promised more than the product delivered. The number is the symptom. The story is the structure. Read only the numbers and you’ll manage the symptoms forever.

Rows hold the nouns. Graphs hold the plot.

Stories don’t fit in rows and columns. A story is entities in relationship over time: this customer, that promise. This content, that moment. This decision, that outcome, six months apart. Relational data holds the nouns. The graph holds the plot.

So extend that consensus one layer up. The semantic layer in today’s playbooks is a dictionary: governed definitions of “active customer” and certified margin numbers. Necessary, and most enterprises still haven’t built it. But a dictionary defines words. It doesn’t tell stories. To follow the chain from an ad impression through an onboarding stumble to a renewal decision, the enterprise needs a persistent relational model of meaning over time. For complex, longitudinal customer journeys, a knowledge graph is the natural architecture: customers, content, products, policies, and moments, connected by relationships a machine can traverse.

Semantic enrichment is the discipline that builds and maintains it. It binds meaning to data across the customer, the content, and the context of the interaction. It works across structured and unstructured sources, and it defines relationships dynamically instead of rebuilding the database every time the business changes. Once the graph is in place, an agent stops retrieving isolated records and starts traversing relationships across time. A graph does not prove causality, but it preserves the structure needed to form and test causal hypotheses. That is the difference between producing an answer and constructing an explanation.

A garden, not a monument

A knowledge graph is a garden, not a monument. Monuments get finished, dedicated, and photographed. Gardens get tended, because everything in them is alive. Products change. Policies get revised. Language drifts. Customers evolve. The graph is a living model of the enterprise, and living models need curators.

The emerging architecture consensus is right that governance is the engine, not the brake. Curation is governance applied to meaning. Someone has to own the ontology the way finance owns the ledger: adding what’s new, retiring what’s dead, reconciling what conflicts. Stop tending the garden and context decays silently. The agents keep answering, confident and polite, from a model of a company that no longer exists. Semantic initiatives rarely fail loudly. They fail by standing still, declared one and done, while the enterprise moves on without them.

Context is what customers feel

All of this cashes out at the one place strategy becomes experience: the customer.

Basic personalization says, “I know your name.” Contextualization says, “I understand what you need next.” The first is a lookup. The second requires persistent relational memory: who this person is, what they’ve been through with you, and what moment they’re in right now. And the experience has to fit the context, not just the message. Channel, tone, interface, timing. A correct answer in the wrong register, at the wrong moment, on the wrong surface, is still a failed experience.

Agents raise the stakes because agents remove the pause. A dashboard built on a wrong assumption sits there until an analyst questions it. An agent with governed data and no customer context doesn’t fail loudly. It fails politely, at machine speed, with perfect lineage: factually correct, well-governed, and contextually irrelevant.

Built to listen

Put the pieces together and Senge’s aspiration becomes an operating loop. The foundation holds the facts. The graph holds the story. Curation keeps the story true. Agents act on it in the moment. Instrumentation records what happened. Evaluation tests whether the intervention worked. The result feeds back into the graph, sharpening the next interaction. Without that loop, the system accumulates history but does not necessarily learn. With it, the enterprise stops reacting to symptoms and starts seeing structures. It catches the reinforcing loop while it’s still forming. It remembers each customer relationship across years, not sessions. It learns.

And learning compounds where technology doesn’t. Model advantages decay quickly. Platforms converge. Accumulated understanding of your customers is the one asset a competitor can’t license, because nobody can copy what your customers taught you.

One of the sharpest lines in the Databricks ebook comes from Stephen Ecker, chief data officer at Trinity Industries: “The data layer is the strategy.” At the infrastructure layer, he is right. At the experience layer, the story is the strategy. The foundation determines what the enterprise can know. The learning loop determines whether it gets smarter.

Every number has a story to tell. The companies that pull ahead will be the ones built to listen.

Disclosure: my company builds knowledge-graph infrastructure for enterprise AI, so I have a commercial interest in this argument. I'd make it anyway.

Leave a comment

Share The Desk of Brad

There are lots of books about holding people accountable, often times these books focus on the clarity of the responsibilities, measurement or structuring conversations that cut through the excuses. Yes, these are all important things to focus on. What I don’t like about many of these books is that accountability is more often a byproduct of great teamwork.

No one who is committed wants to let their teammates down.

So, let’s assume that you work with Smart and Happy people who are committed… but for some reason they are struggling in their job. What should you do to help them? Here are a few things I’ve learned in my career that have helped me bridge the gaps in my teams:

I always try to put myself in the other person’s shoes. What is going on for
them, what is their workload, what is the work that needs to get done and how does it fit with their skill set?

Is the issue a repeating problem or a pattern or is it a one off?
If it’s a repeating pattern then is the issue systemic, a problem with the
way the company is organized, or is it a deficiency in that person’s
abilities? If it’s a one off, is there something going on in the person’s life that we all need to take into consideration? Are they sick, going through a
personally hard time, etc.?

Are they open to having a discussion with me about the concern or do I need to involve someone else (maybe someone in leadership) to address the concern.

I seek first to understand before I make any judgements. It’s easy to blame people and it’s easy to assign reasons for why they are not meeting expectations, but that is laziness and shows a lack of respect.

My mom always used to say that if you respect someone, you’re actively examining your beliefs about that person and re-looking at your underlying assumptions. Once I’ve taken the time to understand, then I take a hard look in the mirror and ask myself am I contributing to or causing the problem?

Then teamwork should kick in… What can I do to help? Does this person need encouragement? Do they need additional resources? Do they have a personal issue that needs attention? Or in a few cases does this person need a corrective talk? If it’s something that is just really difficult for someone (we all have stuff we suck at (responding to email for me) then how can we change the system to help that person be more successful?

Anytime someone on your team is not performing or meeting expectations… it’s almost never an individual problem; it’s a way of working, a way of resourcing or a way of supporting someone’s problem. Don’t be quick to anger, quick to blame or worse yet- stand around and watch someone fail. We win together and we lose together. Be the kind of co-worker that makes everyone around you better. It’s why we have Kudos in the desk of Brad, our better selves should be expressed in the quality of our teamwork.

Let’s go be great!

Brad

Hi Everyone,

Where everyone knows your name… and they’re always glad you came.

Do you make your customers feel this way? Do you know them and are you glad to be working for them? Our business is based on relationships and adding value.

I was thinking recently about a bar I go to where two of the staff, Jason and John, make the place a great experience for me. Not because the drinks are special. Because they remember me. They ask about my week. They make me feel like coming in was a good decision.

That got me thinking about how each of you makes great experiences for our clients. Over the next week, send me what you do to make working with you a great experience. I’ll feature the ideas in next week’s Desk of Brad.

If you find yourself short on answers, then it’s time for a personal inventory. Why are you here? Our goal is to make millions of people a little happier every day. Your clients should be part of that equation. If you’re not proactively trying to figure out how to make a difference and leveraging your unique value to make it happen, then it’s time to get started.

We have a lot to be thankful for. A great team. Great clients. A chance to make a difference in our work. We are agents of change, and change is hard. The comfort of a strong relationship and excellent work is what allows our clients to change in the first place. We are happiest when we do hard work and begin to see the results. Think about getting in shape, going on a diet, or learning a new language. The work is hard. The results are worth it.

Like a good trainer, friend, or teacher, let’s help our clients achieve the outcomes they want.

Our clients deserve to feel like they just walked into Cheers when they interact with you. So do your colleagues. We hire smart and happy people. Let your bubble of joy surround your clients and your team.

It’s not too late to get started. It’s not too late to make a change. It’s not too late to put others above yourself. And if none of that works, then fake it until you make it. Put that smile on and let it shine.

Let’s go be great,

Brad

Hi All,

There are challenges in our business. There always have been and always will be. The strength of a team is the ability to overcome those challenges.

There are many new projects and initiatives in front of us right now. I’d say in many ways we have more opportunities than ever. The work being done is some of the best ever produced by this company. You should be proud of it.

As we drive to consummate these new projects and roll out a new account and delivery model, we are going to have some bumps along the way. We work for big clients with complex organizations, and getting to yes while more effectively organizing ourselves is hard work. We’ve changed a lot of things recently, and I see the momentum.

In order to realize the opportunity in front of us, I need each of you to push hard and not leave any options or conversations unaddressed. It doesn’t matter if the challenge is inside the company and how we are executing, or inside the client. You have to speak up and help create the right outcome. Silence is often perceived as acceptance. We need to hear your voice.

There is a favorite book of mine, The Bumps Are What You Climb On. The moral is that if life were smooth, there’d be nothing to hold on to as you climbed the mountain. Let’s pull each other up and get to the top.

Time to pull together and be heroes.

Let’s go be great,

Brad

Hi Everyone,

I’ve been talking with my daughter about a charity she wants to start. She is going to call it Boxes of Hope, essentially boxes full of items that would benefit children in need. Toys, treats, and books. She is eleven years old, and I was a bit surprised and delighted by her ambition. You know I am a proud father, like everyone. She asked me for help getting an organization started and for some lessons learned.

So I said to her, you have to start with the why before you worry about the what.

I explained that the first act of our company, before we had a name and a clear business plan, was to write down our values. It was important to me to define what the values of the company would be as the basis for why we existed, rather than the other way around. As a reminder, our values are:

1. If you care about your employees and you care about your clients, you’ll have a company worth caring about.

2. We hire for character before we hire for capability.

3. We believe in the marketplace of ideas.

4. We hire interesting people who are interested in change.

5. We look for smart and happy people.

They are the guideposts. The ambition of the company. The decision to focus on customer experience management, and subsequently to recognize that we could make millions of people a little happier every day, was the natural evolution of the business.

Start with the why before you worry about the what applies to much more than the formation of a company or a charity. Think about the work you do for our clients. Ask yourself why. Why are we doing this work?

We have a bunch of new projects kicking off. Has each team sat down and come to a conclusion about the why for each one? I am quite certain that project plans, schedules, and resourcing are all in full swing. Those are the what and the how. Not the why.

Take some time as a team and discuss the why. Write it down. Make sure everyone on your team understands it. It is the north star for all of our client engagements.

Let’s go be great,

Brad

Hi Everyone,

In thinking about what to write this week, I went back to a three-part series I wrote in 2012 called Our Inspiration, Our Values, Our Mission. It’s always good to revisit such things from time to time. You can learn a lot about the true purpose of a company by examining how closely it still adheres to its core principles years down the road. It’s a check on one’s commitment to what one believes, or believed, as the case may be. Ideally, the ideas that bound the company together then still apply today. In Tahzoo’s case, what I wrote then is perhaps even stronger now, if that’s possible. A good sign.

This week we return to part one.

Our Inspiration

Tahzoo is not just a job for me. It’s a labor of love. I have been fortunate to work for some great companies, and I’ve seen the impact a great company can have on the world. My inspiration comes from seeing what a group of committed and determined people can accomplish. That’s why we started Tahzoo.

We are in a time of unparalleled change and opportunity. Technology is recreating the way companies and their customers engage. We haven’t seen a time like this since the advent of TV, well before any of us were born. During such periods of change, only a handful of companies rise to lead the way. Tahzoo is one of those companies.

I think we all want to work for a company that is both inspirational to others and that inspires change in the way business is done.

A business can be inspirational in many ways. It might be in how we work together to tackle difficult problems. We are a group of smart, happy people who proactively provide cutting-edge solutions. In return, we get to do meaningful, rewarding work.

Tahzoo is inspirational in the sense that we inspire our clients to take actions they might not otherwise have taken so they can change the world. Often, that means our clients are betting their careers on our ability to deliver. That takes a lot of inspiration on our part too.

In times of change, people need leadership. We are a company that provides such leadership. This is about caring enough about your clients to take the time to proactively solve their problems.

Inspiration is a way of systematically exceeding our clients’ expectations, each other’s expectations, and even our own. For each of us, this is a time to ask: what could we be doing for one another and our clients that makes a real difference in their business and in the world?

I recognize that this may seem lofty, perhaps beyond what you’d expect from most jobs. You’re right. If you work for Tahzoo, you seek to be part of something bigger than yourself. Part of a community. Working every day with a group of people who expect more of themselves and more of each other.

I hope you came to Tahzoo for precisely those opportunities. To work for a great company. To inspire your colleagues and your clients with your thoughtfulness, innovation, and determination.

Let’s go be great,

Hi Everyone,

Have you ever noticed that a smile from someone can just make your day?

In our business, our clients are looking to us for the ideas and the energy to change. To persevere through difficult transitions. To be open-minded about new approaches and new opportunities. No matter how compelling your argument, no matter how sound your reasons, absolutely nothing starts a conversation off better than a smile.

So even when you’re busy, even when you’re concentrating, and especially when you’re with a client, remember to smile.

Have a smile on your face. A smile in your voice. Use your smile to brighten someone’s day.

Let’s go be great,

Brad

Hi Everyone,

Success is a byproduct of consistency.

Consistency is the secret ingredient that turns good businesses into great ones. It means repeatedly delivering on our promises and maintaining a high standard of quality in every aspect of our work.

Consistency breeds trust. Our clients know what to expect, and we deliver as expected. It is why they choose Tahzoo. Trust is the currency of business relationships. It is hard to earn and easily lost. If we want new projects, or to be referred to new clients, we need to do a great job for the customers we already have. If you take care of your customers, they will take care of you. The formula is straightforward and easy to understand. It still takes hard work and discipline to execute.

I am going to be closely auditing our client experience for the foreseeable future. Checking to see if our deliverables went out the night before. Asking for meeting minutes to be shared after the meeting. When I was a manager at Nordstrom, I used to say: you get what you inspect, not what you expect.

We need to raise the bar and operate as a highly professional boutique consulting firm. I am counting on each of you to do your part.

Let’s go be great,

Brad

Hi Everyone,

Great teams have a common understanding of the central tenets that give the company meaning and purpose. In Simon Sinek’s book Start With Why, he makes a compelling argument that people are more interested in why you do what you do than in what it is you do. It’s a great read.

I have been thinking about the year ahead and there are a few questions that I want everyone to know the answers to. Don’t be surprised if you get called on by me or anyone in leadership to answer these, and please make sure that folks who are new to the company know them too.

Why did I start Tahzoo? To create a company where people could continuously learn and grow to become the best versions of themselves.

What is our mission? To make millions of people a little bit happier every day.

What are our core values?

1. If you care about your clients and you care about your employees, you’ll have a company worth caring about.

2. We look for interesting people who are interested in change.

3. We believe in the marketplace of ideas.

4. We hire for character before we hire for capability.

5. We believe in smart and happy people.

What is the most important tool for improving employee satisfaction? The Voice of the Culture Survey and Kudos.

What are our top three priorities?

1. Great customer service

2. Perfect quality work

3. Driving profitability

How do you know if a client is happy? They tell you, and they buy more services.

How did we come up with the name Tahzoo? We had five criteria for the perfect name:

1. It had to be a six-letter domain name

2. It needed to be two syllables

3. The name should not mean anything

4. It should sound vaguely familiar and slightly whimsical

5. There needed to be search engine equity in the first three letters

With GoDaddy open and having tried countless names, I noticed that behind the barista there were Tazo Tea boxes while sitting at a Starbucks. That provided the inspiration, and Tahzoo became the name of our company.

These are our founding stories. Make sure everyone in the company knows the answers.

Let’s go be great!

Hi Everyone,

I spent twelve years at Nordstrom and held a lot of jobs. I started on the sales floor. I wrapped gifts during the holidays. I worked the switchboard, the old plug board kind, routing every call coming in and going out of the store.

The switchboard had nothing to do with selling clothes. I wasn’t ringing a register. I wasn’t styling anyone or building a display. I was answering a phone.

But I was the first voice most customers heard. Before they walked through the door. Before they met a salesperson. Me. The way I picked up that phone decided whether they felt welcome or in the way. Whether they bothered to come in. Whether they told a friend about us, or warned them off.

That is selling. It just doesn’t look like selling.

The receptionist sells. The developer sells. The person who answers the support ticket sells. The person who writes the invoice sells. Every interaction either earns the next one or burns it.

If your people don’t know that, that is not their failure. That is yours. It means you haven’t drawn the line for them between what they do on a Tuesday morning and the customer who decides, six months later, whether you are worth coming back to.

Draw the line. Make it visible. Make it specific to their job. Then watch what happens when people who thought they were behind the scenes realize they have been on the front line the whole time.

Let’s go be great.

Brad

“I’ve lived through some terrible things in my life, some of which actually happened.”

– Mark Twain

Flow State

About three or four months ago I changed the locked screen on my phone to a quote from Mark Twain. “I’ve lived through some terrible things in my life, some of which actually happened.” Read that quote again… it took me a second read to get it.

I put this on my lock screen because I spend every day running through scenarios in my head about how to drive the business, manage conversations, make decisions, etc. It’s a very useful habit for the job I have but it was also adding to my stress level. When I read this quote it gave me the freedom to be more dispassionate and analytical about things. It was a way to remind myself not to allow my brain to run wild, generating anxiety around worst-case scenario outcomes.

I originally wrote this during the early weeks of the COVID-19 pandemic, but the techniques are universal. It got me thinking about anxiety and how I manage that in my life. First of all, let’s just be super clear, we are living in a time of heightened anxiety and stress. Each of our lives have been disrupted and the timeline for the end of all of this is at best, ambiguous. So, it’s normal and okay to be anxious these days and you won’t always recognize it when it’s happening to you. You could find yourself short-tempered, not sleeping well or just having trouble motivating yourself… anxiety manifests in many different ways.

Personal Inventory

I want each of you to take a quick anxiety self-inventory – a little homework for each of you, so grab a pen and paper or open notepad and answer the following questions:

1. How do you feel when you recognize you’re anxious? Describe the physical feeling… do you feel tension in your shoulders or sick to your stomach? Whatever it feels like for you, write it down.

2. What was going on in your brain right before you became anxious? What were you doing or thinking about? For example, as I mentioned today, my Mom went into the hospital last night and I found myself working and the random thought of “what am I going to do if, God forbid she dies,” popped into my head.

3. What triggers an anxious thought or loss of focus for you? For me it’s two things, random thoughts and when I consciously play out worst-case scenarios. As with all of these questions, everyone is different, so do your best to write down your experience. There is no right or wrong here.

4. When you’re triggered how does your communication or thought process change? Do you talk differently, does your choice of language change? For example, when I am anxious, I talk a lot faster than normal.

5. Write out a list of 5 things you do that cause you to lose track of time. For me they’re reading, writing, tennis, chess, playing with my kids, going on long drives, watching movies and a few other things. Write out your list.

I want you to do this exercise so that you can be more aware of when you’re anxious, and what is going on for you when that happens. I don’t have a magic pill for you but as you can imagine I have a stressful job, so I’ll share some of my techniques to manage stress.

My Remedies

I watch comedies – laughter is great medicine. My brother Matt has a few impressions of President Camacho from Idiocracy that have left me rolling on the floor a couple of times.

I’ve turned off all of the notifications on my phone, except the ringer. I make a conscious choice to use my phone, but I don’t let all of those notifications trigger my anxiety or remove me from the moment.

I walk around a lot, I don’t mean I take a lot of long walks, but I mean I get up and move around.

Several times a day I stop what I’m doing to focus on breathing and relaxing. I also try to enjoy a view. I look out a window or at a piece of art or at something that’s aesthetically pleasing to me.

I exercise. You just can’t beat the restorative power of exercise. Get up and do something, go for a fast walk.

I write out a to-do list every day. Doing this gives me a feeling of control and I make conscious choices around how I am going to spend my day.

Lastly, try to do something every day that makes me lose track of time. Go look at your list and do more of it! It will help inoculate you from moments of stress. They call it being in flow, go look up Mihály Csíkszentmihályi’s work on Flow. There are many benefits to being “in flow.”

These are a few of my coping techniques. Each of us has our own patterns, take the time to be conscious and present about feeling anxious so that you can manage it. I realize that for some people anxiety is a medical condition and my techniques may not get the job done, that’s between you and your doctor.

We are all agents of change. We can spread positive energy and reduce anxiety in ourselves and for each other. We can make the world a little bit better every day. We are all capable of that.

Let’s go be great.

— George Washington

Every business has a way of doing things. Most of the time, that way is accidental, the accumulated habits of people who were never given a better option. IQO is the deliberate alternative: a system for building a company that knows what it does, knows whether it’s working, and can repeat it at scale.

It has three parts. Together, they form a covenant, a promise to clients, to employees, and to the business itself, that what we deliver will be intentional, measurable, and consistent.

Innovation: The Search for the Best Way

Innovation is often confused with creativity. Harvard Professor Theodore Levitt drew the distinction: “Creativity thinks up new things. Innovation does new things.” Innovation isn’t ideation. It’s action.

Innovation is the heart of every exceptional business.

And the most important place to aim it is not at your product, but at the way your business operates. How you interact with clients matters more than what you deliver. By recognizing that it is not just the work itself that demands innovation, but also how that work is presented, scoped, and fulfilled, we aim our innovative energies at the way business does business.

For innovation to be meaningful, it has to take the client’s point of view. Every engagement begins with a promise, a covenant between you and the client about what’s possible and how you’ll get there together. Innovation is the discipline of constantly asking whether you’re keeping that promise in the best possible way, or whether friction, complexity, and habit have gotten in the way. If a process doesn’t make things easier for the client, it’s not innovation. It’s complication.

Innovation is the mechanism through which a business identifies itself in the mind of its customers and establishes its individuality. It is the result of a scientifically generated and quantifiably verified profile of your clients’ perceived needs and unconscious expectations.

The discipline is asking, every day, “What is the best way to do this?” Knowing you’ll probably never find the definitive answer, but that by asking, you’ll always find a better one than you have now.

Innovation is the “Best Way” skill. It produces energy in every company where it’s nurtured, fed, and stimulated. That energy feeds everyone the company touches: employees, customers, suppliers, and partners. In an innovative company, everyone grows.

Innovation is the signature of a bold, imaginative brand.

Quantification: The Truth Is in the Numbers

Lord Kelvin put it plainly in 1883: “When you can measure what you are speaking about, and express it in numbers, you know something about it. When you cannot, your knowledge is of a meagre and unsatisfactory kind.”

What was true for physics is true for business. If you can’t measure it, you can’t predict it. If you can’t predict it, you can’t improve it. Quantification is how we hold ourselves and our business accountable.

Quantification is how a business learns to see itself clearly.

Most companies default to qualitative analysis. They trust their instincts. They tell stories about what’s working. But stories aren’t data, and instinct doesn’t scale. Few companies thoroughly quantify their operations, even the ones that believe in measurement.

Making the commitment to quantify everything about how you do business will profoundly change both your results and your culture. It means tracking the things that matter:

• What was the measurable business impact of our work?

• How many positive client interactions do we have each day?

• Are our capabilities aligned with our pipeline?

Eventually, you think of your entire business in terms of numbers. You can read the company’s health the way a doctor reads vitals. Not guessing but knowing. Because without numbers you can’t possibly know where you are, let alone where you’re going. And when you do know, you can act with confidence instead of hope.

Quantification turns intuition into intelligence.

Orchestration: Consistency in Execution

W. Edwards Deming, the father of modern quality management, said it simply: “A bad system will beat a good person every time.”

You’ve innovated a process. You’ve quantified its impact. Now orchestration ensures your clients get a consistent result, every time. Orchestration is the elimination of discretion at the operating level: replacing individual judgment with a reliable system so that the promise you made to the client is the promise they actually experience.

Orchestration is the discipline that turns a good idea into a reliable business.

Without it, nothing can be planned and nothing anticipated, by you or your client. If everyone in the company is doing things by their own preference rather than following a shared system, they aren’t creating order. They’re creating chaos. Discretion, however well-intentioned, is the enemy of progress, standardization, and quality.

A process that hasn’t been orchestrated can’t be consistent. A process that isn’t consistent can’t be depended on. And a process that can’t be depended on isn’t a business model. It’s a collection of talented people making it up as they go.

A business model is simply your unique way of doing business. IQO is the system that makes that way repeatable. It provides the vehicle for predictability, for ensuring that the covenant you made at the start of every engagement is honored at every step. Because unless clients experience what they were promised, every single time, they will go someplace else to get it.

Orchestration is the glue that holds a company to its customers’ perceptions. It is the certainty that is absent from every other human experience, the order and logic behind the human craving for reason. It is as simple as doing what you say you’re going to do, saying what you mean, and meaning what you say.

Orchestration is what makes a promise keepable.

The Cycle Never Stops

Once you’ve innovated, quantified, and orchestrated something, you’re not done. The world will collide with whatever you’ve built and sooner or later destroy it. IQO is not a one-time project. It’s a permanent operating discipline. You innovate, measure, systematize, and then you do it again.

When you think of Orchestration absent Innovation and Quantification, you’re describing an action stripped of its purpose, its meaning, its vitality. The three only work together.

Innovation, Quantification, and Orchestration are the backbone of every extraordinary business. I want ours to be extraordinary.

The People Who Keep the Covenant

IQO is a system. But systems don’t build themselves, and they don’t run on autopilot. They run on people who understand the covenant, the promise made to every client at the start of every engagement, and who take personal ownership of keeping it.

That’s what transforms work from a habit into a craft. When you commit to IQO, you stop thinking about tasks and start thinking about the promise behind them. Am I innovating the best way to deliver on this commitment? Am I measuring whether it’s working? Am I making it repeatable so the next client gets the same result?

The people who ask those questions, who hold themselves to that standard, are the ones who build extraordinary businesses.

Our mission is to make millions of people a little happier every day. That’s not a slogan. It’s what happens when smart people commit to keeping the covenant, every day, in every interaction.

Let’s go be great.

“Growth is painful. Change is painful. But nothing is as painful as staying stuck somewhere you don’t belong.”

— Mandy Hale

When I started my company in 2010, my central thesis was that personalized experiences would replace the “one size fits all” digital experience. From a marketing perspective, it wasn’t that companies couldn’t figure out who you were or the experience that would be most pleasing to you. The gating factor was that they couldn’t get the right unit of content in front of you quickly enough to make a difference. Turns out that the trend I spotted was and is still true today. While platforms have evolved to tackle some of these issues, there are still technology, strategy, and process gaps that we need to overcome.

I’ve been clearing my calendar to think about the next decade of work. I thought I’d share the questions I’ve been exploring.

The Mathematics of Experience

Mathematics continues to evolve at an accelerating pace. Our ability to write equations that describe our world and our experience is developing rapidly. How should we be applying advancements in mathematics to business?

If I wanted to write an equation that would describe the relative likelihood that I’d visit a Starbucks at 4:30 tomorrow afternoon, what would that look like? What mathematical domains would be necessary to build a reliable model?

What is the relationship between brand affinity and proximity? You might walk two blocks at lunch for your favorite sandwich, but six blocks would be too far. Is there a level of discounting or incentivization that would impact your behavior to walk those additional four blocks? Ronny Chieng does a routine on Amazon Prime becoming “Prime Now.” Two-hour delivery is no longer sufficient; it needs to be Prime NOW. It’s funny, but there is truth in our growing expectation that companies anticipate our needs. How do we help our clients write equations that anticipate what someone will want now?

Machine Learning and the Customer Experience

Given the advancements in mathematics, how are we going to use machine learning and artificial intelligence to improve the customer experience? The wonderful thing about digital experiences is that they’ve given us sample sizes that are statistically valid. There are millions and millions of digital interactions to measure. When we move from statistically based personalization models to algorithmically based ones, how will that impact personalization? What level of uplift will we get from a marketing and customer service perspective?

What new technologies do we need to master to lead the market in these categories? How will our engineering rigor need to change when the building of the enterprise marketing platform is only the first step in the experiment?

A Grand Unified Theory of Content

We are still in need of a grand unified theory of content. We have good tools for searchability (thank you, Google) and findability, which is a byproduct of standardizing UX, but that’s not sufficient. In spite of all the improvements, consumers are still left with the task of finding and navigating content. We still aren’t able to describe content in terms that allow computers and AI to understand it well enough to build effective algorithms for personalization.

I’ve experimented with using the discipline of semiotics to codify content. Certainly DITA, XML, S1000D, and mapping ontologies are all helpful, but we need to bring all of this together into a unified framework for describing content. I don’t have this all figured out, but I sense there is a path that will combine all of these standards into a game-changing solution.

Complexity Theory and Business

This leads to the next set of questions, which involve complexity theory. A complex system is composed of many diverse parts that are highly interconnected and capable of adaptation. If you think about how a brand interacts with its customers on a global scale, you have a complex system. When you think about a company with many different disciplines all working toward a common goal, you have a complex system.

How do we help clients understand the complexity of their customer engagement? How would we visualize that? How would we understand the interactions well enough to make recommendations that positively impact business outcomes?

I recently connected with Kirell Benzi, who creates art by visualizing complex systems and data sets. For those interested in complexity theory, the Santa Fe Institute is a remarkable resource. One of the most important questions for the next decade: how do we build teams comprised of diverse skill sets that effectively collaborate to serve clients?

AI Will Change Everything

AI will be a couple of orders of magnitude more impactful than the advent of the Internet. Imagine how much the world has changed because of the Internet, then multiply the amount of change in the last 25 years by 100. That is what AI is going to bring to our world. We won’t experience a linear progression of change. There will be quantum leaps in technology and understanding. The advancements in science, knowledge, and technology will be astounding beyond belief.

This will create cultural and economic disruption on a scale not seen before in human history. To put this in perspective, it took the Catholic Church about 200 years to come to terms with Galileo’s notion that the earth revolves around the sun. We won’t have that luxury of time.

The Crisis of Belief

Beliefs are our brain’s way of making sense of and navigating our complex world. They are mental representations of the ways our brain expects things in our environment to behave, and how things should be related to each other. Beliefs are templates for efficient learning and are often essential for survival.

What happens when long-standing belief systems are eliminated or proven wrong, virtually overnight? We won’t have 200 years to come to terms with the change in knowledge or perspective. How would we need to teach differently if accepted truths and norms are regularly in jeopardy? What long-standing principles are likely to be challenged in the next decade?

If capitalism is based on a risk-reward relationship, what happens when the risk is virtually eliminated by computing power? That’s not to say that randomness goes away, just that the calculation of risk will be almost perfect. How do you invest your money in a world where the rate of return is already a certainty? What does that mean for capitalism, and will we need to invent a new economic model? All the people who owned big castles in Europe during the Middle Ages thought they would be the dominant economic model for the foreseeable future. Now they are places to visit on vacations.

Or look at healthcare. What happens when life expectancy skyrockets because we apply virtually unlimited computing power to monitoring your health? What would it mean for our economy if average life expectancy doubles in the next 20 years? What does that mean for managing resources and population? If you think that’s not a possibility, start reading and talking to scientists who are at the forefront of this revolution.

The Governance Question

All of us can see how social media is affecting our democracy. Do the basic concepts of freedom of speech apply to machines and computers too? It won’t be long before computers generate more content than human beings. What laws and rules do we need to govern that scenario? How should we think about government, the rule of law, and self?

There is a great quote by Carl Sagan, written in 1995:

“Science is more than a body of knowledge; it is a way of thinking. I have a foreboding of an America in my children’s or grandchildren’s time, when the United States is a service and information economy; when nearly all the key manufacturing industries have slipped away to other countries; when awesome technological powers are in the hands of a very few, and no one representing the public interest can even grasp the issues; when the people have lost the ability to set their own agendas or knowledgeably question those in authority; when, clutching our crystals and nervously consulting our horoscopes, our critical faculties in decline, unable to distinguish between what feels good and what’s true, we slide, almost without noticing, back into superstition and darkness.”

Eerily familiar given the world today. That can’t be the end state, so what happens next?

What Comes Next

We are seeing the beginning of cultural change at an unprecedented scale. I say all the time that we exist to make millions of people a little bit happier every day. We’ve got the ambition and a company of smart and happy people that are and will continue to make a difference.

I am working on these questions and I invite your input and perspective. The work we do is important, and we need to make sure that our thesis for the next decade is a guiding light.

I am certainly not done thinking about what comes next. I am focused on considering and contemplating what the future holds so we can anchor ourselves to the prospect of creating a better world. While at times I am daunted by what comes next, I am also an optimist and I have an unshakeable vision for a greater and more fulfilling world. It’s up to each of us to make a difference.

Let’s go be great.

— Isaac Newton

In a real-world setting, a good salesperson sizes up a new prospect walking in the door and within seconds begins to tailor the conversation to that person’s needs. They read body language, ask the right questions, and adapt. It is instinctive, human, and remarkably effective.

In the digital world, the union of data and content management technologies now allows companies to replicate that same instinct at scale, delivering different content to each customer based on what we know about them in real time. We call this personalization, and it represents a fundamental shift from one-way broadcasting to genuine two-way conversation between brands and their customers.

The impact will be particularly significant in the relationship-based business-to-business space. The technologies for delivering nuanced, personalized content are well established and proven to drive business results through contextual relevancy, higher conversion rates, and long-term customer loyalty. The infrastructure exists. The data exists. The question is why so few companies are doing it well.

The Fired Salesperson

Here is a simple test: if a sales representative sold the way most B2B websites do, they would be fired. Not coached, not retrained. Fired.

For far too long, marketers have gotten away with being terrible salespeople online because they are insulated by the law of large numbers. If they churn through enough people, eventually some small portion will convert to customers. Volume is the name of the game. But volume is also tremendously expensive and inefficient. Worse, it ignores a fundamental truth about customer experience: loyalty is not built in a single transaction. It is the cumulative result of every interaction a customer has with a company, positive and negative alike. Every generic page, every irrelevant recommendation, every moment a website fails to recognize what a customer actually needs is a small withdrawal from that account. Churn enough people through a bad experience and you have not just lost a sale. You have trained your market to expect nothing from you.

The growing gap between truly responsive experiences, those that adapt content to the user based on what we know about the user, and the standard one-size-fits-all content model reveals a fundamental weakness in how most firms sell their products in the digital realm. The technology is not the problem. The thinking is the problem.

Standing on the Shoulders of Giants

Our ability to know the customer through data is better and more powerful than ever. The technology is sophisticated enough to make true personalization a reality. But there is more to the arithmetic of marketing than data and technology alone.

Much of the fault for the state of online selling falls at the feet of a simple failure: companies do not understand how their customers become educated about products and services. They treat the buying journey as a funnel to be optimized rather than as a learning process to be designed.

This is where marketers keep making the same mistake. They reinvent the wheel. They build frameworks from scratch, ignoring decades of proven research on how humans actually learn, evaluate, and make decisions. There are entire fields of study, education theory, cognitive science, behavioral economics, that have spent generations answering precisely the questions marketers are struggling with today. The work has been done. The experts have spoken. The principles are proven. The question is whether we are humble enough to use them.

For inspiration, I turned to a principle of education theory known as Learning Models, first developed by the academic Robert Gagné in the 1980s. Gagné identified nine instructional events that move a learner from attention to retention to transfer. Every good teacher knows this sequence. Every good salesperson practices it instinctively. Almost no B2B website reflects it at all.

When you marry Gagné’s Learning Models with digital personalization, you get a comprehensive framework for the customer experience that addresses the full arc: how customers think about their own business problems and potential solutions, the step-by-step process for educating a prospect through digital channels, how to gather accurate data about those customers, and the technology to deliver a relevant experience that actually sells.

Why This Matters Now

This is not just an argument about personalization. It is a first-principles argument about how we should approach the entire customer experience.

If a considered purchase requires some level of education so that the consumer has the information necessary to make a purchasing decision, then we should rely on the experts who have spent their careers studying how people learn. We should not reinvent the wheel. We should stand on the shoulders of the people who have already solved these problems in other domains and apply their insights with the precision that modern technology makes possible.

This principle, leverage proven academic and expert frameworks rather than building from scratch, will only become more important as the tools at our disposal grow more powerful. Artificial intelligence, knowledge graphs, semantic content models, and scoring frameworks all depend on the same foundational insight: technology is only as good as the thinking behind it. Get the thinking right, and the technology becomes transformative. Get it wrong, and you have built an expensive machine that is still a terrible salesperson.

To remain relevant to today’s digitally driven B2B customers, the industry must move toward greater personalization. If today’s leaders do not take up the charge, a competitor will find a better way to meet the needs of those customers. The B2B space is ripe for disruption, and the disruptor will not be the company with the most data or the best technology. It will be the company that understood how customers actually learn and built the experience around that understanding.

To effectively communicate relevant and personalized content means opening the floodgate, but first inviting consumers to explore their options through a process of genuine self-education. Mere presentation of information must progress to a real and lasting conversation between customers and companies. When learning models and digital personalization are truly aligned, then customers are both engaged and educated. That is when great things happen.

Let’s go be great.

— Steve Jobs

When Customers Give Up

Someone close to me spent a Saturday morning trying to transfer years of photos and files from three old hard drives to her cloud storage account. I watched as she spent hours navigating menus, re-reading help articles, and muttering expletives at her screen. It took the better part of a morning to do something that should have been simple.

The company that made this product is known for having one of the best user experiences in the industry. They are synonymous with simplicity. I use their products every day and genuinely admire them. Which is exactly why this mattered. Even the best companies have blind spots, and the gap between reputation and reality is where users get hurt.

The other detail worth mentioning: she is an executive at that very company.

When users struggle with a digital product, they rarely blame the product. They blame themselves. A capable person sits in front of a screen and thinks, “Why am I not smart enough to figure this out?” That is not frustration. That is self-doubt. And self-doubt is where users give up. They close the tab, put the laptop away, and quietly decide they are the problem. When your customers are defeated, your brand is defeated too.

Every Interaction Is a Brand Event

This is the strongest argument for investing in user experience strategy and rigorous testing: your product’s experience is your brand. Every confusing workflow, every dead-end menu, every moment a user doubts their own competence is a brand event. And brand events compound. The user who felt defeated on Saturday morning does not write a complaint. She simply stops trying. That is the most expensive kind of churn, because you never see it happen.

Eventually, after three calls to the service center, she got it done. But the experience raised a serious question about process. Somewhere in the development of that feature, the team either did not formalize the user scenarios necessary to determine a clear, communicative, and empathetic path to task completion, or they did and failed to act on them. The discipline of user experience requires homework before the build, testing after the build, and continuous refinement based on real user metrics. If any of those steps is missing, even a good product becomes a source of friction.

Methodology Only Gets You So Far

Today’s consumers have become less and less tolerant of digital experiences that don’t work. If users have a tool that frustrates them, they simply find another tool. Most importantly, as the digital tools we use become increasingly complex, there is an ever-increasing need to link seemingly simple screen interactions with the complex engineering environment necessary to deliver our on-screen promises.

The best UX work I have seen begins and ends with empathy, and with a simple belief: good user experience makes people just a little happier every day. It is not only about removing friction. It is about giving users a sense of accomplishment, a feeling that the tool respected their time and intelligence. That belief drives the methodology: sitting with real users, closely following their engagement with the product, asking questions, conducting interviews, and reading between the lines to find where the subtle misses are. I have watched teams travel the world to sit beside users and observe them in context. Often, the difference between a good experience and a bad one comes down to something small: the placement of a button, the length of a content field, the color of a header, the way a search result is displayed. But it takes real skill to see those things. It takes the discipline to ask the second and third and fourth question and get to the bottom of the real issue.

User stories are the foundation. Every interaction with a digital product can be mapped to a detailed scenario. The UX team’s job is to anticipate everything a user might want to do, then work hand in hand with the engineering team to determine the most efficient path from intent to completion. The two disciplines are inseparable. As digital products grow more complex, the need to bridge simple screen interactions with the complex engineering underneath becomes the central design challenge.

User experience is half science and half art. It is both empirical and intuitive. There are tools to be used, methodologies to be employed, theories to be tested, and data to be gathered. But great UX work is also found in the character of the people doing it: whether they listen, whether they notice nuance, whether they care enough about the right answer to keep asking questions after everyone else has moved on. A lot of UX design theory can be taught. The tools for measuring user interaction improve every year. But the one thing that separates excellence from mediocrity, the thing that keeps a capable person from ever feeling defeated by your product, is something that cannot be taught or measured. Talent.

Let’s go be great.

– Don Shula

I’ve been unusually contemplative this week, turning things over in ways that feel different from my normal pattern of analysis. I am keenly aware that time is marching on even though each day is bleeding into the next. I find myself vacillating between forgetting which day of the week it is and being shocked by the news of the world into higher states of consciousness. One moment I’m drifting through the day on autopilot, comfortably numb in the routine of busyness, and the next I’m jolted awake by some insight that demands my full attention. That oscillation between sleepwalking and sudden clarity is what I want to talk about, because I think most of us live there without naming it.

It seems trite and selfish in the midst of a global crisis to spend time focusing on myself, but this question of my purpose keeps resonating through my thought process. Since I was very young, I’ve made a deliberate effort to monitor my internal dialogue. The narrative in my head has been a source of suffering at times, but also great insight. I share this with you because it’s my hope that each of you uses this gift of extra time to seek answers to some lingering questions in your life. Open the door as the moments of awareness present themselves and just sit with the questions.

As a young teenager, I was very interested in philosophy and religion. Call it divine intervention or good fortune, I met a man named Don Williams who became a mentor and a second father to me. Don has a Ph.D. in world religions, a Master of Divinity, has written a dozen books, and wrote the articles of faith for the Vineyard Church. He became famous as a young pastor in the late sixties for giving a sermon called the “The Gospel According to Bob Dylan,” which drew over 3,500 attendees to the Hollywood Presbyterian Church. If you’re interested in learning more about Don, check out documentary on Amazon called Salt and the Light.

The reason that I bring up Don is that I spent a lot of time with him contemplating the meaning of life and how various religions approached the concepts of enlightenment and salvation. As a teenager, Don played a critical role in shaping my thinking and how to consider larger life questions. We had a very Socratic relationship; he would give me books to read and then we’d talk about them. There is consistently a thematic approach across all these religions and books which is the idea of life as a practice. A practice being a set of meditations/prayers, a demonstration of values, daily activities, and habits. The point being is that you set your life’s course and incorporate the concept of practice into your daily life. More easily said than done for sure, but it has been a guiding life strategy for me. It’s another reason I wrote out the company values for my company before I even began building the business plan.

The people I admire most in my life, my heroes, are people who have struggled to live their life as a practice in service of a higher calling.

Purpose tells you where to aim. Practice is how you walk there every day. Don Williams gave me the first; Don Shula showed me the second.

This same principle applies to our professional lives. Every day at work is a practice. Every project, every client conversation, every decision is an opportunity to pursue excellence. You don’t achieve perfection, but the discipline of defining what great looks like and then pursuing it relentlessly is what separates the good from the exceptional. The organizations and people I admire most don’t stumble into greatness. They practice it, every single day.

This week Don Shula passed away. He was the coach of the Miami Dolphins and led the team to the only undefeated season in NFL history. It’s an unparalleled achievement in a team sport. Certainly, this accomplishment is the headline of his life’s work, but it belies the mythology of success in our culture today. Success is not found. It’s not luck, and it’s not the façade presented on social media. Success is a way of life, it’s a practice.

Don Shula presented at a Microsoft event I attended. It was a great speech, all about the pursuit of perfection and the importance of practice… practice, practice, practice. When most NFL teams were practicing once per day, Shula had the Dolphins practicing three times a day. I was thrilled to hear from him but not as thrilled as my mentor from Microsoft, Jason, who grew up in Miami and has been a lifelong Dolphins fan. Jason had the honor of escorting Don Shula and his wife throughout the event.

My friend from Microsoft, Jason, shared a story with me this week that sums up the essence of Shula’s life and the main point of this essay. The speech kicked off at 8 AM sharp and there was a rehearsal scheduled for 5 AM. A Vice President from HP was going to introduce Shula, it was all written out and very specific. Sure enough, 5 AM came rolling around and the VP was a no show. Shula lost his temper and demanded that someone go get the VP out of bed and get him to the stage ASAP to practice. Disheveled and barely awake the VP arrived and gave a very poor first dry run. The VP had clearly not practiced and was expecting to just wing it. Shula angrily turned to the team of people prepping the event, including Jason and said, “and that is why we fucking practice!”

We all face challenges in our lives. The point of practice is not to start when the challenge arrives, by then it’s too late. Practice is what you do every day so that when difficulty comes, you’re ready. You may be practicing something you’ve done all your life, or something entirely new, and it’s frustrating to not get it quite right. That’s not what matters. It’s the effort, determination, and dedication to a constant pursuit of excellence that counts. Practice is a way of life.

Let’s go be great.

— Immanuel Kant, An Answer to the Question: What Is Enlightenment?

The Keyword Illusion

All of us have grown up in a world where keywords are the basis for search and advertising. It is everywhere and embedded in everything we do. The device does not matter. The software platform does not matter. The paradigm is the same. You open a search bar, type in some keywords or phrases, and hope you find what you are looking for.

In the world of unlimited computing power and artificial intelligence, this should not be the best we can do. Computer science was never supposed to be built on hope, guessing, or tricking people to click on something that was not exactly what they were looking for. Consider the absurdity of the current paradigm. You ask a direct question and receive a series of answers, half of which are self-serving, the other half close to what you needed, but only if you asked the question perfectly. If that were a conversation with your doctor, you would never go back.

A New Way of Solving the Problem

We tolerate inaccurate, disingenuous search because it is the current paradigm. We are used to it. But familiarity is not the same as adequacy. To drive real change, not just an evolution of technology, you need a fundamentally new way of solving the problem. Immanuel Kant used the phrase “revolution of the way of thinking” (Revolution der Denkart) in the preface to the second edition of his Critique of Pure Reason. He was describing the kind of shift that Greek mathematics brought to geometry and Newtonian physics brought to the natural sciences. Not an incremental improvement, but a complete reframing of what the discipline could be.

What the Industry Must Build

The current search paradigm is insufficient, and the industry knows it. The question is whether the next generation of tools will be another incremental improvement or something genuinely new. I believe the answer lies in a combination of disciplines that have been developing in parallel but have never been properly integrated: ontologies, the semantic web, computational linguistics, and artificial intelligence. Together, these fields offer a way to move beyond keyword matching and toward systems that actually understand what a person is asking and why.

No single discipline will solve this problem. Ontologists cannot build it without engineers. Linguists cannot train it without data scientists. Designers cannot shape it without understanding the underlying knowledge architecture.

The revolution Kant described, the shift from accumulating answers to restructuring the question, requires interdisciplinary teams willing to work at the boundaries of their expertise. The future of search, and of information itself, belongs to the organizations that build these teams: groups where computer scientists sit alongside linguists, where taxonomists collaborate with machine learning engineers, where user researchers inform the ontology.

This is not a technology problem. It is a team-building problem. And the organizations that recognize this first will change the relationship between people and information forever.

Let’s go be great.

— Benjamin Franklin

The Energy of Not Knowing

Think back to your early twenties and the experience of looking for a job. The questions were enormous. What is my future going to be? Will I be successful, fall in love, buy a house, have children? You had a lot of unknowns and a lot of big decisions ahead of you. Now that I am a little older, I want to remind you that at that age, you also had a lot of energy. Exciting and heady times for everyone.

A really big question looming in your life at that time was probably what your career is going to be. You sort of know what you like, and you have ambition, although how much is not always clear. So you go job hunting, talk to your friends, and try to find a place where the work is interesting and the people are good. I remember one of our earliest employees telling me she decided to join the company after seeing photos from our first Christmas party, we were giving away iPads at a whisky bar in DC. She thought to herself, that looks like a cool place to work. At that stage in life, most of us are looking for exactly that: somewhere cool where you can learn a lot.

Where It Started

Most people do not know this about me, but early in my life I was on staff at a Presbyterian church as a youth director. I did not have the job for a long time, but it had a lasting impact on me. I was responsible for the middle schoolers, sixth grade through ninth grade. It is a really tough age, full of changes and confusion about everything. I loved working with kids at that stage because it was not too late to keep them off bad paths. The moral compass can be unsteady at that age, and sometimes a young person just needs someone to talk to. Someone to keep them from getting in with the wrong crowd, doing drugs, committing crimes or worse. They are just trying to figure out how to grow up.

That is where I first learned how much I loved teaching and helping people. It is the most rewarding thing I have done in my career, more than all of the other successes and by some measure. Helping people grow and mature is my life’s calling. It is why I started my company. The idea was simple: if we could bring together a group of smart and happy people, we could make a hugely positive impact on the world. We would learn together, focus on something noble, and try to make millions of people a little bit happier every day.

The Privilege of the Long Road

It has been a long road. Building a company is not a straight line, and the lessons do not arrive on schedule. But the thing I am most proud of, looking back, is not the contracts we won or the technology we built. It is the people who came through our doors as junior consultants and left as leaders. That is the return on investment that no balance sheet captures.

Every experienced professional carries something that no training program can replicate: perspective. You know what it feels like to fail on a deadline, to recover a client relationship, to sit in a meeting where you were the least experienced person in the room and learn something that changed the way you work. That perspective is not just valuable. It is an obligation. If someone gave you a chance early in your career, the best way to honor that investment is to give someone else the same opportunity.

Let’s go be great.