For the first time in the history of enterprise technology, the people using the technology know more about its potential than the people buying it.

Let that sink in for a moment. Because it inverts everything we know about organizational change management - and it's why your traditional approach to building a Centre of Excellence will fail when it comes to AI.

The ChatGPT Moment

Dr. Debbie Qaqish, in her white paper on AI Centres of Excellence (2024), captures this perfectly. She describes watching every major tech evolution of the past four decades - from rotary phones to smartphones, from dial-up internet to cloud computing, from on-premise servers to SaaS platforms. Nothing, she says, was as earth-shaking as the release of ChatGPT on November 30, 2022.

Why? Because every previous technology came with a predictable evolution path. You could see where it was going. You could plan for it. You could reasonably accurately define use cases upfront and execute against them.

AI shatters that predictability. We are in unknown territory. And that changes everything about how organisations must adapt.

How We've Always Done Tech Implementation

Let me show you what I mean with a concrete example.

Think about a CRM rollout in the 2010s - let's say Salesforce:

• Leadership identifies the problem: "Our sales pipeline visibility is terrible; deals are falling through cracks"
• Leadership selects the solution: They evaluate vendors and choose Salesforce
• Leadership defines the use cases: Lead tracking, opportunity management, forecasting reports - all documented upfront in requirements
• Workers execute the plan: Sales reps get trained on defined fields, follow mandatory processes, use standardized reports
• Knowledge flows DOWN: "Here's how you'll use it, here's the dashboard you'll look at, here's the fields you'll fill in"

The Centre of Excellence's role in this world? Implementation, training, and optimisation of those predetermined use cases.

This model worked beautifully for decades. The technology was stable. The use cases were knowable. The path was clear.

Enter AI - And Everything Breaks

Now let me show you what's actually happening with AI in organisations today.

I recently worked with a European Customer Support team on AI integration. Here's what we discovered:

Support agents started using AI to draft responses. Nothing revolutionary there - that was the planned use case. But then something interesting happened. Agents began noticing that the AI was identifying sentiment patterns they had never formally tracked. One agent said, "Wait - this AI noticed that customers who use certain phrases are actually asking about X, not Y."

Then they discovered the AI could predict escalation risk based on subtle language cues that nobody had ever documented. These weren't use cases we planned for. These were discoveries made by front-line workers experimenting with the technology.

The knowledge didn't flow down. It flowed up.

The AI CoE's role became capturing these emergent insights and scaling them across teams. Not training people on predetermined workflows but harvesting what workers discovered about AI's capabilities.

The Tacit Knowledge Goldmine

But here's where it gets really interesting - where AI and knowledge management converge in a way that's never been possible before.

Consider financial advisors. I recently delivered a customised program for  an Insurance client, working with their team of several advisors nationwide. These senior advisors hold extraordinary tacit knowledge - the kind that traditional technology could never capture:

Pattern Recognition: "I can tell from a conversation if someone's underinsured." That's not in any manual. That's 20 years of experience reading between the lines.

Client Psychology: "How to explain complex coverage in simple terms. When to push and when to back off. How to have difficult conversations about underinsurance." No CRM workflow can teach this. It's intuitive, contextual emotional intelligence built over thousands of client interactions.

Local/Regional Expertise: Understanding flood zones, weather patterns, crime rates, local business ecosystems, community relationships and networks. This is place-based tacit knowledge that exists in advisors' heads, not in databases.

Claims Wisdom: How to guide clients through claims processes, what to document at the scene, how to advocate for clients with claims teams. Real-world responses to "that's too expensive." How to explain the value of coverage.

Creative Problem-Solving: Which products naturally go together, how to package solutions for different life stages, creative solutions for unique client situations. Each client is different. Senior advisors have a mental library of "I once had a client who..." scenarios that saved the day.

Underwriting Judgment: When to escalate a risk versus handle it, how to present a borderline risk to underwriters, what information underwriters really need.

The traditional tech approach would have built workflows for standard cases, created dropdown menus for common scenarios, documented "best practices" in a manual nobody reads - and missed 80% of the actual value in those advisors' heads.

But here's what we discovered with AI:

When advisors start experimenting with AI in Communities of Practice, something remarkable can happen. The AI could help them articulate their tacit knowledge. One veteran advisor would be able to say: "The AI just explained the pattern I've been following unconsciously for 15 years. I never knew how to teach this to newer advisors, but now I can see it."

AI becomes the externalisation engine - converting "I just know" into "Here's why I know."

And the AI CoE's role in this brave new world? Systematically capturing these discoveries flowing UP from practitioners and scaling them across all the many advisors.

This Is Pure SECI in Action

If you're familiar with knowledge management theory, you'll recognize Nonaka's SECI model at work:

• Socialisation: Practitioners in Communities of Practice sharing "hey, I tried this with AI and it worked"
• Externalisation: The CoE capturing those tacit discoveries and converting them into documented use cases
• Combination: The CoE synthesising patterns across experiments into frameworks and best practices
• Internalisation: Organisation-wide learning and capability building

The AI Centre of Excellence becomes the knowledge conversion engine - transforming frontline tacit knowledge about AI's emergent capabilities into organisational strategic advantage.

This has never been possible before. Traditional technology couldn't access tacit knowledge. It could only automate explicit processes. AI can help surface, articulate, and scale what people know but couldn't explain.

Why AI CoEs Are Completely Different

Dr. Qaqish identifies three key differences that make AI Centres of Excellence unlike any CoE you've built before:

1. Continuous big changes vs. step-chain improvement

Traditional tech followed a "pilot, test, deploy, optimise" model. You implemented once, then made incremental improvements. AI doesn't work that way. It requires ongoing adaptation to rapid, sometimes disruptive changes. Your CoE isn't optimising a stable platform - it's managing continuous experimentation and change.

2. Bottom-up vs. top-down

This is the game-changer. Because nobody can predict AI's evolution, initiatives must come from hands-on users experimenting and learning, not from leadership defining use cases upfront. The insights flow up from practitioners, not down from executives.

This inverts traditional change management. Your workers know more about AI's potential applications than your leadership does. The CoE's job is to harvest that knowledge and convert it into organisational capability.

3. Requires more leadership, resourcing, and budget

Unlike other technology CoEs that could operate as "nice to have" side projects staffed by people in their free time, the AI CoE needs dedicated time, real budget, executive clout, new incentives, and structured support.

Why? Because this isn't about implementing a predetermined solution. It's about creating an organisational learning system that can adapt at the speed of AI evolution.

The Two Functions Your AI CoE Must Integrate

Some frameworks separate the AI Council (governance, risk, compliance) from the AI Centre of Excellence (innovation, experimentation, capability building). I've found this creates unnecessary friction and slows everything down.

Your AI CoE needs to integrate both functions:

Governance Function: Policy development, risk assessment, ethical frameworks, compliance. The "don't screw up" guardrails.

Innovation Function: Managed experimentation, capability building, training, best practices. The "make cool stuff happen" engine.

Why keep them together? Because effective experimentation requires governance guardrails. You can't separate "try new things" from "do it safely" without creating either chaos or paralysis. One integrated team moves faster than two teams coordinating.

What This Means For Your Organization

The implications are profound:

Traditional tech CoE role: Train people to use the platform as designed.

 AI CoE role: Harvest what people discover about AI's capabilities and convert it into strategic advantage

Traditional knowledge flow: Leadership → "Here's the system" → Workers use it

AI knowledge flow: Workers → "Here's what we discovered" → CoE → Organisational transformation

Traditional CoE success metric: Adoption rates, process compliance, efficiency gains

AI CoE success metric: Rate of knowledge capture, speed of capability scaling, tacit knowledge externalisation

Companies that treat their AI CoE like a traditional implementation team will lose to companies that treat it like a knowledge creation system.

Getting Started

If you're building or reimagining your AI Centre of Excellence, here's where to focus:

1. Establish Communities of Practice - Create structured spaces for hands-on workers to experiment and share discoveries. This is your knowledge generation engine.

2. Build knowledge capture systems - Don't just let experiments happen. Systematically document what's being learned, especially tacit knowledge that AI helps surface.

3. Ensure executive clout - Your CoE leader needs power to move quickly on discoveries. When front-line workers find a game-changing application, you need to scale it fast.

4. Resource it properly - This isn't a side project. People need dedicated time to experiment, reflect, and collaborate. Budget for tools, training, and incentives.

5. Integrate governance and innovation - Don't separate them. Build one CoE that can experiment safely and scale learnings responsibly.

The Bottom Line

For the first time in enterprise technology history, the knowledge about what's possible flows from the bottom up, not the top down. Your front-line workers, experimenting with AI in their daily work, are discovering capabilities and applications that leadership couldn't have predicted.

The AI Centre of Excellence isn't about deploying technology. It's about harvesting tacit knowledge, converting discoveries into capabilities, and building organisational learning systems that can adapt at the speed of AI evolution.

This is where AI and knowledge management meet. And it changes everything about how we think about Centres of Excellence.

The question isn't whether to build an AI CoE. The question is: Are you building a traditional implementation team or a knowledge conversion engine?

Because only one of those will succeed in the AI era.

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For years, Knowledge Management has struggled with the same uncomfortable truths:

• Portals are full, yet people can’t find what they need
• Users hesitate because of confidentiality risks
• Tagging feels like extra work
• Lessons learned vanish after projects close
• Adoption depends more on habit than value

AI changes this—but not by replacing KM teams or flooding systems with automation. The power of AI in KM lies in enabling trust, discovery, and participation without requiring additional effort from people.

1. Confidentiality & Intelligent Access Control

One of the biggest unspoken barriers to knowledge sharing is fear: “What if I upload something sensitive?”

AI can act as the first line of governance, not the last, because Knowledge Managers need to be the final gatekeepers.

By training internal AI models on organizational policies, restricted terms, client names, deal markers, and IP indicators, AI can:

• Scan content at the point of upload
• Flag sensitive data automatically
• Recommend the right confidentiality level (Public / Internal / Restricted)
• Suggest the correct library and access group

Instead of relying on contributors to interpret complex policies, AI guides them safely.

Outcome:

• Reduced governance risk
• Increased confidence to share
• Faster publishing without manual review bottlenecks

2. Intelligent Auto-Tagging That Actually Works

Manual tagging has always been KM’s weakest link—not because people don’t care, but because context is hard to judge while uploading. Additionally, people often follow their own tagging, making content discoverability a tedious cleanup task for knowledge managers.

AI solves this by:

• Understanding the meaning of the content, not just keywords
• Applying standardized taxonomy automatically
• Adding contextual metadata such as:
  • Practice / capability
  • Industry
  • Use-case type
  • Maturity level

The result is consistent, high-quality metadata—making content discovery intuitive.

‍3. AI as a Knowledge Guide, Not a Search Box

Most users don’t struggle because content doesn’t exist—they struggle because they don’t know what to ask for.

AI transforms KM search into a guided experience.

Instead of returning documents, AI can:

• Understand intent
• Surface relevant snippets
• Suggest related assets
• Answer questions conversationally

Example:

“Show me CX transformation pitch assets for BFSI deals under $5M.”

AI pulls together slides, case snippets, and key insights—without forcing users to open ten files.

‍4. AI-Captured Lessons Learned (Without Extra Meetings)

Lessons learned often disappear because capturing them feels like another task.

AI removes this friction by capturing knowledge where it already exists:

• Project retrospectives
• Meeting transcripts
• Collaboration tools

AI then converts this into:

• Key insights
• What worked / what didn’t
• Reusable recommendations

Presented as:

• Short summaries
• Role-based insights
• “Use this when…” prompts

Knowledge becomes actionable, not archival.

5. AI-Powered Motivation Through Micro-Content

KM adoption doesn’t improve through reminders—it improves through recognition and relevance.

AI can:

• Convert long documents into:
  • 30-second explainer videos
  • Knowledge cards
  • Carousel-ready visuals
• Highlight real impact:
  • “Your asset was reused in 3 proposals”
  • “Your insight supported a winning deal”

When contributors see their knowledge being used, motivation becomes organic.

A Simple AI-Enabled KM Workflow

Create Content

↓

AI Scans & Classifies

↓

Auto-Tagging & Security Assignment

↓

Contextual Discovery via AI Assistant

↓

Reuse, Insights & Impact Visibility

This is not about more content—it’s about better, safer, usable knowledge.

KM no longer needs more portals, folders, or documents. It needs intelligence layered over content with easy connections to content and skill owners.

AI allows us to:

• Reduce fear of sharing
• Improve discovery without extra effort
• Capture tacit knowledge naturally
• Reward contribution visibly
• Make a connection with SME easily

Knowledge is no longer something we store. It’s something we activate.

____________________________________________________________________________

Artificial Intelligence is reshaping knowledge management (KM) — accelerating content harvesting, analysis, and distribution. But with speed comes risk: content security and governance are now the critical gatekeepers ensuring that knowledge remains an asset, not a liability.

Content Governance as the Gatekeeper

In today’s AI‑driven KM landscape, governance is not optional. It ensures:

• Confidential content is protected from misuse.
• Licensed subscriptions are used within authorized terms.
• Teams understand content provenance — where information comes from and how it can be used.
• Privacy and confidentiality clauses are embedded into workflows.

Case in Point

• Publishing Industry: AI tools can summarize subscription‑based journals. Without governance, this risks violating licensing agreements.
• Financial Services: AI can analyze confidential reports. KM must ensure outputs don’t leak sensitive data.
• Healthcare: AI may harvest patient data for insights. Governance ensures compliance with HIPAA/GDPR and ethical boundaries.

The AI Factor

AI magnifies both opportunity and risk:

• Training AI responsibly: KM must ensure AI learns only from approved, non‑confidential datasets.
• Monitoring outputs: AI can unintentionally breach usage terms; KM must act as the final gatekeeper.
• Bias & compliance checks: Governance frameworks must include regular audits to align AI outputs with ethics and law.

5‑Point Checklist for KM Teams

1. Define clear policies for external content usage and subscription terms.
2. Embed confidentiality protocols into AI workflows and team practices.
3. Audit regularly — review AI outputs and content flows for compliance.
4. Educate teams on provenance, privacy, and responsible AI use.
5. Act as final gatekeeper — KM validates that AI‑generated knowledge is secure, ethical, and aligned with organizational values.

Without strong governance, KM repositories can become vulnerable. Knowledge managers must embrace their evolving role as custodians of trust — training AI responsibly, gatekeeping outputs, and ensuring that knowledge flows are secure, ethical, and strategically valuable.

_______________________

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AI-driven knowledge management uses artificial intelligence to capture, organize, and apply knowledge at scale—fundamentally changing how organizations create value and how knowledge workers contribute.
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Introduction

Modern organizations generate more data and content than ever before, yet employees still struggle to find accurate, relevant, and trustworthy knowledge when they need it. Documents live across intranets, cloud drives, chat tools, and emails, creating fragmentation instead of clarity. Traditional knowledge management (KM) systems rely heavily on manual documentation, static repositories, and personal discipline, which makes them difficult to scale and sustain.

AI-driven knowledge management introduces intelligence directly into how knowledge is captured, structured, and reused. Instead of asking employees to “manage knowledge,” AI embeds KM into daily work. This shift is not just transforming systems—it is redefining the role of knowledge workers themselves, moving them toward higher-value, decision-focused work.
(Related internal reading: AI in Digital Transformation Strategy)

What Is AI-Driven Knowledge Management?

AI-driven knowledge management refers to the use of artificial intelligence technologies to support and automate the entire knowledge lifecycle—creation, capture, organization, sharing, and reuse—across an organization.

Unlike traditional KM, which depends on predefined taxonomies and manual tagging, AI-driven KM systems learn continuously from content, context, and user behavior. They improve over time, delivering more relevant knowledge with less effort from employees.

Key enabling technologies include:

• Machine learning, which improves relevance based on usage patterns
• Natural language processing (NLP), which understands meaning and intent in text and speech
• Generative AI, which summarizes, connects, and explains information
• Speech and audio AI, including voiceover AI, which enables spoken knowledge capture and delivery

According to IBM Research, AI-based knowledge systems significantly improve information retrieval accuracy by focusing on meaning rather than keywords.

Echo Block — Section Takeaway
AI-driven knowledge management uses intelligent technologies to automate and improve how knowledge is captured, organized, and applied across the organization.

Why Traditional Knowledge Management Struggles Today

Most KM initiatives fail not because knowledge is missing, but because it is difficult to find, trust, or reuse.

Common challenges include:

• Employees spending excessive time searching for information
• Duplicate, outdated, or conflicting content across systems
• Loss of tacit knowledge when experienced employees leave
• Knowledge documentation viewed as “extra work”

As organizations become more digital, remote, and fast-moving, these problems intensify. A study by McKinsey found that knowledge workers spend nearly 20% of their time searching for information (McKinsey Global Institute).

AI-driven KM reduces friction by embedding knowledge directly into workflows, rather than relying on separate repositories.
(Related internal reading: Why Knowledge Management Initiatives Fail)

Echo Block — Section Takeaway
Traditional KM does not scale well; AI-driven KM reduces friction by integrating knowledge into everyday work.

How AI Changes the Knowledge Management Lifecycle

AI-driven KM reshapes every stage of the knowledge lifecycle, from capture to reuse.

Knowledge Creation and Capture

Traditional KM expects employees to manually document what they know. AI shifts this by capturing knowledge automatically as work happens.

Examples include:

• Transcribing meetings and extracting key decisions
• Analyzing collaboration tools for emerging insights
• Using voiceover AI to record spoken explanations from experts and convert them into searchable assets

This approach preserves tacit knowledge while reducing administrative burden. Research from Gartner highlights that automated knowledge capture significantly improves KM adoption rates.

Echo Block — Section Takeaway
AI captures knowledge as a byproduct of work, making KM easier and more sustainable.

Knowledge Organization and Structure

Manual taxonomies are expensive to maintain and quickly become outdated. AI-driven KM organizes knowledge based on meaning rather than rigid categories.

This enables:

• Semantic clustering of related content
• Automatic updates as language and topics evolve
• Improved cross-functional visibility

Knowledge structures adapt dynamically as the organization changes.
(Related internal reading: Semantic Search vs Keyword Search)

Echo Block — Section Takeaway
AI replaces static taxonomies with adaptive, meaning-based knowledge organization.

Knowledge Retrieval and Application

The true value of KM lies in delivering the right knowledge at the right time. AI improves retrieval by understanding user intent and work context.

Key capabilities include:

• Natural-language search instead of keyword matching
• Proactive recommendations based on role and task
• Voice-enabled access using voiceover AI for hands-free environments

According to Microsoft Research, contextual AI search reduces task completion time in knowledge work by over 30%.

Echo Block — Section Takeaway
AI-driven KM delivers relevant knowledge in context, not just on request.

The Role of Voiceover AI in Knowledge Management

Voiceover AI expands how knowledge is created, accessed, and shared—especially in mobile and knowledge-intensive environments.

What Is Voiceover AI in KM?

Voiceover AI refers to AI systems that generate, process, or deliver spoken content. In KM, this allows organizations to treat spoken knowledge as a first-class asset.

Key applications include:

• Capturing expert insights through short audio explanations
• Delivering audio summaries of complex documents
• Supporting multilingual and inclusive knowledge access

This is especially valuable in frontline, field-based, or accessibility-focused environments.
(Related internal reading: Audio-First Knowledge Sharing Models)

Echo Block — Section Takeaway
Voiceover AI extends KM beyond text, making knowledge more accessible, inclusive, and reusable.

How AI-Driven KM Changes the Role of Knowledge Workers

AI does not replace knowledge workers—it reshapes how they create value.

From Knowledge Holders to Knowledge Stewards

When AI handles storage and retrieval, knowledge workers focus on:

• Validating accuracy and relevance
• Providing context and judgment
• Ensuring ethical and responsible use of knowledge

Their role shifts from control to stewardship. This aligns with modern KM frameworks promoted by organizations like the Knowledge Management Institute (KM Institute).

Echo Block — Section Takeaway
Knowledge workers move from owning information to stewarding meaning and quality.

From Content Producers to Sense-Makers

Generative AI can create drafts and summaries, but it lacks organizational context.

Knowledge workers increasingly:

• Interpret AI-generated outputs
• Connect insights across domains
• Translate knowledge into decisions and action

This supports knowledge-enabled decision-making rather than content volume.

Echo Block — Section Takeaway
AI generates content; knowledge workers provide interpretation and insight.

From Searchers to Strategic Contributors

By reducing time spent searching, AI-driven KM enables knowledge workers to focus on:

• Problem-solving
• Innovation
• Collaboration

Productivity shifts from output quantity to business impact.
(Related internal reading)

Echo Block — Section Takeaway
AI frees knowledge workers to focus on higher-value, strategic work.

Organizational Benefits of AI-Driven Knowledge Management

When aligned with strategy, AI-driven KM delivers measurable benefits:

• Faster and more consistent decision-making
• Reduced knowledge loss from employee turnover
• Improved onboarding and continuous learning
• Stronger collaboration across silos

McKinsey research shows that AI can significantly reduce time spent processing information in knowledge-intensive roles.

Echo Block — Section Takeaway
AI-driven KM improves speed, resilience, and organizational learning.

‍

Governance and Risk Considerations

AI-driven KM introduces new responsibilities alongside its benefits.

Common risks include:

• Bias in AI-generated insights
• Over-reliance on automated outputs
• Data privacy and trust concerns

Strong governance, transparency, and human oversight are essential. MIT Sloan emphasizes that responsible AI governance is critical for long-term value creation.

Echo Block — Section Takeaway
Effective governance is critical to building trust in AI-driven KM systems.

Frequently Asked Questions

What makes AI-driven knowledge management different from traditional KM?

AI-driven KM automates capture, organization, and retrieval using intelligent systems rather than manual processes.

Echo Block — FAQ Takeaway
AI-driven KM replaces manual effort with adaptive intelligence.

Does AI replace knowledge workers?

No. AI changes their role by handling routine tasks while humans focus on judgment, ethics, and strategy.

Echo Block — FAQ Takeaway
AI augments knowledge workers rather than replacing them.

How does voiceover AI support knowledge management?

Voiceover AI enables spoken knowledge capture and audio-based access, improving speed and inclusivity.

Echo Block — FAQ Takeaway
Voiceover AI expands KM into audio-first knowledge sharing.

Is AI-driven KM suitable for all organizations?

It is most effective in knowledge-intensive environments and should align with organizational maturity and culture.

Echo Block — FAQ Takeaway
AI-driven KM works best when matched to organizational readiness.

Conclusion: The Future of Knowledge Work Is Augmented

AI-driven knowledge management represents a shift from managing information to enabling understanding. By integrating technologies such as voiceover AI, organizations make knowledge more dynamic, accessible, and embedded in daily work. For knowledge workers, the future is not about competing with AI—it is about using it to amplify human judgment, learning, and impact.

Final Echo Block — Executive Summary
AI-driven knowledge management transforms KM into intelligent enablement, redefining knowledge workers as stewards, sense-makers, and strategic contributors.

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Google Cloud CEO Thomas Kurian and Replit CEO Amjad Masad just dropped a partnership that changes everything about who gets to build software in your organization.

The goal? "Make enterprise vibe-coding a thing” says Masad. And the implications are massive.

‍
The New Reality

"Instead of people working in silos, designers only doing design, product managers only write...now anyone in the company can be entrepreneurial “ Masad explains.

Translation: Your HR team can build their own tools. Your salespeople can create custom dashboards. Your marketing folks can prototype their own automation.

No tickets. No backlogs. No "waiting for dev."

Why This Matters for KM

This is where knowledge management meets its inflection point. When vibe coding democratises software creation, you're not just automating tasks—you're enabling people to externalise their tacit knowledge directly into functioning systems.

Think about the SECI model. The salesperson who knows the perfect qualification workflow can now build it themselves. The customer service rep with deep process knowledge can create the tool that captures it.

Knowledge doesn't get stuck in someone's head or lost in a ticket queue. It becomes software.

The AI Centre of Excellence Play

But here's the critical piece most organisations will miss -  Democratisation without Orchestration is chaos.

This is where an AI Centre of Excellence becomes essential. You need a hub that:

•Curates the best vibe-coded solutions across the organization

•Shares proven patterns and successful apps

•Ensures governance without killing innovation

•Transforms individual experiments into organizational assets

•Replit grew from $2.8 million to $150 million in revenue in under a year. The enterprise is ready. But without a CoE, you'll have 1,000 isolated solutions instead of 10 transformative ones.

NB: We’re currently seeing AI COE’s running at 20% of our CAIM students to date. I predict that number will easily go north of 50% this time next year.  (see: sample job examples below) 

The Certified AI Manager Connection

This is exactly what we demonstrate in the Certified AI Manager Course —using Claude to vibe code business solutions with human centric KM at the centre.

P.S. or Footnote:  When you start to realize that this phase of AI actually eats software, the $3 billion valuation of Replit and Cursor's $29.3 billion valuation don't seem so crazy after all. And when you consider Anthropic's Claude Code hit $1 billion in run-rate revenue —the very tool powering much of this vibe coding revolution—you start to see we're not just witnessing a shift in how software gets built. We're watching software consumption replace software purchase. They're not just selling tools—they're selling the dissolution of the software industry as we knew it.

Knowledge Management Roles within AI Centre of Excellence Contexts

Knowledge Management & Leadership Roles in the AI Centre of Excellence