Cognita Journal: July 2026
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On July 4, 2024, I sat in my recliner by the big front window of our Texas Hill Country cabin and filed the documents to establish Cognita Knowledge Management, LLC.
The company I had worked for was circling the drain. Many of my U.S.-based co-workers had been laid off and I expected to get an email any day telling me that the company had filed bankruptcy and my job there would end.
Ernest Hemingway said it best: bankruptcy happens “slowly, then all at once.” The writing had been on the wall for some time, and I had been methodically applying for other knowledge management positions, but to no avail. I had to do something different.
So I declared my independence, put together a business plan and used the last couple of months of employment to get the pieces in place.
(By the way, the choice of July 4 as the filing date was coincidental; I didn’t know the Texas Secretary of State’s office would actually process the documentation and give me July 4, 2024 as the date of formation. Independence day indeed!)
One of the bits of advice I got when I first started talking to other people about my new venture was to give myself two years to get established. It would take that long to really determine whether my venture had legs, or was just a way to waste time.
Well, here we are, two years later. Lately, I’ve been thinking about what worked, what didn’t work, where I am at this point, and where it seems I’m going. My verdict: yeah, this thing has legs.
I’ve published a book, been on podcasts, been invited to speak on webinars and in-person conferences and most importantly, helped clients do knowledge management better, saving them money and grief along the way.
I’m currently working with two clients. When I think about the day-to day work of Cognita KM and the commitments I have outside of work, I’m busier than ever. But I’m okay with being a little busier. I’ve run the numbers, and as we’re heading into the second half of the year, I’ve figured out that I can handle one additional client without cutting in my ability to deliver quality results.
(If you’ve been thinking about improving your KM operation, building a KM strategy or just doing an assessment, get in touch with me soon to get something on the books before the Q4 rush.)
One last thing before we get back to our regularly scheduled KM programming: none of this would have happened without the support of a ton of folks, including Mark Brody of Brohawk Consulting, Brad Shaw at livepro, Fred Stacey and Darren Prine at Cloud Tech Gurus, Stephen Pappas at eGain, Mitch Pautz and Sam Chan at UCSF IT, the fine folks at CX Accelerator and Support Driven, and last but certainly not least, my wife Cheryl and my family near and far.
Going to Chicago
I wasn’t able to make it to Contact Center Week in Las Vegas due to personal commitments (one of them being a Rush show that was rescheduled). My next conference appearance will be the Support Driven Expo in Chicago.
This time, I’ll be on the stage as a speaker instead of sitting in the audience. I’m excited to talk about how customer support organizations can tackle the specter of shadow knowledge.
The Illusion of the Automated Org Chart
You’ve no doubt seen the countless stories (or maybe even encountered them in your job): companies that treat AI as a glorified headcount reduction tool. And you’ve probably also seen the result: seduced by promises of immediate operational savings, a company decides to dismantle their human support structures, which destabilizes the very asset that makes AI function—their organizational memory.
A recent article in Fortune highlights shows that 80% of business executives who have piloted autonomous technologies reported subsequent workforce reductions. Even worse, they made these cuts regardless of whether the technology was actually generating clear financial returns. As the Gartner report quoted in Fortune points out, chasing organizational value solely through headcount reduction is a short-sighted strategy that leads down a path of severely limited returns.
When you lay off experienced professionals under the assumption that an LLM can seamlessly replicate their subject matter expertise, you don't actually eliminate the labor. You merely transform documented expertise into undocumented, fragmented shadow knowledge.
The reality is that agentic AI isn’t some sort of independent program running on a server. In reality. it’s a bunch of processes and subprocesses working together. It’s an end-to-end operational workflow. If your underlying knowledge, ingestion pipelines, re-ranking, and context layers are brittle and unoptimized, your fancy AI bot will encounter terminal delays and hallucinate under high concurrent traffic. AI can’t magically retrieve what’s been forgotten, and it can’t reason over a broken Sharepoint site.
To build a resilient enterprise, stop looking at AI as a replacement for human intellect. Build those AI systems, but only after you have deployed frameworks (like our MVP KM™ approach) that treat knowledge as an evolving, format-first infrastructure capable of fueling both human insight and machine intelligence.
Breaking Silos via the Open Knowledge Format (OKF)
One of the biggest hurdles I’ve consistently encountered in my KM career is the fragmented nature of corporate knowledge. It’s been trapped inside proprietary content management systems, isolated intranet wikis, scattered shared drives, OneNote, stickies, chat threads and agent brains.
Getting from there to a “single point of truth” has been my goal in every KM initiative I’ve worked on. And now there is something that could help get down that road faster. My former employer Google Cloud has introduced the Open Knowledge Format: a pragmatic, vendor-neutral shift away from heavy, centralized "knowledge platforms" and toward a lightweight, decentralized architecture.
OKF relies on flat Markdown files and simple YAML frontmatter to act like a lingua franca that separates knowledge content from restrictive tool ecosystems. (If you don’t know what YAML is, don’t worry, I didn’t either. According to Wikipedia, it’s a “human-readable data serialization language. It is commonly used for configuration files and in applications where data is being stored or transmitted.”
Why is this a big deal? Because it’s a first stab at creating a common knowledge format that can be used by AI and humans alike across systems.
Knowledge as Code: Because an OKF bundle lives as flat files within a directory structure, it integrates corporate documentation directly into engineering and development workflows. This allows organizations to apply software engineering rigor—such as standard Git workflows, pull requests, peer reviews, and seamless rollbacks—to core business assets.
Curing "Wiki Rot" via Division of Labor: Traditional corporate wikis fail because human maintenance is tedious, leaving dependencies and cross-references to decay over time. The OKF explicitly leverages an "LLM-wiki" pattern: automated AI agents handle the bookkeeping—crawling data schemas and updating links—while humans elevate to high-level curators and editors. This is where approaches like KCS® can shine.
Slashing RAG Infrastructure Costs: Enterprises currently spend massive development cycles building custom retrieval pipelines (RAG) to assemble context from fragmented legacy systems. Because OKF pre-structures data relationships into a standardized graph using explicit Markdown cross-links, AI agents can traverse and reason over a repository exponentially faster, bypassing bespoke pipeline costs.
Metrics That Matter: From "Containment" to Frontline Force Multipliers
If your company is still measuring the success of its knowledge management strategy by looking at "Bot Containment" or "Deflection Rates," you’re optimizing for the wrong outcome. At least, that’s according to Varun Sharma’s article recently published by HDI Service and Support World.
Most CX pros know that “containment” is an obsolete approach that damages customer experience. Trapping a frustrated user inside a circular, unhelpful chatbot loop might satisfy an internal deflection dashboard, but it ultimately accelerates downstream customer churn. Forward-thinking organizations are transitioning to Verified Resolution Rates (VRR), a closed-loop KPI that measures absolute goal completion and dynamically turns real-time chat interactions into fresh, self-service knowledge.
Even with AI in the picture, 20% to 30% of all technical tickets reach Tier 2 support or higher. These “escalations” or “elevations” are rarely a resource issue. Instead, they’re a sign that you have critical knowledge gaps, poor information access, or ineffective KM frameworks.
When frontline staff can’t easily get high-quality, embedded documentation, they’re forced to hand off tickets, multiplying labor costs and inflating Mean Time to Resolution (MTTR). By embedding accurate, real-time context via knowledge directly at the Level 1 support tier, organizations can dramatically reduce escalation dependency, maximize existing personnel resources, and elevate customer happiness.
Beyond the Help Desk: Why KCS® is Rebranding for Corporate Survival
By Martin Hobratschk
Principal & Founder
Earlier today, I tuned into an incredibly vital webinar hosted by Kelly Murray from the Consortium for Service Innovation. What was unveiled wasn’t just a minor cosmetic patch or a dry technical adjustment to a legacy process. I’d classify it as a major philosophical pivot.
The Consortium is officially retiring the "KCS v6" (Knowledge-Centered Service) moniker and replacing it with something far more ambitious: Knowledge-Centered Success.
This isn’t a massive, disruptive "version 7" release that will force organizations to overhaul their entire tech stack. Instead, the Consortium has introduced a living, refactored framework designed to naturally extend across the modern enterprise. Why now? Because the profound corporate and societal shifts of the past decade, coupled with the rapid maturation of automation and Generative AI, have fundamentally changed how human beings interact with corporate memory. If your organization is still treating knowledge as a siloed support function, you are already falling behind.
Here’s the strategic "so what" from the update, and why these structural changes matter for your organization's resilience, efficiency, and customer happiness.
The Strategic Shift: Managing Enterprise Demand
For years, Knowledge-Centered Service was cooped up in the basement of customer support and IT help desks. The shift to Knowledge-Centered Success breaks down those artificial boundaries. The new methodology is universally applicable for any knowledge-intensive environment.
Knowledge isn't a department; it's the nervous system of your company. By evolving the framework to handle enterprise-wide knowledge demand, the methodology becomes intuitive for newcomers while remaining seamless for veterans. By building it as a living framework, organizations can implement continuous, minor, iterative refinements rather than fearing the disruptive shock of "big bang" version overhauls.
Dismantling the Digital Landfill: Structural Changes to the 8 Practices
The framework maintains its foundational structure of 4 Solve Loop and 4 Evolve Loop practices, but it shifts a few things around and overhauls some concepts to match modern human behavior.
The Solve Loop: Reuse Before You Create
Knowledge creation can often feel like an assembly line producing excess noise. The re-imagined Solve Loop flips the sequence explicitly to: Reuse → Improve → Capture → Structure.
Starting with Reuse forces teams to abandon the false assumption that every customer or internal interaction demands a brand-new article. This stops the creation of duplicate, low-value noise—preventing what we call a "digital landfill."
The loop introduces a brilliant distinction between capturing context (which must always happen within the flow of work) and actually creating an article (which should only happen when actual, systemic demand dictates it).
The Evolve Loop: From Metrics Surveillance to Workflow Insight
The Evolve Loop shifts away from policing human behavior and leans into enabling it. Performance Assessment has transitioned into Performance Insight. The focus is squarely on active workflow coaching rather than hitting strict, arbitrary quantitative metrics. At the same time, Leadership and Communication has evolved into Change Management, focusing early on motivator strategies that align human incentives with organizational outcomes.
The Complexity Mindset: Where AI Meets Human Ambiguity
The most provocative takeaway from the webinar centers on how we frame value in an automated world. Modern organizations, organizational memory, and Generative AI don’t operate in a linear fashion. They’re complex systems where cause and effect are not directly correlated. This reality demands a complete mindset shift away from predictable templates and toward continuous experimentation and testing.
As AI tools rapidly mature, they’ll autonomously resolve straightforward, transactional Solve Loop issues. This isn't a threat to human workers. It’s really an elevation. The human workforce will naturally migrate toward the Evolve Loop, which focuses on resolving deep ambiguity, building cross-organizational coalitions, and deriving meaning from trends.
Support teams have an opportunity to reframe their corporate narrative. Instead of just being viewed as a cost center solving customer issues, they become the vital engine feeding clean, contextual organizational memory into corporate AI datasets. If your AI is hallucinating, it's because you haven't taken care of the knowledge feeding it.
The Horizon: When Does This Happen?
While the operational rollout has a clear timeline that takes us into 2027, the underlying shifts have already started. The new Practices Guide, Coach Reference Guide, and Measuring Self-Service Success Guide are ready for consumption. Next year, we’ll see new training and certification updates. You don’t have to wait to start evolving your strategy.
The transition from Knowledge-Centered Service to Knowledge-Centered Success isn't just a change in wording. It's a roadmap for keeping your organizational memory alive and resilient in the age of AI. If you’re ready to stop building digital landfills and start leveraging your knowledge as a strategic enabler of efficiency, employee retention, and customer happiness, let's talk.
Get in touch with me today. Let’s partner to evaluate your current framework, design a robust roadmap, and seamlessly implement these next-generation KCS practices within your workflow.
KCS® is a service mark of the Consortium for Service Innovation™
The “Hidden Layer: Why Knowledge Infrastructure is the Secret to AI Success
By Martin Hobratschk
Principal & Founder, Cognita Knowledge Management
In early 2026, many corporate leaders found themselves re-hiring for roles they had just eliminated “because of AI. Why? Because they forgot about the “judgement layer,” or the human expertise that keeps AI from hallucinating.
But even companies who re-hire employees are destined for AI failure if they don’t pay attention to their knowledge infrastructure, a hidden layer that determines whether enterprise AI projects succeed or fail.
Whether it’s stories about Gartner executive surveys, discussions in webinars or conversations at conventions, it’s increasingly clear that one of the main reasons service and support organizations are seeing their AI projects collapse is because they attempt to deploy sophisticated AI models on top of fragmented, unstructured, or ungoverned knowledge.
The end result is unreliable and even completely made up outputs, also known as hallucinations. And that leads to disengaged employees and upset customers.
The organizations that are going to succeed with AI are the ones who treat their internal knowledge infrastructure as a strategic, human-centered asset and core priority.
Powering AI Copilots and Service Agents
To function at an enterprise scale, AI systems have to be more than just standalone chatbots. They have to be deeply integrated with CRM and internal knowledge systems.
In the contact center, this becomes reality with AI copilots that assist human agents by instantly retrieving relevant knowledge base articles, summarizing prior interactions, and suggesting accurate responses in real time.
The next evolution is Agentic AI. Traditional AI acts as an assistant that provides analytical support like a calculator or a researcher. In contrast, an AI Agent is capable of goal-directed decision-making. It can observe an environment, reason through a goal, and execute multi-step actions autonomously.
The division of labor is clear: the agent handles the high-volume technical execution, while the human provides the "judgment layer" and context. As these agents take over the "volume," they don't just replace people; they create an increased demand for new human experts to guide them. The role of the knowledge worker is evolving into a knowledge curator.
Shifting Human Workloads to Knowledge Maintenance
It seems that everywhere you turn these days, there’s a story about some company laying off thousands of employees because of AI. But when you scratch the surface, we’re finding that AI is just a convenient scapegoat, and many of the layoffs are really just the consequences of bad human management decisions.
In all of this, it seems that we are overlooking something important, at least according to one researcher. It’s called the "Automation Paradox." AI excels at the predictable 30% of interactions but frequently fails the 70% that require a judgement, or maybe better yet, wisdom. You know, those situations that involve non-routine context, discretion, and human empathy. The result may be short-term cost savings, but in the long term companies see “demand destruction” and the loss of essential human judgement.
This failure to account for human complexity led to a documented "rehiring wave." In recent report from Forbes, 52% of HR leaders reported rehiring for roles they had previously eliminated via AI within just six months. That’s another added cost, because companies spend roughly $1.27 for every $1 saved through workforce reductions once severance and productivity losses are factored in.
The Swedish fintech firm Klarna serves as a cautionary tale; while their AI assistant initially handled the work of 700 agents and boosted profits by $40 million, the subsequent decline in customer satisfaction scores forced a realization that prioritizing cost over the "judgment layer" erodes long-term brand value.
Successful organizations view AI as an amplifier of human capability, handling the repetition so humans can focus on the high-value complexity.
AI doesn’t make support operations disappear, it shifts them. While AI can handle routine inquiries, human support teams must now dedicate time to maintaining knowledge bases, monitoring AI outputs, reviewing edge cases, and continuously retraining workflows. If the underlying knowledge base is not actively managed, the AI's performance will rapidly degrade.
AI handles the repetitive, the high-volume, and the high-speed pattern matching. This frees human workers to focus on the things that actually drive long-term value: complexity, relationships, and judgment. It’s a partnership. In a world where the cost of intelligence is dropping 280-fold, your value is no longer just what you know, but in how you direct that knowledge to solve the world’s most complex problems.
KM as the Foundation for AI Performance
The relationship between Knowledge Management and Artificial Intelligence isn’t linear. It’s a symbiotic feedback loop. High-quality KM provides the fuel for AI performance, while AI-native tools are revolutionizing the discipline of KM itself, evolving it from a passive archive into an active reasoning engine.
Deploying advanced AI on a weak knowledge base is a foundation built on sand. Many organizations struggle with shadow knowledge: unstructured, fragmented information that remains invisible to automated systems (and other humans). Worse yet is fragmented, unstructured knowledge that is accessible, which has a polluting effect. According to one study, this “knowledge pollution” has a direct, measurable impact on reliability: even a 20% pollution rate in training data results in a 10-percentage-point decline in model accuracy.
AI as the Catalyst for KM Evolution
AI transforms KM from an archive into a living resource that can take action based on context.. AI-native foundations can now unify unstructured knowledge for high-precision extraction, moving beyond simple retrieval to "active reasoning." One company used an AI-driven knowledge foundation to automate complex patent data extraction. By providing the model with a structured knowledge base, they reduced a 4-hour manual task to a 4-minute automated workflow—a 90% reduction in time.
While these technical foundations are essential, they merely prepare the stage for the human operators who must navigate this new automated landscape.
The Path Forward
Success in the AI era won’t be determined by which model you choose, but by the robustness of your knowledge infrastructure and the quality of your human-AI collaboration. The picture is clear: organizations that view AI as a simple replacement for human labor are facing the hidden costs of declining quality and the financial burden of the rehiring wave.
Three immediate steps that every organization can take to ensure success for human-centered AI projects:
Audit your Shadow Knowledge: Most AI projects fail because they rely on fragmented knowledge that is virtually invisible. This shadow knowledge (the unwritten rules and tribal lore of your senior experts) must be formalized into verified assets. Without this, your AI models are flying blind.
Define your Judgment Layer: AI can handle the predictable 30% of tasks, but it lacks the context and empathy for the other 70%. Explicitly define where human experts must act as curators to ensure outputs are contextual and ethically sound. This transforms your workforce into a strategic referee layer.
Invest in Maintenance: Knowledge is a living asset. Ongoing maintenance, involving the pruning of outdated info and monitoring for "model drift," typically accounts for 10–15% of the total cost of ownership. Without this investment, your AI will inevitably degrade into a "digital landfill."
Organizations that treat knowledge as a strategic asset and AI as an amplifier of human capability will achieve durable, compounding gains. The ultimate competitive advantage lies in building a "judgment layer" where people and technology make each other more effective, ensuring that the investment in AI solutions translates into genuine, accountable value.
The End of the "Answer Factory": Why Knowledge-Centered Service is the Only Way to Scale
In the traditional support model, knowledge is treated like a finished product. It’s something polished by a technical writer, stored in a vault, and handed down to agents as a blessed script (or canned response). This is the "Answer Factory" model, and in a 2026 service environment, it is fundamentally broken.
When your agents are merely delivery drivers for static documents, they are slow, disengaged, and prone to error. To build a truly resilient support organization, you must use Knowledge-Centered Service (KCS) principles.
KCS isn't a software update, it’s a shift in organizational memory. It’s the realization that every support interaction is a learning event that should refine your collective intelligence in real-time.
1. The Double-Loop Learning Advantage
In a standard support model, an agent solves a problem and moves on (Single-Loop). In a KCS environment, the act of solving the problem is the act of creating or refining the knowledge (Double-Loop).
By capturing knowledge as a byproduct of the flow of work, you eliminate the knowledge decay that happens when documentation is treated as a separate, monthly chore. Your knowledge base becomes a living, breathing map of reality, not a historical archive.
2. Reducing the New Hire Friction
One of the biggest taxes on a contact center is the Onboarding Gap. It can take months for a new hire to soak up enough shadow knowledge to be effective.
With a KCS approach, the expertise of your veterans is liberated and made searchable instantly. Memorization becomes a thing of the past as agents begin leveraging the collective brain of the organization.
Benefit: Accelerated "Time-to-Competency."
Outcome: Higher employee retention because agents feel empowered, not overwhelmed.
3. Turning Silent Failures into Successes
Without KCS, if an agent finds a workaround for a bug but doesn't tell anyone, that knowledge is trapped. It’s a silent failure of the system.
KCS rewards the Flag and Fix mentality. When an agent updates an article during a call, they aren't just helping themselves, they are helping every agent who follows them. This is how you build operational velocity.
Why Cognita is the Engine for KCS
Implementing KCS is notoriously difficult because it requires a culture shift. This is where Cognita Knowledge Management excels. We don't just give you a repository; we architect the feedback loops and the incentive structures that make KCS stick.
We help you move from “Me” (I know the answer) to "We" (We own the solution). When you implement a Cognita-backed strategy, you aren't just lowering handle time, you’re increasing your organization’s strategic agility.
KCS is the difference between having a library and having a brain. In a library, you have to go look for things. A brain just knows. Stop managing your support documents and start mobilizing your support intelligence.