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Your Organisation Has a Knowledge Management Problem. It Just Doesn’t Know It Yet

(This post originally appeared on my LinkedIn Profile.)

Ask most people what Knowledge Management means and they’ll mention a chatbot. Or a FAQ database. Or the system the contact centre uses to answer customer calls faster.

They’re not wrong. That is KM. But it’s one room in a very large house.

And while everyone’s focused on that room, the rest of the house is quietly falling apart.

The contact centre problem

Contact centre KM is visible. It has vendors. It has metrics. It has a clear ROI story — faster handle times, fewer escalations, happier customers. There are software platforms built specifically for it. Conferences dedicated to it. Entire consulting practices around it.

So when organisations say “we have KM,” they often mean “we have a tool that helps agents answer questions.”

That’s not KM strategy. That’s knowledge delivery at one endpoint.

What’s happening everywhere else

While the contact centre is meticulously managed, here’s what’s happening in the same organisation:

A senior leader retires and takes twenty years of institutional memory with them. Nobody captured it. Nobody thought to.

A project team spends three weeks solving a problem that another team solved eighteen months ago. The solution existed. It just wasn’t findable.

A proposal goes out with assumptions that contradict positions taken on a previous engagement. The left hand didn’t know what the right hand had written.

A new hire takes six months to reach full productivity because the knowledge they need lives in people’s heads, not in any system.

None of this shows up in a contact centre dashboard. All of it is a knowledge management failure.

The invisible KM problem

Contact centre KM is visible KM. It has a home, a budget, and a team accountable for it.

Everything else is invisible KM — the organisational knowledge that lives in people, relationships, past work, and institutional memory. It has no home. No owner. No metrics. And so, no urgency.

Until someone leaves. Until a bid is lost. Until a mistake is repeated. Until the organisation realises it keeps reinventing the wheel because nobody was minding the wheel.

KM is a whole-organisation discipline

The international standard for KM — ISO 30401 — doesn’t mention contact centres. It talks about strategy, culture, learning, leadership, and governance. It defines KM as a management discipline that helps organisations create, retain, share, and apply knowledge to achieve their objectives.

APQC, one of the most respected research bodies in the field, frames KM across five pillars: strategy, process, content, culture, and technology. Technology is one pillar. The contact centre sits inside technology. That’s a lot of building left unattended.

The questions that reveal the real problem

You don’t need a formal audit to see where your organisation’s invisible KM is failing. Just ask:

  • What happens when your most experienced person walks out the door?
  • How long does it take a new hire to become genuinely productive?
  • How do you capture what worked — and what didn’t — at the end of a major project?
  • Can you find your organisation’s best thinking on any given topic in under five minutes?
  • Do your teams build on each other’s work, or do they start from scratch every time?

If those questions make people uncomfortable, that’s not a contact centre problem. That’s a knowledge strategy problem.

What to do about it

Start by separating the tool from the discipline. Contact centre software is a tool. KM is a discipline that applies across every part of an organisation — from how you onboard people to how you run retrospectives to how you capture competitive intelligence to how you make expert knowledge accessible to everyone who needs it.

Then look at where knowledge is genuinely at risk. Who are your critical knowledge holders? What happens when they leave? Where does important knowledge live that isn’t written down anywhere?

You don’t need a massive programme to start. You need someone asking the right questions. And you need leadership that understands that knowledge isn’t just a contact centre asset — it’s the organisation’s most valuable and most under protected resource.

The contact centre KM is working fine. That’s not the problem.

The problem is the illusion of coverage it creates. The belief that because one type of knowledge is well-managed, the rest is too.

It isn’t. And the cost of that gap shows up slowly — in turnover, in repeated mistakes, in lost bids, in the quiet erosion of things that once worked.

KM done well is invisible too. You notice it in how fast people find what they need. In how smoothly transitions happen. In how teams learn from each other rather than around each other.

That kind of KM doesn’t live in a single tool. It lives in how an organisation thinks about what it knows — and what it can’t afford to lose.

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Your KM Problem Isn’t the Platform and AI isn’t going to fix it

(This post originally appeared on my LinkedIn Profile and received a lot of attention there, feel free to read the comments there.)

I’ve spent my entire career fighting the same battle.

Someone finds out I work in knowledge management, and within minutes the conversation has shifted to SharePoint, or Confluence, or whatever platform is currently being championed as “the solution.” These days, that platform increasingly has “AI-powered” somewhere in the marketing copy.

And I understand why. Technology is visible. It has a price tag, a vendor, a demo. You can point to it in a procurement document. Knowledge — the human, messy, relational kind — is much harder to put in a slide deck.

But here’s what two decades in KM have taught me: the platform is never the problem. And it’s never the solution either. Neither is the AI layer on top of it.

When a knowledge base goes stale three months after launch, that’s not a technology failure. It’s a culture failure. When frontline staff can’t find what they need in the moment of need, it’s usually not because the search function is broken — it’s because the knowledge was never captured in a way that reflects how those people actually think and work.

Now imagine pointing an AI at that same broken foundation. A large language model is extraordinarily good at generating fluent, confident-sounding responses. It is only as trustworthy as the content it draws from. Outdated articles, undocumented workarounds, tribal knowledge that never made it into the system, contradictory guidance left unresolved for years — AI doesn’t fix any of that. It amplifies it. Confidently.

This focus on people and relationships is the heart of Radical Knowledge Management.

Radical KM starts from a simple premise: knowledge lives in people, not systems. Systems can support its flow, but they cannot create it, sustain it, or replace the trust and relationships that make people willing to share what they know. If you haven’t invested in those human conditions first, you can implement the most sophisticated AI-powered platform on the market and still end up with an expensive, high-velocity misinformation engine.

Moving beyond “KM is a technology problem” requires three honest conversations most organisations are reluctant to have:

Who owns knowledge here — and who is excluded from that? Knowledge governance isn’t about permissions and taxonomies. It’s about power. Whose expertise is captured and valued, and whose is invisible? AI will simply encode and accelerate whatever answer your organisation has already given to that question.

What does your culture reward? If people are measured on individual output and knowledge-sharing is an afterthought, no tool will change that — and no AI will surface knowledge that was never shared in the first place.

What does “good” actually look like for the people doing the work? Not for leadership. Not for auditors. For the person answering their fifteenth call of the day, under pressure, needing to find an accurate answer quickly. AI can be a powerful ally for that person — but only if the knowledge behind it has been curated with their reality in mind.

Technology has an important role. I’ve spent considerable time helping organisations choose and implement platforms, assess readiness, and design governance frameworks. The tools matter. AI genuinely matters. But they’re the last conversation, not the first.

The first conversation is about people. What they know, how they share it, whether they feel safe doing so, and whether the organisation has created the conditions for knowledge to actually flow.

Until we’re willing to have that conversation — really have it — we’ll keep buying new platforms, adding AI on top, and wondering why nothing changes.

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Your Contact Centre Has a Knowledge Base — It Probably Doesn’t Have a Knowledge Management Strategy

(This post was originally shared on my LinkedIn Profile.)

There’s a distinction that rarely gets made in contact centre operations — and it’s costing organisations more than they realise.

A knowledge base is a technology. A knowledge management strategy is a discipline. Most government contact centres have invested in the first and almost completely ignored the second.

I’ve seen this pattern repeatedly in my work with public sector clients: a KB that was built with good intentions, populated in a flurry of activity at go-live, and then largely left to drift. Articles go stale. Ownership blurs. Agents stop trusting the content and start working around it — calling a colleague, improvising an answer, or escalating unnecessarily. The KB becomes a liability dressed up as an asset.

The Real Cost of Undermanaged Knowledge

Here’s what undermanaged knowledge actually looks like in practice:

  • The majority of KB articles in a typical contact centre are rarely or never accessed by agents — not because the need doesn’t exist, but because the content isn’t trusted, findable, or current
  • A significant portion of agent time on any given call is spent searching for information rather than using it — a direct consequence of poorly governed knowledge
  • Training costs multiply when knowledge isn’t structured — new agents take longer to ramp, and the learning is inconsistent
  • Compliance risk accumulates quietly — outdated content in a regulated environment isn’t just inefficient, it’s a governance problem

None of this is inevitable. It’s the predictable result of treating knowledge as a byproduct rather than managing it as infrastructure.

Where Most Contact Centres Actually Sit

When I assess contact centre KM maturity, I use a five-level model:

Level 1 — Reactive: A KB exists but it’s unstructured, inconsistently used, and has no governance. Knowledge lives in people’s heads or tribal networks.

Level 2 — Managed: Content standards are beginning to emerge. Ownership is being assigned. Review cycles exist on paper if not always in practice.

Level 3 — Defined: KM is integrated into agent onboarding, quality assurance, and performance frameworks. Knowledge-Centred Service (KCS) methodology may be in scope.

Level 4 — Optimised: Continuous improvement loops are running. Agents actively create and flag knowledge as part of their workflow. Metrics drive decisions.

Level 5 — Strategic: Knowledge management is a competitive differentiator. The KB feeds self-service channels. AI-assisted tools are deployed on a well-governed foundation.

The majority of government contact centres I encounter are operating between Level 1 and Level 2. They have the technology. They lack the strategy, the governance, and the culture to make it work.

What Moving Up the Maturity Curve Actually Requires

There is no shortcut from Level 1 to Level 4. But the path is well understood, and each step produces real, measurable value.

Start with an honest diagnostic. Before proposing solutions, you need to understand where the gaps actually are — not where leadership assumes they are. A structured KM health check covering content quality, governance, agent culture, technology integration, and measurement gives you a baseline and a prioritised roadmap.

Fix the content before you fix the culture. Agents won’t trust a KB they’ve learned not to trust. A content audit — systematically assessing quality, relevance, and alignment with how customers actually query — is often the fastest way to rebuild confidence and demonstrate early wins.

Build governance that outlasts any individual. The most common reason KM investments decay is that accountability wasn’t designed in. Without clear roles (who owns what domain, who approves what content, what the review cycle is), even excellent knowledge management falls apart when key people leave. And in contact centres, people leave.

Consider KCS when the conditions are right. Knowledge-Centred Service is the industry-standard methodology for embedding knowledge creation into resolution workflows — so that KB maintenance becomes a natural output of doing the work, rather than a separate burden. It’s transformative when an organisation is ready for it. A readiness assessment determines whether yours is.

Why This Matters More Now Than It Did Five Years Ago

There are two trends that make knowledge management infrastructure more urgent than it’s ever been.

The first is AI readiness. Every contact centre AI tool — virtual agents, agent-assist copilots, automated knowledge surfacing — depends on a well-structured, well-governed knowledge base to function. Organisations that invest in KM maturity now are building the foundation for AI deployment. Those that don’t will find that AI amplifies the mess rather than solving it.

The second is workforce continuity. Public sector contact centres face persistent turnover pressures. When knowledge lives in people rather than systems, every departure is a knowledge loss event. Structured KM is one of the most practical responses to that risk.

The Business Case Is Operational, Not Theoretical

I’ve noticed that KM initiatives sometimes struggle to get funded because they’re framed as capability-building or best practice adoption — language that doesn’t move budgets in government contracting environments.

The frame that works is operational infrastructure. Knowledge management improvement reduces handle time, lowers training costs, decreases escalations, reduces compliance risk, and creates the conditions for AI deployment. Those are outcomes executives fund.

The entry point doesn’t have to be a large programme. A two-to-three week KM health check produces an executive-ready maturity scorecard and prioritised recommendations. That’s enough to make the case for what comes next — and often, it’s enough to start the conversation that should have happened years earlier.

If you’re working in or with government contact centres and recognise this pattern, I’d be glad to connect and compare notes. The gap between having a knowledge base and having a knowledge management strategy is wide — but it’s entirely closeable.

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Your Organisation Is Losing Its Most Valuable Knowledge — And You’re Looking in the Wrong Place

(This post was originally posted on my LinkedIn Profile.)

Most knowledge management programmes are built on a flawed assumption: that the knowledge worth capturing is the kind you can write down.

Databases. Repositories. Best practice libraries. Lessons learned registers. These are the tools we reach for, and they are not without value. But they systematically leave behind the knowledge that organisations need most — the tacit, relational, embodied, and culturally embedded knowledge that lives in people’s instincts, their ways of seeing, their ability to navigate ambiguity. And when those people leave, retire, or burn out, that knowledge goes with them.

I have spent years pulling at the thread of what it would take to build a genuinely different approach to knowledge management — one that takes seriously the full range of what humans know, not just the fraction that fits into a text field. The review of 70+ research studies that have been sitting on my hard drive has strengthened my conviction that the answer lies somewhere most KM practitioners have not looked: in the arts, in creativity, and in a fundamental rethinking of what knowledge really is.

Creativity Is Not a KM Output. It Is a KM Process.

Most frameworks treat creativity as something that knowledge enables — invest in KM, and innovation follows. The research suggests the inverse is equally true, and arguably more important: creative processes are themselves a primary mechanism for knowledge creation.

This is not a semantic distinction. It has structural implications. If creativity is a core KM process — not a downstream benefit — then knowledge management systems need to be architected around enabling creative application, not just knowledge access and storage.

The practical implication is this: your knowledge management programme will never perform to its potential if it optimises for retrieval while neglecting creation. Knowledge that is shared but not creatively applied circulates without transforming. It becomes organisational wallpaper.

What Neuroscience Is Telling Us About How Knowledge Is Actually Formed

Here is something that does not appear in most KM strategy documents: arts engagement simultaneously activates multiple brain systems — sensory, emotional, memory, and social — that purely linguistic knowledge exchange does not reach.

This is not a soft claim. It is a structural argument, supported by neuroscience research, for why arts-based processes generate different and richer knowledge than text-based alternatives. When we design knowledge processes that engage multiple sensory modalities — visual, auditory, kinaesthetic, tactile — we enhance memory formation, creative connection, and knowledge retention in ways that a well-formatted document simply cannot replicate.

The corollary is equally important: the knowledge that organisations most need to preserve and transfer — what experienced practitioners know through gesture, spatial sense, physical practice, and professional intuition — is precisely the knowledge that conventional KM systems are structurally unable to capture.

The Knowledge Your Organisation Cannot Afford to Keep Losing

Research by Root-Bernstein on Nobel laureates and highly innovative scientists reveals a striking pattern: artistic avocation and scientific or organisational innovation are strongly linked. This is not biographical curiosity. It points to something structural about cognition: the moves developed through artistic practice — observation, pattern recognition, aesthetic discrimination, creative synthesis — transfer directly into knowledge work and produce measurably better outcomes.

Meanwhile, a landmark meta-analysis of 268 publications and 205 documented cases of arts-based interventions in organisations identified 393 distinct positive outcomes. These were not soft or anecdotal: they mapped directly onto what we recognise as KM objectives — improved collaboration, enhanced sense-making, stronger learning cultures, greater capacity to navigate complexity.

The evidence base for arts-informed approaches to knowledge management is substantial. What has been missing is a methodology that makes it organisationally accessible.

The Problem with How We Measure Creativity

One of the most consistent findings in the creativity measurement literature is that organisations damage creative performance by applying innovation metrics to creative processes. The two modes require fundamentally different conditions, incentives, and approaches to evaluation.

The most common way organisations kill creativity is by applying business-case logic too early. Ideation phases — genuinely exploratory, divergent, high tolerance for ambiguity — need to be protected from premature evaluative pressure. This is not a cultural nicety. It is a design requirement.

The research recommends measuring creative process quality alongside outputs: psychological safety levels, diversity of perspectives in dialogue, quality of questioning, willingness to challenge assumptions. And it argues for tracking disposition indicators as leading measures — curiosity, comfort with ambiguity, eagerness to explore — because these predict creative output better than measuring outputs directly.

This is a genuinely different logic from most organisational measurement frameworks, and it matters because the wrong measurement approach actively undermines the thing you are trying to build.

The Rise of AI Makes This More Urgent, Not Less

Two threads in the research address the AI question directly, and both arrive at the same place: the risk is not competition between human and artificial intelligence. The risk is substitution — outsourcing creative processes to AI in ways that reduce human engagement in precisely the activities that generate authentic knowledge and genuine human connection.

The knowledge created through creative, arts-informed processes carries something that AI-assisted processes cannot replicate: the self-disclosure and authentic engagement of human beings working through complexity together. This is not sentimentality. It is a functional argument for why relational knowledge — the kind created in the interspace between people — remains irreducibly human, and why protecting it matters more as AI becomes more capable, not less.

AI belongs in knowledge management as infrastructure: search, synthesis, organisation. Human creative engagement belongs at the centre, not as a nice-to-have, but as the core activity that makes the rest of it worth doing.

What This Means for How Organisations Should Approach KM

The research points to several structural changes that would make a genuine difference.

Organisations need to create what researchers call interspaces — protected environments, physical and psychological, where conventional organisational norms are temporarily suspended so that genuinely different knowledge can emerge. The arts are particularly effective at creating these spaces because they carry cultural permission for experimentation and ambiguity that normal business contexts do not. But the research is clear that an interspace only delivers KM value if insights flow back into mainstream practice. The re-integration pathway matters as much as the creative space itself.

Demographic diversity is necessary but not sufficient. What the research points to is cognitive diversity — different thinking modes, expertise domains, and problem-solving approaches — as the primary driver of creative knowledge work. The research from Unilever’s innovation practice establishes this as the primary predictor of creative problem-solving performance. And it requires facilitation processes designed to harness the productive friction of different perspectives, rather than suppressing it in the name of social comfort.

Knowledge management needs to be framed as a wellbeing and human development practice, not merely an efficiency tool. The neuroscience literature establishes that arts engagement generates measurable benefits to stress resilience, emotional regulation, and cognitive flexibility — all of which directly affect the quality of knowledge work over time. Organisations that attend to the human experience of knowledge work generate more and better knowledge. This is not a values claim. It is a performance claim.

A Different Kind of Knowledge Management is not just Possible. It Is Necessary.

We are asking our organisations to navigate a world of genuine complexity — technological disruption, demographic change, shifting social expectations, and wicked problems that do not yield to standard playbooks. The knowledge management frameworks we inherited from the 1990s were designed for a different world. They optimised for efficiency, standardisation, and explicit knowledge. They have served their purpose.

What we need now is a methodology that takes seriously the full spectrum of what human beings know — including the knowledge that lives in the body, in relationships, in aesthetic sensibility, in the capacity to sit with ambiguity long enough to generate something genuinely new.

That is what Radical Knowledge Management is designed to do.

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What does world-class knowledge management look like?

(This post was originally posted on my LinkedIn Profile and has been edited slightly.)

I’ve been reflecting on this question through the lens of the Global MIKE Award — the international benchmark that’s recognised organisations like Infosys, Wärtsilä, and Bangchak for excellence in knowledge and innovation management. As a member of the judging panel since 2022, I’ve had the privilege of reviewing submissions from organisations across sectors and geographies, and patterns emerge.

What strikes me is what doesn’t appear in the assessment criteria.

There’s no category for “most sophisticated technology stack.” No points awarded for the slickest search interface or the largest content repository.

Instead, the eight criteria centre on distinctly human concerns: empowering knowledge workers, transformative leadership, cultivating innovation culture, developing networks and connectivity.

The technology is assumed. The differentiator is everything else.

This resonates with something the Consortium for Service Innovation discovered when developing Knowledge-Centered Service methodology in the 1990s. They started by focusing on tools — and then realised that success “was more about people and their behaviours than it was about the tool.”

Three decades later, we’re still learning this lesson.

World-class KM isn’t a system you install. It’s a set of organisational capabilities you cultivate:

Knowledge created in the flow of work, not in isolation from it. Governance that enables rather than restricts. Frontline workers trusted as authors, not just consumers. Contribution recognised and celebrated. Learning embedded in daily practice.

In contact centres I see organisations investing heavily in AI-powered knowledge tools while neglecting the fundamentals: governance, culture, content quality, feedback loops. The technology can’t compensate for these gaps. It amplifies them.

The MIKE framework reminds us that intellectual capital has three dimensions: human, relational, and structural. Most technology investments target structural capital alone. World-class organisations invest across all three.

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The Magic in the White Space: why what we can’t measure what matters most

(This post originally appeared on my LinkedIn profile.)

We’ve optimised ourselves into a corner.

Somewhere along the way, we started measuring everything. Click-through rates. Response times. Conversion metrics. Engagement scores. Customer lifetime value. Net Promoter Scores. And in doing so, we forgot something fundamental: people aren’t data points.

I’ve been thinking a lot lately about how our obsession with technology and analytics has made us deeply, painfully transactional. Every interaction has become an opportunity to capture, quantify, and optimise. We’ve replaced conversations with touch-points. Relationships with customer journeys. Trust with sentiment analysis.

The cost? We’ve lost our ability to think holistically. And we’ve forgotten that the most important things that happen in organisations—and in life—happen in the spaces between the things we measure.

The Tyranny of the Measurable

There’s a management axiom that gets trotted out with alarming frequency: “What gets measured gets managed.” It’s usually attributed to Peter Drucker, though he probably never said it in quite that way. Regardless of its provenance, we’ve taken it as gospel and built entire industries around it.

But here’s the problem with making measurement the centre of everything: we start to believe that if something can’t be measured, it doesn’t matter. Or worse, it doesn’t exist.

Consider what happens when everything is reduced to discrete, measurable transactions. We stop seeing patterns. We stop understanding context. We stop recognising that the person on the other end of that support ticket is the same one who attended our webinar, recommended us to a colleague, and has been quietly championing our work for years. Their “customer journey” looks like a series of disconnected events in our CRM, but their actual relationship with us is something far richer—and far more valuable—than any dashboard can capture.

Strategic thinking requires us to hold complexity. To see connections that don’t fit neatly into a spreadsheet. To value things we cannot easily quantify—like loyalty, goodwill, institutional memory, and the trust that accumulates through countless small moments of being seen and valued.

What Lives in the White Space

In design, white space—the empty areas around and between elements—isn’t wasted space. It’s what makes the rest of the design work. It provides breathing room, creates emphasis, and allows the eye to rest. Without white space, everything blurs together into noise.

Organisations have white space too. It’s the corridor conversation that sparks an unexpected idea. The coffee break where two people from different departments discover they’re working on complementary problems. The offhand comment in a meeting that someone remembers six months later and applies in a completely different context. It’s the trust that accumulates not through formal processes but through countless small moments of showing up, following through, and treating people as whole human beings rather than inputs in a workflow.

None of this fits in a spreadsheet. None of it shows up in your quarterly metrics review. And yet—ask anyone where their best ideas came from, and they’ll almost never point to a scheduled brainstorming session or a formal innovation programme. They’ll tell you about a conversation. A chance encounter. A moment of connection that couldn’t have been predicted or planned.

This is where the magic happens. And we’re systematically eliminating it.

The Efficiency Trap

In our pursuit of efficiency, we’ve optimised away the very conditions that allow innovation, creativity, and genuine human connection to flourish. Every minute is accounted for. Every interaction has a purpose. Every meeting has an agenda and a hard stop.

We’ve mistaken activity for progress and transactions for relationships.

The irony is profound. We invest millions in innovation programmes while simultaneously creating cultures that leave no room for the unstructured thinking that innovation requires. We talk about the importance of relationships while treating every customer interaction as a data point to be captured and processed. We celebrate “human-centred design” while building systems that reduce humans to behavioural patterns and conversion funnels.

You can’t build a relationship in a transaction. You build it in the space between transactions. In the follow-up that wasn’t required. In remembering what matters to someone. In showing up consistently, not just when the metrics demand it.

A Different Way of Seeing

So how do we measure the magic that happens in the white space?

I think the uncomfortable answer is: we don’t. Not directly, anyway. The white space is where serendipity lives, and serendipity resists quantification. That might be precisely the point.

But that doesn’t mean we’re helpless. It means we need to shift our focus from measuring the magic itself to measuring—and deliberately cultivating—the conditions that allow magic to happen.

We can ask different questions. Do people have unstructured time to think and connect? Are there spaces—physical or virtual—where unexpected collisions can occur? Is there psychological safety to share half-formed ideas without them being immediately evaluated against KPIs? Do we reward relationship-building, or only task completion?

We can notice the outcomes, even if we can’t trace the path. Innovation doesn’t announce its origins. But we can ask: where did that breakthrough actually come from? When we bother to trace these things back, the answer is almost never “the innovation committee met and decided.” It’s “I had a conversation with someone in a completely different department” or “I remembered something a client mentioned offhand three months ago.”

We can listen for stories instead of statistics. Narrative is how humans have always captured what metrics cannot. The organisations that understand the value of white space are the ones that collect and share these stories deliberately—not as marketing material, but as genuine organisational knowledge about how value actually gets created.

The Human Imperative

This matters more now than ever. As artificial intelligence takes over more of our transactional work—answering routine queries, processing standard requests, analysing data at scale—what remains distinctly human is precisely the stuff that happens in the white space. The intuition. The empathy. The ability to hold ambiguity and see connections across domains. The relationships built on genuine understanding rather than algorithmic prediction.

If we continue to optimise humans out of the white space, we’ll have nothing left to offer that machines can’t do better. But if we recognise that the white space is where our uniquely human value lives, we can build organisations that amplify rather than replace what makes us irreplaceable.

Technology should serve relationships, not replace them. Data should inform human judgement, not substitute for it. Efficiency should create space for what matters, not consume every moment in pursuit of marginal gains.

An Invitation

Perhaps it’s time we measured what actually matters—even if that means accepting that some things can’t be measured at all.

What would it look like to run an organisation that valued the white space? That deliberately protected time for unstructured connection? That evaluated leaders not just on what they delivered but on the relationships they built and the conditions they created for others to thrive?

What would it look like to approach each interaction not as a transaction to be completed but as a relationship to be nurtured? To see the person behind the data point, the story behind the metric, the magic behind the measurement?

The magic is in the white space. It always has been.

Maybe it’s time we stopped trying to fill every inch of it with something we can count.

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The AI Revolution in Call Centres: Elevating Customer Satisfaction Through Smart Technology

(This post originally appeared on my LinkedIn profile.)

The landscape of customer service is experiencing a transformational shift as artificial intelligence becomes the cornerstone of modern call centre operations. With the global call centre AI market valued at US$3.23 billion in 2024 and projected to reach approximately US$25.84 billion, organisations worldwide are recognising that AI isn’t just a technological upgrade—it’s a strategic imperative for delivering exceptional customer experiences.

The Business Case for AI in Customer Service

Recent research presents compelling evidence for AI adoption in call centres. Mature AI adopters reported 17% higher customer satisfaction scores, while human agent satisfaction increased by 15% when supported by intelligent systems. These aren’t merely incremental improvements; they represent a fundamental shift in how customer service organisations operate.

The scale of this transformation is remarkable. By 2025, 95% of customer interactions are predicted to be handled by AI, with 75% of customer inquiries now being resolved by AI tools without human intervention. This isn’t about replacing human agents—it’s about empowering them to deliver more meaningful, high-value interactions.

Key Benefits Driving Customer Satisfaction

Enhanced Response Times and Availability

AI-powered systems deliver round-the-clock service that modern customers demand. 77% of customers expect to reach someone right away when they contact a company, and 90% say a quick response is critical, with 60% expecting “immediate” to mean within 10 minutes. AI chatbots and virtual assistants meet these expectations by providing instant responses to common queries, eliminating wait times for routine issues.

Personalised Customer Experiences

Modern AI systems leverage comprehensive customer data to deliver highly personalised service. 66% of global customer service managers who are optimising AI use generative AI to increase personalisation. By analysing purchase history, past interactions, and real-time context, AI enables agents to provide tailored solutions that feel genuinely human.

Improved First Call Resolution

First Call Resolution (FCR) rates are now expected to reach 80% or higher, a significant improvement from previous benchmarks. AI assists agents by providing real-time suggestions, retrieving relevant knowledge base articles, and surfacing customer history instantly, enabling them to resolve issues more effectively on the first contact.

Sentiment Analysis and Emotional Intelligence

Advanced AI systems now incorporate sentiment analysis to gauge customer emotions in real-time. When speech analytics are used, customer satisfaction increases as much as 10%, whilst operational costs can decrease by up to 30%. This emotional intelligence allows for proactive intervention when customers become frustrated, preventing escalation and improving overall satisfaction.

Best Practices for AI Implementation

Start with Clear Objectives

Successful AI implementation begins with defining specific goals. Whether aiming to reduce wait times, improve resolution rates, or enhance agent productivity, clear goals that align with your business’s needs are essential for measuring success and guiding technology choices.

Adopt a Human-AI Collaborative Approach

The most effective call centres don’t replace human agents with AI—they enhance them. AI is not replacing human agents but enhancing them, providing real-time support and context that allows agents to focus on complex issues requiring empathy and critical thinking.

Ensure Comprehensive Training and Change Management

75 percent of CX leaders believe the skillsets of support teams will look drastically different in three years as agents become AI managers. Investing in comprehensive training programmes ensures agents can effectively collaborate with AI systems and evolve into more strategic roles.

Prioritise Data Integration and Quality

AI systems are only as effective as the data they access. Unified customer data from multiple touchpoints enables AI to deliver personalised, contextually relevant responses. This integration allows different systems to communicate effectively, ensuring seamless customer experiences across all channels.

Measuring Success: Key Performance Indicators

Organisations implementing AI in call centres should track several critical metrics:

  • Customer Satisfaction (CSAT) scores: Modern AI tools can analyse 100% of conversations to provide comprehensive satisfaction metrics
  • First Call Resolution rates: Target improvement from 70% to 80%+ with AI assistance
  • Average Handle Time: Balance efficiency gains with quality service delivery
  • Agent satisfaction scores: Measure how AI tools impact employee experience
  • Cost per interaction: Track operational efficiency improvements

The Canadian Context

Canadian businesses are particularly well-positioned to leverage AI in call centres, with several leading companies already demonstrating best practices. Canadian organisations should consider partnering with local AI specialists who understand the regulatory environment and can provide culturally relevant implementations.

The technology infrastructure in Canada, combined with strong privacy frameworks, creates an ideal environment for responsible AI deployment in customer service settings.

Looking Ahead: The Future of AI-Enhanced Customer Service

The trajectory is clear: AI will continue evolving from a support tool to an integral component of customer service strategy. It is expected that by the end of 2025, 80% of companies will have adopted or are planning to adopt AI-powered chatbots, making this transformation inevitable rather than optional.

Future developments will likely include even more sophisticated natural language processing, predictive customer service that anticipates needs before they arise, and seamless integration across omni-channel experiences. The organisations that invest in AI capabilities now will be best positioned to meet rising customer expectations and maintain a competitive advantage.

Conclusion

The evidence is overwhelming: AI in call centres isn’t just improving operational metrics—it’s fundamentally enhancing customer satisfaction whilst empowering human agents to deliver more meaningful service. With 40% of companies worldwide using AI, and with 82% either using or exploring AI in their operations, the question isn’t whether to adopt AI, but how quickly and effectively organisations can implement these transformative technologies.

The companies that embrace AI-powered customer service today will set the standard for customer experience tomorrow. By focusing on human-AI collaboration, comprehensive training, and customer-centric implementation, organisations can harness the full potential of artificial intelligence to create truly exceptional customer service experiences.

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Beyond Prompt Engineering: Strengthening the Uniquely Human Skills AI Can’t Replace

(This post originally appeared on my LinkedIn Profile.)

As artificial intelligence transforms workplaces across every industry, the conversation often centres on technical preparedness: learning to write effective prompts, understanding AI tools, or adapting to new technologies. But there’s a more fundamental question we should be asking: What uniquely human capabilities should we be strengthening to remain indispensable in an AI-augmented workplace?

The answer isn’t about competing with AI—it’s about complementing it by doubling down on the skills that make us irreplaceably human.

The Context Revolution: Where Human Intelligence Shines

AI excels at processing information and recognising patterns, but it struggles with something humans do intuitively: understanding context that isn’t explicitly stated. When a team member says they’re “fine” with a decision. Still, their body language suggests otherwise. When a client’s straightforward request masks a more profound concern, or when market data fails to capture the cultural nuances affecting a product launch, these are moments where human insight becomes invaluable.

Strengthen your contextual intelligence by:

  • Practising active listening that goes beyond words to understand underlying concerns
  • Developing cultural competency and awareness of unspoken dynamics
  • Learning to read between the lines in communications and situations
  • Building your ability to synthesise information from multiple, often contradictory sources

Ethical Reasoning in Complex Situations

AI systems can be programmed with rules, but they struggle with the grey areas where ethical considerations intersect with practical realities. Human professionals who can navigate complex ethical dilemmas—balancing stakeholder interests, considering long-term implications, and making principled decisions under pressure—will become increasingly valuable.

Develop your ethical reasoning by:

  • Studying case studies in your industry where ethical considerations shaped business decisions
  • Engaging with diverse perspectives on moral and ethical issues
  • Practising scenario-based thinking about the potential consequences of decisions
  • Learning frameworks for ethical decision-making in professional contexts

Relationship Building and Trust Creation

Trust is built through consistent human interaction, emotional connection, and shared experiences. While AI can facilitate communication, the deep relationships that drive business success—whether with clients, team members, or partners—remain fundamentally human endeavours.

Strengthen your relationship-building skills by:

  • Developing genuine curiosity about others’ perspectives and experiences
  • Practising empathy and emotional intelligence in professional settings
  • Learning to build rapport across different communication styles and cultural backgrounds
  • Mastering the art of difficult conversations with sensitivity and tact

Creative Problem-Solving and Innovation

True innovation often emerges from connecting seemingly unrelated ideas, challenging fundamental assumptions, or approaching problems from entirely new angles. While AI can generate variations on existing themes, breakthrough thinking typically requires the kind of creative leaps that come from human intuition and imagination.

Enhance your creative problem-solving by:

  • Exposing yourself to disciplines outside your expertise area
  • Practising brainstorming techniques that encourage unconventional thinking
  • Learning to suspend judgment and explore “what if” scenarios
  • Developing comfort with ambiguity and uncertainty

Strategic Thinking and Long-Term Vision

AI is excellent at optimising for defined parameters, but setting those parameters—determining what success looks like in a complex, changing world—requires strategic thinking that considers multiple stakeholders, uncertain futures, and competing priorities.

Build your strategic capabilities by:

  • Studying how successful leaders have navigated industry transformations
  • Practising systems thinking that considers interconnected effects
  • Developing comfort with making decisions based on incomplete information
  • Learning to balance short-term pressures with long-term objectives

Adaptability and Continuous Learning

Perhaps most importantly, the ability to adapt quickly to new situations, learn from failures, and pivot when circumstances change is a fundamentally human trait. While AI systems require retraining to handle new situations, humans can draw on experience, intuition, and creativity to navigate uncharted territory.

Cultivate your adaptability by:

  • Embracing new challenges outside your comfort zone
  • Learning from setbacks and viewing failure as valuable data
  • Developing mental models that can be applied across different contexts
  • Staying curious about emerging trends and their potential implications

Leadership in Times of Change

As organisations implement AI, they’ll need leaders who can guide teams through transformation, address concerns about job security, and help people find meaning in their evolving roles. This requires emotional intelligence, practical communication skills, and the ability to inspire confidence during times of uncertainty.

Develop your leadership skills by:

  • Practising transparent communication about change and its implications
  • Learning to address fears and concerns with empathy and honesty
  • Building skills in change management and organisational development
  • Developing your ability to articulate vision and purpose

The Integration Imperative

The most successful professionals won’t be those who ignore AI or those who simply learn to use AI tools—they’ll be those who understand how to integrate AI capabilities with uniquely human strengths to create value that neither could generate alone.

This means developing what I call “integration intelligence”: the ability to understand what AI does well, what it doesn’t do well, and how human capabilities can best complement artificial ones. It’s about becoming the bridge between technological capability and human need.

Practical Steps to Start Today

  1. Audit your current role: Identify which tasks could potentially be automated and which require distinctly human judgment.
  2. Invest in relationships: Spend more time building genuine connections with colleagues, clients, and industry peers.
  3. Seek diverse experiences: Volunteer for cross-functional projects or take on challenges outside your usual scope.
  4. Practice difficult conversations: Develop your skills in navigating conflict, delivering difficult news, or facilitating challenging discussions.
  5. Study other industries: Look for insights and approaches from fields completely different from your own
  6. Embrace ambiguity: Take on projects where the path forward isn’t straightforward and success metrics are still being defined.

The Future Belongs to Human-AI Collaboration

The organisations that will thrive in the AI era won’t be those that replace humans with machines, but those that create powerful collaborations between human insight and artificial intelligence. By strengthening our uniquely human capabilities—our ability to understand context, navigate complexity, build relationships, think creatively, and lead through change—we position ourselves not as competitors to AI, but as essential partners in creating value that neither humans nor AI could achieve alone.

The question isn’t whether AI will change our work—it already is. The question is whether we’ll be ready to contribute our irreplaceable human value to that transformation. Now is the time to start strengthening those muscles.

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Keeping Humans in the Loop: A Strategic Approach to AI Implementation

(this post originally appeared on my LinkedIn Profile)

As organisations increasingly adopt artificial intelligence technologies, a critical question emerges: how do we harness AI’s power whilst maintaining essential human oversight and judgment? The answer lies in implementing robust human-in-the-loop (HITL) systems that combine the efficiency of automation with the nuanced decision-making capabilities that only humans possess.

Understanding Human-in-the-Loop Systems

Human-in-the-loop refers to AI systems designed to incorporate human judgment at critical decision points. Rather than fully automated processes, HITL systems pause at predetermined moments to seek human input, validation, or correction. This approach acknowledges that whilst AI excels at processing vast amounts of data and identifying patterns, humans remain superior at contextual interpretation, ethical reasoning, and handling edge cases.

The concept becomes particularly relevant in Canadian organisations navigating the proposed Artificial Intelligence and Data Act, which emphasises the importance of human oversight in high-impact AI systems.

Why Human Oversight Remains Essential

Contextual Understanding AI systems, despite their sophistication, often struggle with context that humans intuitively grasp. A customer service chatbot might misinterpret sarcasm or fail to recognise when a routine inquiry masks a serious complaint requiring immediate escalation. Human agents can read between the lines and understand the broader context of interactions.

Ethical Decision-Making Complex ethical dilemmas require human judgment that considers values, cultural nuances, and long-term consequences. When AI systems encounter scenarios involving fairness, privacy, or potential harm, human intervention ensures decisions align with organisational values and societal expectations.

Accountability and Trust Maintaining human involvement in AI decision-making processes supports accountability frameworks and builds public trust. Stakeholders are more comfortable with AI systems when they know qualified humans are monitoring and can intervene when necessary.

Continuous Learning and Improvement Human feedback serves as a crucial training mechanism for AI systems. When humans correct AI decisions or provide alternative solutions, this information can be fed back into the system to improve future performance.

Practical Strategies for Implementation

1. Tiered Decision Authority

Implement a hierarchical system where AI handles routine decisions independently but escalates complex cases to human reviewers. For example, in healthcare AI systems used by Canadian hospitals, routine diagnostic suggestions might proceed automatically, whilst unusual cases require physician review.

Implementation Framework:

  • Level 1: AI handles standard cases (80-90% of volume)
  • Level 2: AI flags uncertain cases for human review (5-15%)
  • Level 3: Complex cases requiring specialist human judgment (1-5%)

2. Active Learning Loops

Design systems that continuously learn from human corrections and feedback. This approach is particularly effective in knowledge management systems where subject matter experts can refine AI-generated content recommendations or search results.

3. Confidence Thresholds

Configure AI systems to request human input when their confidence levels fall below predetermined thresholds. This ensures that uncertain decisions receive appropriate human scrutiny whilst maintaining efficiency for high-confidence scenarios.

4. Collaborative Interfaces

Develop user interfaces that facilitate seamless collaboration between humans and AI. Rather than treating AI as a black box, create transparent systems that show their reasoning and allow humans to easily modify or override decisions.

Overcoming Implementation Challenges

Resource Allocation One of the primary challenges in HITL implementation is determining the appropriate level of human involvement. Too much human oversight defeats the purpose of automation, whilst too little risks poor outcomes. Organisations should start conservatively and gradually reduce human intervention as systems prove reliable.

Training and Skill Development Staff need training not just on how to use AI systems, but on how to effectively collaborate with them. This includes understanding AI capabilities and limitations, recognising when to intervene, and providing meaningful feedback for system improvement.

Change Management Some employees may view HITL systems with suspicion, seeing them either as job threats or as additional burdens. Clear communication about the benefits of human-AI collaboration and how it enhances rather than replaces human capabilities is essential.

Sector-Specific Considerations

Healthcare Canadian healthcare organisations implementing AI diagnostics must maintain physician oversight for critical decisions whilst leveraging AI for initial screening and triage. The College of Physicians and Surgeons’ guidelines emphasise that clinical judgment cannot be fully delegated to AI systems.

Financial Services In compliance with Canadian banking regulations, financial institutions using AI for lending decisions must ensure human reviewers can understand and explain automated decisions to customers and regulators.

Government Services Public sector AI implementations require particular attention to transparency and accountability. Citizens have the right to understand how AI systems affect decisions about their benefits, services, or regulatory compliance.

Building Effective Human-AI Teams

The most successful HITL implementations treat humans and AI as complementary team members rather than competitors. This requires:

Clear Role Definition Establish clear boundaries between what AI handles independently and what requires human input. These boundaries should be regularly reviewed and adjusted based on system performance and organisational needs.

Feedback Mechanisms Create structured processes for humans to provide feedback on AI decisions, including both corrections and confirmations of good decisions. This data becomes valuable for system improvement.

Performance Monitoring Implement metrics that measure not just AI accuracy but also the effectiveness of human-AI collaboration. Track how often human interventions improve outcomes and use this data to optimise the balance.

Future-Proofing Human-AI Collaboration

As AI capabilities continue to evolve, HITL systems must be designed for adaptability. What requires human oversight today might be handled autonomously tomorrow, whilst new capabilities may create new needs for human involvement.

Organisations should regularly reassess their HITL configurations, staying informed about advances in AI technology whilst maintaining focus on their core mission and values. The goal isn’t to eliminate human involvement but to ensure it remains meaningful and value-adding.

Conclusion

Keeping humans in the loop isn’t about limiting AI’s potential—it’s about maximising the combined potential of human intelligence and artificial intelligence. By thoughtfully designing HITL systems, organisations can harness AI’s efficiency and analytical power whilst preserving the judgment, creativity, and ethical reasoning that humans bring to complex decisions.

The most successful organisations of the future will be those that master this collaboration, creating systems where humans and AI work together seamlessly to achieve outcomes neither could accomplish alone. In the Canadian context, with increasing regulatory attention to AI governance, human-in-the-loop approaches aren’t just good practice—they’re becoming essential for compliance and public trust.

As knowledge managers and organisational leaders navigate this landscape, the question isn’t whether to keep humans involved in AI systems, but how to do so most effectively. The answer lies in thoughtful design, continuous learning, and a commitment to maintaining the human elements that make organisations truly intelligent.

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AI-Ready

(this post originally appeared on my LinkedIn profile and has been edited slightly from the original)

I’ve been talking to many people about the importance of keeping humans in the loop and preparing organisations to be AI-ready. With that in mind, I thought I might do a short series on those themes. Today marks the first in a series focused on helping organisations become AI-ready. This has been a theme at the conferences I’ve attended over the last couple of years, so I’m sure many of you have thoughts about this necessity. Feel free to share your thoughts in the comments.

Knowledge managers play a crucial role in preparing organisations for AI adoption. Here are the key areas they need to focus on:

Data Governance and Quality Establish robust data governance frameworks that ensure information is accurate, consistent, and accessible. AI systems are only as good as the data they’re trained on, so knowledge managers must audit existing knowledge repositories, standardise data formats, and implement quality control processes. This includes creating metadata schemas and ensuring compliance with Canadian privacy legislation like PIPEDA.

Knowledge Audit and Documentation Conduct comprehensive audits of organisational knowledge assets – both explicit (documented) and tacit (experiential) knowledge. Map knowledge flows, identify critical knowledge gaps, and document processes that have previously relied on institutional memory. This foundation is essential for training AI systems effectively.

Change Management and Skills Development: Develop change management strategies that address employee concerns about AI, while building digital literacy. Knowledge managers should collaborate with HR and training departments to create programmes that help staff understand how AI will augment rather than replace their work. Focus on developing human skills that complement AI capabilities.

Technology Infrastructure Assessment: Evaluate current knowledge management systems and determine what upgrades or integrations are needed to support AI tools. This might involve migrating to cloud-based platforms, improving search capabilities, or ensuring systems can integrate with AI applications through APIs.

Ethical Guidelines and Bias Mitigation: Establish clear guidelines for ethical AI use within the organisation. Knowledge managers should work with legal teams to develop policies around AI transparency, accountability, and bias prevention. This is particularly important in Canadian organisations operating under evolving AI governance frameworks.

Pilot Programmes and Gradual Implementation Start with small-scale AI implementations in specific knowledge domains before organisation-wide deployment. This allows for testing, learning, and refinement while building internal confidence and expertise.

Cross-Functional Collaboration: Foster partnerships between knowledge management, IT, legal, and business units to ensure AI initiatives align with organisational objectives and compliance requirements.

The key is taking a strategic, human-centred approach that treats AI as a tool to enhance organisational knowledge capabilities rather than simply a technological upgrade.