266: AI Leadership Strategy: Stop Dabbling and Start Leading with AI

If you’re a leader right now, you’ve probably heard the phrase ‘AI changes everything.’ And yes, AI can change everything — but here’s the truth: most teams aren’t actually moving faster. They’re just experimenting more. That’s not AI-Ready Leadership — that’s AI dabbling.

In this episode, I want to talk about what it really means to step into AI Leadership Strategy — not just letting your team play with tools, but building the culture, processes, and expectations that actually deliver results. Because here’s the reality: the companies that become Future-Ready Leaders with AI — the ones that double or even 10x their output — they’re going to be the ones hiring when everyone else is downsizing. The rest? They’ll be scrambling to figure out how to maintain output with fewer people… and that’s a much harder position to lead from.

So if you want to be the leader who’s scaling output with AI leadership — not just talking about it — this is the episode for you. Let’s get into it.

2. The Current Reality

There is a widening gap between the hype around AI and the actual results showing up inside most companies. Despite all the noise, most teams are not scaling output with AI leadership. Companies that are building AI and their investors are spending billions. And uptake is looking good. But the reality is that so far, this isn’t resulting in the productivity boom everyone is predicting. 

In fact, A recent study suggests that AI coding tools might not actually boost developer productivity as much as initially expected, and in some cases, can even lead to slower completion times. While developers often anticipate increased speed, the reality, according to the research, can be quite different. However, the study also highlights that proper training and experience with AI tools are crucial for realizing their potential benefits

What is happening instead is that a well-meaning individual contributor discovers a tool, tests it out, and maybe even achieves some quick wins. While that experimentation is valuable, it is not a strategy. It is not AI-driven team acceleration. It is a collection of isolated initiatives that never become part of a repeatable, sustainable process.

The study, involving 16 developers, found that while the developers predicted an average 24% speed increase using AI, their actual completion times were, on average, 19% slower

Developers anticipated faster task completion with AI assistance, but the study revealed a different outcome, with AI actually hindering their progress. 

The study also points out that participants in the research received minimal training on the AI tools before using them. DataArt’s blog highlights this A separate case study showed that developers who used AI extensively and received proper training experienced a 25% speed increase. 

OK, so that’s just developers. But what about other areas of business. 

I’ll talk about my business for a second. I’m perhaps a moderately early adopter. I’m not the earliest to using LLMs as a CEO but Iwas certainly one of the earlier accounts. What has surprised me is my team’s slow adoption of AI. I mean my team is awesome. I love them. I’m honored to work with them. And yet rather than finding they’re all biting at the bit to use AI I’ve had to say ‘why aren’t you using AI for that’, and often they’re surprised or will give me a somewhat valid response around accuracy, hallucinations, and a very real concern about climate change and the impact of AI on climate. Which by the way is exactly what we were talking about in terms of the climate cost of gooling something a decade ago. 

I hear their concerns, and we’ll work through them. We do due diligence on what’s right or wrong. But I’ve been surprised that I needed to take this step. To me using LLMs in particular fo rmy business operations has been incredible for me. 

And this is the gap. The leadership gap to be precise. 

I talk about leadership gaps all the time. Lack of self awareness. Low than ideal confidence. Public speaking. Knowing to be aggressive. These leadership gaps are well understood and well documented. 

Now we have a new one – the gap between us as leaders and what we think our team is embracing with AI, and the reality. 

The reality is both a reluctance of uptake in some, a lack of training, a lack of learning how to use AI for true productivity gains, and still maintaining creative thinking. Because another study has shown that critical thinking drops with a greater reliance on AI. But here’s the curious thing – as with any tool, when used to expand how you think, it CAN and does grow. But if we use it to stop thinking, then yes, our ability to solve problems goes down. And i suspect this is what’s happening in the coding productivity problem too. Instead of critically thinking and using AI to write better quality code faster, we just work on replacing ourselves.

Again we’ve been here before – the use of calculators and computers was predicted to stop us all thinking. What it has actually required is a shift. And that’s where our teams have an opportunity. 

And that’s where we are loosing out. 

We think our teams are AI ready. But they’re not. 

But the companies who do have high productive adoption of AI, will be able to do more, cut costs. All while keeping their team. Their market share will grow. 

There are pockets of excellence. Engineering teams are ahead of the curve, embracing coding assistants, automation, and QA improvements. But marketing teams, in many cases, are still working with outdated workflows. They are not redesigning processes to create and test campaigns faster, nor are they using AI to analyse data in real time to inform creative decisions. This is a missed opportunity for AI productivity leadership.

Sales functions face the same challenge. The potential here is massive — AI can support almost every aspect of sales except the actual human conversation. From personalising outreach, to prompting timely follow-ups, to crafting better proposals, AI has the ability to accelerate sales cycles significantly. Too often, leaders are leaving these capabilities to chance rather than embedding them into the daily operating rhythm of the team.

Without future-ready leadership with AI, adoption stays reactive. There may be occasional bright spots, but without strategic direction, organisations will not see the sustained performance gains that protect market share and fuel growth.

3. The Coming Shift

Market forces are going to reshape teams, whether leaders are ready for it or not. As AI adoption matures, teams will inevitably become smaller. For organisations with strong AI productivity leadership, this shift will be proactive and strategic, allowing them to maintain or even increase output with fewer resources. For others, it will be reactive, driven by loss of market share and the urgent need to cut costs.

This is where strategic thinking for CEOs and senior leaders becomes critical. Those who anticipate the change and build AI-driven team acceleration into their operations will not only avoid forced downsizing but may find themselves in a position to expand while competitors contract.

Unfortunately, many leaders shy away from talking about these changes until they are unavoidable. There is a fear of the human impact of smaller teams, which is understandable. But ignoring the conversation does not prevent the change — it simply means the organisation will have to make harder, faster decisions later, often in a way that damages morale and trust.

Future-ready leadership with AI means having these conversations now, setting clear output and speed goals, and redesigning workflows so that AI is embedded at every level. Leaders who do this will be the ones hiring when others are firing, capturing greater market share, and building more resilient organisations in the process.

This piece is key. If you run a team of say 10 right now, and you managed to 2x or even 10x output, you can reduce costs. You’ll take more of the market. Your competitors will suffer. 

If instead you don’t actively work on improving the output of your team, but your competitors do, you’ll be loosing market share to your competitors. And you’ll realize it’s because they’ve reduced their costs and you haven’t. So the company will be forced to cut costs – which as we often know comes from staff cuts. Efficiency is often forced upon us by a loss of profits. But as anyone who’s ever been in a company that has done efficiency driven layoffs knows (and who hasn’t at this point) it is not a great way to build efficiency. You’re dragging an unhappy team forward. They don’t want to change, their anxious. They’re being asked to do not just what the company did before the layoffs but with fewer team members but more as well. 

You do not want to be that leader. 

And CEOs are finally realizing tht saying they adopt AI tools and buying them in isn’t enough. 

So you want to be an AI ready leader you need to be figuring this out for your specific niche. Not just enabling your team with tools, but actively moving towards it with purpose, discovery, coaching and much more. 

4. What “AI-Ready Leadership” Looks Like   

AI-ready leadership is not about having the latest tools or encouraging occasional experimentation. It is about creating an intentional, organisation-wide approach to AI adoption that delivers measurable business results. This requires an AI leadership strategy that is embedded into culture, processes, and daily operations.

The first step is vision. Leaders must set clear output and speed goals that define what success with AI will look like for the organisation. Without that vision, adoption becomes scattered and results remain inconsistent. But as we all know vision alone is not going to cut it. In fact it can feel like a hammer to knock everyone down with. 

The gap I’m seeing  is really the second step: proper and thorough enablement. Providing the right tools is important, but it is equally critical to give teams the training and frameworks they need to use AI effectively. This is where strategic leadership presence matters — demonstrating that AI adoption is not optional, but a key part of how the organisation competes and wins. This needs a lot of input from us as leaders, and all your managers down the stack. This is new territory. In fact the entire point of this podcast is to encourage you to lead your team through this before your competitors do. That means that you and your team don’t have the answer. Just saying ‘we need to move faster  by using AI’ isn’t going to cut it. You have to work with your team on the specifics of what this means. What workflow changes need to be brought in and how you’re going to experiment, while still ensuring your team are not loosing their critical thinking. 

The third step is measurement. Leaders need to track AI-driven outcomes, not just usage. It is easy to celebrate that teams are “using AI,” but without metrics that show faster delivery, increased quality, or reduced costs, the business case remains weak.

The fourth step is process redesign. AI must be built into the workflow, not bolted on as an afterthought. This often means rethinking roles, redistributing tasks, and finding ways for humans and AI to complement each other’s strengths.

Finally, there is culture. AI-ready leaders cultivate a mindset of curiosity, adaptability, and continuous improvement. This includes addressing ethical AI leadership — ensuring that AI is deployed in ways that are responsible, unbiased, and aligned with company values. And it means listening to your teams concerns – just like I’ve had to do. 

An AI leadership strategy built on these principles positions an organisation to move faster, adapt sooner, and lead in its market — while giving teams the clarity and confidence they need to embrace change.

5. Functional Examples

AI-ready leadership looks different in each function, but the principles are the same — embed AI into core workflows, redesign processes for speed, and measure the results.

In marketing, the opportunities are significant. AI can generate faster first drafts of content, support more creative variations, and enable real-time A/B testing at a scale that was previously impossible. It can also analyse campaign data instantly and provide insights that improve decision-making. Leaders who encourage this shift not only deliver better results but are also seen as strategic at work because they are proactively improving the team’s performance.

In sales, AI can handle almost every step except the actual human conversation. From researching prospects, to personalising outreach, to prompting timely follow-ups, to drafting proposals — the technology is available now. Leaders who guide their teams to integrate these tools consistently can achieve meaningful AI-driven team acceleration, freeing salespeople to focus on relationship building and closing deals.

Engineering teams have already shown strong adoption of AI for code generation, bug fixes, and QA automation. But there is still untapped potential in areas like documentation, technical debt management, and internal knowledge sharing. By embedding AI into these workflows, leaders can create sustainable gains in productivity without sacrificing quality.And as we discussed at the beginning maintaining critical thinking is key to making sure productivity gains are actually realized here. 

For all of these functions, the role of the leader is to provide clarity on the strategy, ensure AI adoption aligns with business priorities, and build a culture that sees these tools as enablers rather than threats. For us as women leader, this can be an opportunity for executive positioning — showing that you can lead a transformation that delivers tangible business impact and positions the organisation for long-term success.

6. Career Edge: Selling AI-Readiness in Interviews

One other area that I’m discussing a lot right now with my clients is being able to articulate this when you are interviewing. I don’t think many candidates can show a clear vision for this AI tool adoption for genuine performance changes. They think it is sufficient to say ‘yes I can bring in X tool’. Being able to lead AI adoption is not just a business advantage; it is a personal career advantage. Very few candidates in interviews can clearly articulate how they would use AI to drive measurable results. This creates a major opportunity for leaders who want to stand out and accelerate their career progression.

When you position yourself as someone who can implement an AI leadership strategy, you signal that you are thinking at an executive level. You are not just keeping up with the tools — you are driving transformation, aligning AI initiatives with organisational goals, and ensuring the company moves faster than its competitors. This is how to think like a VP, regardless of your current title.

For women in leadership, particularly in the tech sector, this is also a way to strengthen executive positioning in a world where I think we’re being seen as women as slow adopters. Something I personally think is unfair. Framing yourself as the leader who can double team output without increasing burnout, while keeping quality high, is a powerful differentiator.

In promotion conversations, connect AI-ready leadership directly to business outcomes: faster time to market, increased revenue, reduced costs, or improved customer satisfaction. This reframes you from being an excellent operator to being a strategic leader, which is essential for career progression in women in tech and for those asking how to get promoted at work.

If you can walk into an interview and clearly outline how you would embed AI into team workflows, set measurable goals, and lead cultural adoption, you position yourself as the candidate who is ready to deliver results in the AI era — and that is exactly the kind of future-ready leadership organisations are looking for.

7. Conclusions

AI is not a passing trend. It is reshaping industries, changing customer expectations, and rewriting what it means to lead effectively. The leaders who thrive in this shift will be those who step into AI-ready leadership now — not those who wait for someone else to set the pace.

Your competitors are not standing still. Somewhere, another team is building an AI leadership strategy, redesigning workflows, and finding ways to scale output with AI leadership so they can deliver more value at a lower cost. If you want to protect and grow your market share, you need to be doing the same.

Start by identifying one workflow in your team that can be redesigned with AI this month. Set a clear outcome, enable your team with the tools and training they need, measure the results, and build on those gains. This is how you shift from reactive adoption to future-ready leadership with AI.

Whether you are leading a small team or an entire organisation, the time to act is now. Build the culture, the processes, and the clarity that will make AI a competitive advantage — for your business, for your career, and for the people you lead.