Tag: governance

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Turning ROT into ART: Why Humans and Governance Are Your AI Readiness Foundation

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

We talk obsessively about AI readiness. Plug in the right tools, train the model, scale the adoption. But I keep hearing the same quiet concern in rooms full of knowledge and learning leaders: What happens when we feed the beast garbage?

The garbage has a name: ROT. Redundant, obsolete, trivial content. And if you’re serious about AI readiness—or frankly, about knowledge management at all—you need to stop treating ROT disposal as a nice-to-have technical task. It’s a governance problem. It’s a people problem. And it’s non-negotiable.

The ROT Problem Is Your AI Problem

Here’s the uncomfortable truth: the organisations struggling most with AI implementation are the ones drowning in ROT.

Redundant documents that say the same thing three different ways. Obsolete processes locked in PDFs from 2015. Trivial information cluttering search results so badly that finding what you actually need takes longer than just asking a colleague. Add multiple systems, no single source of truth, and inconsistent version control, and you’ve created a perfect petri dish for training data that’s incomplete, contradictory, or simply wrong.

Now feed that into an AI system. The model doesn’t know intent. It doesn’t know that the 2015 process was replaced by something newer. It can’t distinguish between the authoritative procedure and the three near-duplicates authored by well-meaning teams in different divisions. It learns from what you give it. Garbage in, garbage out—but faster, at scale, and with the veneer of algorithmic confidence.

That’s not an AI problem. That’s a governance problem. Which is a people problem.

Enter ART: Accurate, Reliable, Trustworthy

The antidote to ROT is ART: content that is accurate, reliable, and trustworthy.

Accurate means it reflects reality as it currently stands. The process described is the process actually followed. The policy hasn’t shifted. The template is current. Accuracy requires human judgment—someone who understands the business has to say, “This is true today.”

Reliable means it’s consistent and findable. Identical information doesn’t exist in five formats. Metadata is correct. Version history is transparent. There’s a clear path to the current, authoritative version. Reliability requires governance: standards, ownership, maintenance schedules.

Trustworthy means people believe it enough to act on it. That only happens when they’ve seen it stay accurate and reliable over time. Trustworthiness is built through culture—through demonstrating that the knowledge function takes stewardship seriously.

None of these are technical properties. They’re human commitments, enforced through governance structures.

The Governance Gap

Here’s where most organisations trip. They treat quality as a technical project: cleansing tools, metadata standards, taxonomy implementation. These are important. But without governance, they’re cosmetic.

Governance asks the hard questions:

  • Who owns this content? (Not who wrote it. Who’s responsible for keeping it true?)
  • Who decides what’s redundant, and by what criteria? (Delete the 2015 process, but who makes that call, and what if someone was relying on it for context?)
  • When does content become obsolete, and who’s checking? (That vendor partnership doc from 2012—is it history or a hidden liability?)
  • What happens when we find contradictions? (Two teams think they own the same process. Now what?)
  • Who’s accountable for trustworthiness? (Not a system. A person or team.)

Governance without people is theatre. But people without governance quickly become overwhelmed, inconsistent, or burnt out. You need both.

Keeping Humans in the Loop

This is where AI readiness becomes interesting. The temptation is to assume AI can automate the governance problem away—classifiers that identify ROT automatically, algorithms that spot inconsistencies, systems that flag obsolete content.

Some of that is useful. But it’s not sufficient. And if you try to make it sufficient, you’re back where you started: garbage in, garbage out.

Humans stay in the loop at the critical junctures:

Defining what matters. What counts as redundant rather than useful context? What’s a necessary variant rather than an inconsistency? These are judgement calls. Tools can flag; humans decide.

Ownership and accountability. An algorithm can tell you content needs updating. A human has to take responsibility for updating it, and for saying “I’ve reviewed this and it’s true.”

Interpretive consistency. Policies that sound the same might apply differently in different contexts. Humans who understand the business catch that. Algorithms amplify the confusion.

Escalation and exceptions. ROT-clearing sometimes reveals gaps or conflicts in your business logic, not just your documentation. Those need human attention, not automated resolution.

The goal isn’t to remove humans from governance. It’s to remove humans from the repetitive, low-judgment parts so they can focus on the parts that require discretion, accountability, and real understanding.

The Real ROI

Organisations that have tackled ROT properly—through committed governance, clear ownership, and sustained human attention—report something interesting: it’s not just cleaner data. They report faster decision-making, fewer duplicated efforts, lower training time for new staff, and better cross-team collaboration.

And when they do deploy AI, it works. Not because the tool is magic, but because the foundation is solid.

The quiet concern I keep hearing? It’s legitimate. You can’t automate your way out of a governance problem. But you can solve it. It just requires treating knowledge stewardship as seriously as you treat technology adoption.

That’s the difference between readiness and recklessness.

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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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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.