Welcome to the New and Improved Cognita Journal
Longtime subscribers already know that I’ve decided to make some changes to how I handle my newsletter. Basically, I’m simplifying my workflow and optimizing my content marketing strategy by going with a blog-first approach.
That means I’ll be publishing the newsletter at www.cognita-km.com/blog, then sending it out to subscribers. I’m also going to stop publishing the newsletter on LinkedIn, though I will still be quite active there as it’s my primary social media channel for Cognita.
Let me know if you have any suggestions for improvements!
Upcoming appearances
On September 18, I’ll be speaking at the Association for Talent Development Austin Chapter’s Quarterly Chapter Meeting. Topic: From “Just-In-Case” to “Just-In-Time” – Empowering Learning & Performance with MVP Knowledge Management. Learn more
I’m excited to announce that I’ve been invited to speak at the Contact Center AI Association’s quarterly meeting in New York City on September 25. The meeting will focus on Knowledge Base Management as well as Roles and People Driving AI Initiatives. Learn more
Don't Let Your AI Trip Over its Own Two Feet: Why Trusted Knowledge is the Real MVP!
I recently came across the State of AI 2025: Mid-Year Report from eGain and Unisphere Research, and found some insights that anyone who manages a knowledge base (or cares how they’re managed) should keep in mind.
The report says that 70% of the organizations they surveyed are already piloting or having deployed GenAI. From customer self-service (cited by 45% of respondents) to human-assisted contact centers (38%), AI is quickly becoming part of how we deliver support. Even knowledge workers, including authors and managers, are significant users of GenAI output, at 45% each.
But (and it's a big one) there's a crisis of trust, which probably comes as no surprise. In the survey, 61% of respondents said they’re concerned about the accuracy or consistency of AI answers, and 54% worry about non-compliance. “Garbage in, garbage out" has never been more relevant. If your AI is built on faulty data, you can't expect reliable results.
The report points directly to the Achilles' heel of many organizations: siloed knowledge bases and failing legacy systems. A majority of companies (55%) are wrestling with three or more knowledge silos, and 51% are juggling three or more different KM tools.
As the report puts it, "Simply slapping GenAI over these silos only takes chaos to the next level.” This is exactly why a badly managed KB is worse than no KB at all—you're paying to create problems, not solve them.
If I’ve said it once, I’ve said it a thousand times: knowledge management technology and processes are the key to successfully deploying customer service AI that works (and that people want to use). It looks like 83% of the survey respondents agree. Without a robust knowledge foundation, AI struggles to understand context and intent, leading to frustration for both customers and agents. This isn't just about efficiency; it's about making sure your AI isn't messing up your AI.
So, what's the solution? A robust, well-managed knowledge base that serves as your single point of truth. The approach outlined in my book, “MVP KB: A Practical Guide for Customer Service Knowledge Management" is one way to tackle this problem.
Here’s how a trusted MVP KB and AI can create a powerful duet:
Automation for Efficiency: AI can automate the discovery, creation, curation, publication, and optimization of knowledge, potentially leading to a ten-fold improvement in the speed of knowledge creation and findability of answers. This frees up your human experts to focus on complex issues.
Empowering Your People: Even with AI, human expert involvement is critical for assuring the quality of GenAI output. Your agents are the experts, and the MVP KB empowers them to contribute, share, and provide feedback, turning tacit knowledge into explicit, valuable assets. The knowledge manager acts as a change agent, not a gatekeeper.
Single Source of Truth: Unifying your knowledge into a single, trusted hub is essential. This means better, more consistent answers for customers and faster problem-solving for agents. Currently, close to two in three respondents (64%) in the survey I talked about earlier either have no plans for consolidating their knowledge silos or are unaware of such plans. This represents a key reason for the failure of KM initiatives.
Modern Capabilities: Many companies are still not actively using GenAI for content creation and curation. However, modern KM systems offer AI-powered features for drafting articles, summarizing information, and identifying knowledge gaps, making content creation faster and easier.
Don't Be Afraid to Start!
While 62% of companies are "not in 'love'" with their current KM technology, and many are missing opportunities to leverage GenAI for content creation, the good news is that it’s fixable. Get a jumpstart on building your MVP KB by reading MVP KB: A Practical Guide for Customer Service Knowledge Management, or schedule a free, no-strings consultation with yours truly.
Take the first step toward ensuring your AI systems truly work for your customers and agents, providing accurate, relevant, and accessible knowledge every single time. Make sure your AI plays a winning tune, not just noise.
KM News
This one has 4.5
Derek Smalls being one of the greatest bass players of all time means that I have to share this story about where AI consistently comes up short.
See Me, Feel Me
If you’re not following me on LinkedIn, you may not know that I recently was a guest on the Contact Center Gurus Podcast. I’ve been working with Fred, Darren and the team at Cloud Tech Gurus since late last year. They’ve recently restarted their podcast series, and invited me on to talk about the future of knowledge management. Check it out:
Take on me
I’m not much for hot takes, but I’m of the strong opinion that AI is the ultimate KM tool (if it’s used in the right way). A recent story from AI Invest supports my contention. The AI-driven KM market is on track to be about $9.6 billion by 2025, driven by the urgent need to automate data workflows and analyze unstructured information. By freeing employees from repetitive tasks, they can focus on high-value work, becoming much more efficient. But it can only happen with robust governance frameworks and a clear strategy, turning knowledge from a liability into a genuine competitive asset.
Song of the Week
“MRI” - Derek Smalls (feat. Dweezil Zappa)
Building a People-First Knowledge Base
It’s happened to me more than once: someone asks me to get a new knowledge base going, and during the initial assessment we find an old knowledge base just sitting around gathering dust.
At one company where I worked, I was hired specifically because the VP for Customer Care realized how important it was to have a solid, well-managed knowledge base.
When I got into the job and started digging, the first thing I found was a “sort-of” KB that some SMEs had put together in Microsoft One-Note. Yeah, there was a lot of info in there, but it was out of date, inconsistent and impossible to find. You had to know exactly what you were looking for to find anything – not a great experience, especially for new employees.
I dug a little deeper and discovered that some team managers had made a half-hearted attempt five years before to use Salesforce Knowledge. That was a whole lot better than One Note. But nobody was using it, and the people who had started it up had long since left for other jobs.
I’ve seen this pattern repeated in my consulting work time and again. Someone in the past thought it would be a good idea to have a KB, they tried to build something and then it just … went away.
In all of the cases, the failure boiled down to a simple reason: no one was thinking about the people it served. They were just putting together a system to hold some knowledge artifacts.
But the secret to a successful Knowledge Base (KB) isn't just about the system or even the knowledge it holds; it has put people first. When you empower your team to own the knowledge they use and create, you unlock the true potential of your knowledge assets.
Here’s how to foster a human-centered KB:
Empower Knowledge Users: Your frontline agents are the key to success. They should be using the KB daily to solve customer issues and are often the first to spot knowledge gaps or inaccuracies. When you give them the tools to highlight gaps or areas for improvement, that creates a sense of ownership – meaning they’re more likely to use it.
Leverage Knowledge Contributors (SMEs): In an ideal world, every single agent can share their know-how; that’s the promise of frameworks like Knowledge Centered Service (KCS). In my experience implementing KM programs, I’ve found that Subject Matter Experts (SMEs) are your go-to people for "know-how" and tribal knowledge. They are the ones that should create and update articles directly in the KB as they work. This captures their valuable insights and frees them from repeatedly answering the same questions.
Support Knowledge Curators: Even when you have a healthy number of expert contributors, you still need folks who can curate all the submissions, to make sure that they meet specifications, aren’t duplicates and so on. Knowledge Curators work right beside Contributors, reviewing submissions, ensuring consistency with style guides, and maintaining the KB's overall health and findability. In fact, it’s usually best if your Curators are drawn from the ranks of the Knowledge Contributors.
Define the Knowledge Coordinator/Manager Role: Many companies who don’t have a knowledge management function are adverse to creating management-heavy teams to do the work. Still, it’s crucial to have at least one person who "owns" the KB, overseeing daily operations, monitoring its health, planning improvements, and serving as the primary point of contact for leadership and cross-functional teams. They are a change agent, not a gatekeeper, facilitating knowledge sharing across the organization.
Cultivate a Culture of Sharing: Encourage open communication, collaboration, and reward employees for their valuable contributions. Implement forums like communities of practice (or "Knowledge Cafés" or use channels like Slack/Teams for informal sharing and new knowledge flagging.
At the end of the day, it’s important to remember: knowledge management is something that you do for (and with) people, not to them. Following a collaborative approach ensures your KB will be something people actually like to use because it’s continuously fresh, relevant, and truly valuable.
KM News Notes
Cresta Unveils New "Generative AI for CX" Features to Proactively Assist Agents
When most people think about KM for Customer Service Contact Centers, they think of customer self-help chat bots. But AI-based agent assist is a key tool for contact center leaders. Recently, Cresta announced a significant expansion of its generative AI capabilities that moves beyond reactive assistance to proactive knowledge delivery for customer support agents. The updated Cresta Agent Assist will now automatically surface knowledge base articles, step-by-step guides, and real-time coaching suggestions based on the live context of a customer conversation, without the agent needing to manually search. This addresses a key challenge in knowledge management: ensuring agents can find the right information precisely when they need it, reducing hold times and improving first-contact resolution.
Salesforce Study Reveals AI's Impact on Customer Service Knowledge Gaps
A recent report from Salesforce provides data-backed evidence that AI is crucial for knowledge management success in customer support. One key finding: 81% of service agents believe AI is most valuable for its ability to automatically surface knowledge from various sources. The study shows that companies using AI-powered knowledge management tools saw a significant decrease in agent onboarding time and more consistency in case resolution.
Glean Launches "Workplace Search" Assistant to Unify Enterprise Knowledge for Support Teams
A big challenge for customer support teams in virtually every organization is having to find knowledge that’s trapped in silos across the company. Tech startup Glean has officially launched its new workplace assistant designed to solve this problem. The Glean assistant connects to over 100 different applications – like Zendesk, Slack, Jira, and Confluence – to create a single, unified search experience. When a support agent has a complex customer query, they can ask the Glean assistant one question and get an answer from resolved tickets, internal wikis, team chats and more. This approach drastically cuts down on search time and improves the quality of agent responses.
How to Build a Business Case for Your KM Project
For as long as I’ve been working in contact centers, they’ve been stuck with the "cost center" label. It's the kind of thing that makes every single expenditure, especially for something as crucial as Knowledge Management (KM), feel like it's being scrutinized against razor-thin margins.
Last week, I talked about how to build a knowledge base that customer service contact center agents actually trust and want to use. But even if you're able to figure out why your agents are struggling with a clunky knowledge base and what needs fixing, getting the necessary buy-in – and by that, I mean money and resources – can feel like an uphill battle. The financial and political challenges of securing this kind of investment are pretty substantial?
What I’ve run up against time and again when I’ve tried justifying investing in KM is that it’s often viewed as intangible. And let’s be honest, sometimes attempts at proving ROI don’t exactly deliver on their estimates, which can make things even harder.
This is precisely why we need to reframe the entire conversation. It’s not just about improving your KB or implementing a shiny new knowledge management system. It's about investing in a strategic program designed to achieve specific, measurable business outcomes. Shifting the perspective is absolutely crucial, and it’s the first step in tackling that whole narrative of customer support as just a cost center.
To do this effectively, you've got to approach it from two key angles. The first, and most crucial, aspect of this reframing is strategic alignment. Your Knowledge Management program objectives must be explicitly linked to the overarching strategic goals of the organization.
I’ve seen it more than a few times: companies will spend a lot of time and effort developing an organizational or corporate-wide strategy, but they don't necessarily put a lot of thought into their knowledge management strategy.
If you don’t have a KM strategy that’s closely tied to your organizational strategy, then you’re setting yourself up for failure–or at the very least, holding your organization back from being as successful as it could be.
If your company is laser-focused on improving customer retention, then your KM strategy should directly support that. This could mean looking at key performance indicators (KPIs) like customer satisfaction (CSAT), Net Promoter Score (NPS), or even customer churn.
If the focus is on reducing operational costs, then KM's impact on metrics like Average Handle Time (AHT) or First Contact Resolution (FCR) becomes paramount. We can also look at how KM impacts agent attrition in this context.
When you align your KM initiative with these larger organizational priorities, it becomes part of the solution to a significant business problem, rather than just a standalone project. That’s how you get buy-in.
When you’re putting together your case for knowledge management, remember that a one-size-fits-all business case will fail. Your narrative needs to be specific to the concerns of each key stakeholder.
For example, the Head of Customer Support cares deeply about FCR and AHT. The Head of HR will be focused on agent attrition and engagement. And for the CFO? They're looking at cost savings, revenue impact, and a clear ROI calculation. Your business case must speak their language, presenting the metrics and financial projections that matter most to them.
To build a truly credible and compelling narrative, we need a holistic framework for measuring KM success that captures its full impact. This framework can be broken down into four distinct, yet interconnected, pillars:
Operational Efficiency: This is all about running leaner, cheaper, and faster. Think about how KM can directly reduce AHT and improve FCR. Crucially, a really good KM program can also shine by deflecting contacts through successful self-service, preventing them from even reaching the contact center. This ticket deflection is super powerful and a key area where KM can demonstrate significant savings.
Agent Enablement and Retention: This pillar focuses on creating a more effective and stable workforce. Can you reduce new agent training time or agent onboarding time? Can you lower agent attrition?
A better knowledge infrastructure is a direct answer to frustrated agents and burnout because it provides them with the support they need to do the jobs you're asking them to do. This translates into real money saved by reducing recruitment costs, retraining costs, advertising fees, and all the overhead administration of hiring and onboarding.
While sometimes considered "soft," agent satisfaction and engagement is a super powerful leading indicator for agent attrition. You can use internal surveys like eNPS to measure the impact of your improved KM system on agent morale, providing both qualitative and quantitative data to strengthen your business case.Customer Experience and Loyalty: The goal here is simple: create happier and more valuable customers. The metrics are clear and obvious: Customer Satisfaction (CSAT), Net Promoter Score (NPS), and Customer Effort Score (CES). CES, for instance, is a really good way to understand how hard your customers are working to get their problem solved. A really good Knowledge Management System, both for agents and for self-service, is going to make it a lot easier for customers to get their issues resolved.
When you lower that customer effort, that’s a really key driver for loyalty. When customers don't have to work hard, they're much more likely to stay.Direct Financial Impact: This is the bottom line, the total quantifiable sum of savings from all the other pillars we've talked about. This includes reduced labor costs from lower AHT, fewer repeat calls due to higher FCR, reduced recruitment and training costs from lower attrition, and significant savings from deflected self-service tickets.
Perhaps most strategically, you can connect improved customer service to tangible revenue outcomes. Research from Bain & Company suggests that a 5% improvement in customer retention can increase profitability by anywhere from 25% to 95%.
Your business case needs to model how the financial impact of reduced customer churn, resulting from higher CSAT and NPS scores due to a better KM system, is really going to impact that bottom line. When agents aren't struggling to retrieve basic information, they have a lot more capacity for higher-value activities like upselling or cross-selling, which can also be tracked and quantified.
Ultimately, a successful business case for KM isn't just a spreadsheet churning out a final ROI percentage. It's a compelling and defensible narrative that tells a powerful story of transformation. The most effective leaders understand they're not just presenting calculations; they're persuading skeptics and sharing a vision for a strategy that's going to drive success for the entire organization.
If you’re interested in making a stronger case for your KM program, let’s talk!
Why Your Agents Hate Your Knowledge Base (And How to Fix It)
One of the biggest problems I’ve faced as a knowledge manager in customer service contact centers falls under the umbrella of agent adoption. I’ve run projects where we replaced an old, clunky knowledge base—something built in SharePoint or OneNote (yikes)—with a brand-new system in Salesforce, complete with a much-improved search algorithm. And yet, even after the upgrade, I’d still hear the same complaint from agents:
“I can’t find anything.”
It’s a frustratingly common problem. Even if you have a good system, the KB often becomes a source of irritation rather than a tool for success. This isn’t just about one thing; it’s a failure on multiple fronts.
First, there’s the search itself. Even with powerful algorithms and suggested content, older keyword-based search is flawed. We used to spend so much effort maintaining lists of keywords for each article, but agents would still get a dozen results with the same tags. They’d have to click into each one, read a bit, and back out, all while a customer is waiting on the line. That wasted time is a direct hit to your metrics.
Second, the information itself isn’t reliable. Agents search and find multiple articles on the same topic with slightly different, contradictory takes. They use one, get burned in a QA evaluation or anger a customer, and immediately lose trust in the entire system. Content health is crucial; if agents can’t rely on what they find, the tool is useless.
Finally, the design and structure of the content is often a major pain point. Agents aren’t knowledge managers; they’re problem-solvers under immense pressure. They need information structured logically and intuitively.
I remember working at one company where we had a very detailed policy for handling certain issues. The subject matter experts were adamant that it needed to be one big document with everything included. I really had to fight the battle to break it up into digestible chunks. When we finally did, the adoption and user ratings for that content went way up. It became understandable.
These failures have real consequences. They create longer handle times, lead to inconsistent answers across channels, and erode customer confidence. Most importantly, they burn out your agents. When agents feel uncertain and get beat up every day because their tools are failing them, they’re not going to stick around. All the money you invested in training and retaining them is wasted.
So, how do we fix it?
Improving discoverability is paramount. Modern, AI-powered search helps a lot because it can better predict the agent’s intent. An agent co-pilot that listens to the conversation and suggests the right content in the moment is a game-changer.
But the most overlooked solution is to engage your agents in knowledge creation. They can’t just be consumers of knowledge. Methodologies like Knowledge-Centered Service (KCS), which the Consortium for Service Innovation has been developing for nearly 30 years, are built on this idea. In KCS, knowledge is created as a by-product of solving problems. Agents are actively involved in creating new articles and flagging what’s outdated or conflicting.
To make this work, you need to build great feedback mechanisms and incentivize their use. Maybe you create a program that rewards agents whose feedback results in a change to an article. Give them the tools to create and share knowledge when they come across something new. New AI tools can even help, taking case notes and conversations and drafting an article for an agent to validate.
The knowledge they’re sharing and updating must be structured correctly. This isn't just about making it look neat; it's about making it usable. Think like an agent and organize information in the natural order of a call: greeting, authentication, troubleshooting steps, and so on.
Use formatting that makes information scannable—bullet points, highlighted scripting, and clear headings instead of long, dense blocks of text. You also need different article formats for different needs. A brand-new employee needs a verbose, step-by-step guide, while a veteran agent just needs a bare-bones refresher. (Exposing content based on roles is a powerful way to help improve discoverability, too.)
These days, you also have to structure content for AI. This means using standardized templates, like the "Problem-Solution" format, and clearly labeling sections so that bots can digest the information correctly. A good practice is to include a detailed procedure for the main body of the article but also add a list of FAQs at the bottom. This gives both agents and AI the best of both worlds: a comprehensive guide and quick, specific answers.
The bad news? This sounds like a lot of work.
It is.
The good news: AI tools can help with all of it. It’s not here to replace your agents, it’s here to help them–and by extension, your customer service team–work smarter and faster. By engaging your users in the conversation, you amplify acceptance and adoption.
Ultimately, this is about more than just technology. It’s an organizational transformation. The Boston Consulting Group calls it the 10/20/70 rule: 10 percent of thought toward the algorithm, 30 percent toward technology and 70 percent on organizational transformation.
Starting with a holistic approach and focusing on the human element is the key.