From Geek to Star #43 - Enabling AI in your organisation as a Tech Leader (1/3)

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🗓️ This Week – Episode 43: Enabling AI in your organisation as a Tech Leader

On April 7th 2026, Anthropic announced Claude Mythos Preview and also announced at the same time it would not be made available for general release. Anthropic's red team published an analysis of the Mythos LLM explaining how advanced the model had become, founding zero-day vulnerabilities in every major operating system and every major web browser. Fully autonomously. No human guidance needed. Instead of releasing this version, Anthropic announced the launch of Project Glasswing, “a new initiative that brings together Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks in an effort to secure the world’s most critical software”.

For all of us working in tech, using AI in our work daily, for some generating entire codebase entirely regularly, I believe we all see and feel the immense power that AI can unleash. And the consequences it can bring negatively indeed in cybersecurity, but also in terms of impacts on jobs, on society. For me, it seems sometimes (often) overwhelming. 

At a company level, what can “Enabling with AI” mean when there is so much potential power together with so many risks? How to amplify the positive potential of AI without releasing the associated potential hell? In my current thoughts, I see AI enablement for an organisation around 3 dimensions: 

1. AI augmentation of people and teams across functions: how to augment people across all functions, not just tech, thanks to AI. Not only at an individual level but also at a team collaboration level. 

2. AI-driven software engineering: how to fundamentally change software engineering practices with the usage of AI, up to potentially revising the sourcing strategy of the company between internal and outsourcing capabilities. 

3. AI-augmented cybersecurity: how to integrate AI in the cybersecurity operating model of the company in face of the exponential threats brought by AI on the other side, but also to manage the acceleration of digital assets in the company due to dimensions 1/ and 2/ above.

In this newsletter, I will share my thoughts on Dimension 1: AI augmentation of people and teams across functions.

AI enablement of business people: redefining the “definition of done” for tech teams

Deploying AI in an organisation is not just about selecting the right LLM, integrating it into your infrastructure, and making it available to business users. Yet, this is where many organisations stop. This is not new. I have seen this pattern for years: IT / tech teams deploy a tool, communicate about it… and consider the job “done”.

The rest is left to users. Take a simple example: Microsoft Teams. Almost every organisation has it. Very few fully leverage it.

Not because the tool is not good but because users were given access, not ways of working.

AI will amplify this gap dramatically.

From tool deployment to capability building

With AI, the definition of “done” for tech teams must evolve.

It is no longer: Tool selected → Tool deployed → Access granted. Done

It becomes:

• People understand what AI can (and cannot) do
  

• People know how to use it in their daily work
  

• Teams adapt how they collaborate with AI
  

• The organisation evolves how work gets done
  

👉 In other words, AI is not a tool deployment problem. It is a capability transformation problem. It means investing in training, workflows, and change management.

Adoption is where the value is. This is where the definition of “done” should be.

There is a simple reality that tech leaders know… but sometimes forget: The value of a technology is not in its availability. It is in its adoption.

Left on their own, most users will:

• use AI superficially
  

• avoid it
  

• or use it without understanding its limits and its potential of damage if used wrongly

  This can create a paradox: AI is everywhere… but the value is limited.

And this is where tech teams can shift perception: from “IT support” to “business enablement”.

Overcoming the “IT stuff” reflex for business people

In many organisations, especially traditional ones, there is a very common reaction: “This is IT stuff, I don’t know anything about it.”

With AI, this becomes a major barrier. Because to use AI effectively, a minimum level of understanding is required: what prompts are, why context matters, what hallucinations are, how outputs should be validated…

Without this, users remain consumers, not AI-qualified operators.

Tech teams have a critical role to play here: not simplifying everything to the point of abstraction, but translating technical concepts into usable understanding.

Turning for example:

• tokens to cost awareness
  

• prompt engineering to structured thinking
  

• model limitations to critical judgement

This is a new form of enablement.

From individual productivity to team transformation

Most AI initiatives today focus on individual productivity, which can already create impressive results. But I believe that the real impact will come at the team level.

Questions we should look at further into implementing AI in an organisation:

• How does AI change how a team collaborates?
  

• Where should AI sit in workflows?
  

• What decisions remain human-led?
  

• How do we avoid fragmentation across teams?

Without this thinking, organisations risk: inconsistent practices, duplicated efforts, confusion and bigger silos.

AI as a colleague, not just a tool

Enabling AI is not only about tools or training. It is about rethinking how work happens.

If AI becomes: a co-pilot, an assistant, a colleague, sometimes an autonomous agent… then teams need to learn how to work with AI as part of the system.

This implies redefining roles. reshaping non only tech jobs, clarifying responsibilities, adjusting decision-making

In many cases, improving human collaboration is also a necessity to achieve AI integration of the ways of working.

The hidden foundation: knowledge management

There is also a deeper layer that becomes critical with AI: knowledge.

AI is only as good as the information it can access. And the reality is that most organisations are not strong in knowledge management.

For years, this was manageable. Us humans could compensate with: informal communication, experience, tacit knowledge.

AI cannot.

If knowledge is scattered, unclear (which powerpoint holds the latest info?), not maintained…
Then AI will amplify confusion instead of clarity.

👉 This is a major opportunity for tech leaders: to elevate and structure knowledge management into a strategic capability.

A shift in responsibility

All of this leads to a fundamental shift:

👉 Tech teams are no longer responsible only for delivering systems.

👉 They are responsible for enabling new ways of working.

And this is where the opportunity lies. Because the more AI spreads across organisations,

the more valuable will be those who can:

• connect technology with business reality
  

• translate complexity into usable practices
  

• drive adoption beyond tools

🙏 I’d Love to Hear From You

How do you see AI augmentation of people across functions happen in your organisation and is there a value added opportunity for tech teams to play in it? 

Reply to this email, I read every note.

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From Geek to Star by Khang | The Way Forward