Since 2016, I’ve led AI initiatives across multiple tech giants and learned an uncomfortable truth: AI projects aren’t just another technology implementation. They’re fundamentally different beasts that demand a completely new playbook. The challenge isn’t technical—it’s cultural and organisational.
The expectation-execution reality check
You’ve probably seen this meme format:
- Who are we? CEOs!
- What do we want? AI!
- AI to do what? We don’t know yet!
- When do we want it? NOW!
Behind the humour lies a painful reality: too many teams are tasked with finding AI use cases after leadership has already decided AI is the answer. This backward approach—solution in search of a problem—explains why so many AI initiatives deliver limited ROI.
The AI-IT culture mismatch
Here’s another uncomfortable truth: traditional IT departments and AI initiatives often clash at a fundamental level. IT excels at stability, predictability, and risk mitigation. AI thrives on experimentation, iteration, and controlled learning from setbacks.
This isn’t a criticism—it’s a recognition that effective AI value extraction requires new organisational structures. The highest-impact implementations create cross-functional teams that blend technical expertise with deep domain knowledge, giving them the autonomy to iterate rapidly and course-correct.
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The leadership paradox
There’s a cruel irony in many AI initiatives: the executives demanding “AI transformation NOW!” are often the furthest from the daily operational inefficiencies that AI could actually provide the best value.
Leadership sees the big picture but misses the granular friction points where AI delivers real benefit. Meanwhile, frontline employees understand where the most tedious and boring task is, but lack the authority or knowledge to implement solutions.
The answer isn’t top-down mandates or bottom-up rebellion—it’s bridging this gap through collaborative problem identification and solution design.
Beyond the accuracy obsession
Here’s another myth about model accuracy. The truth is, no matter how “cutting edge” the tool or model is, you would only know how beneficial it is after you test it against your data and scenarios.
Think of AI models like job candidates: a top performer at one company might struggle at another due to cultural fit, specific requirements, and operational context. Other company’s 95 per cent accurate model means nothing if it can’t handle your cases or integrate with your existing systems.
Simple AI got higher chance to win
Some of my highest-impact AI projects have been embarrassingly simple: a targeted document classifier, or a basic predictive model. No sophisticated design, no fancy models. Just well-scoped solutions to clearly defined problems.
The sexiest AI isn’t always the most valuable. When you have a hammer-and-nail problem, don’t reach for a Swiss Army knife just because it has more features.
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AI is fundamentally about people
Here’s what ties all these challenges together: We tend to talk about AI as a technological marvel—but isn’t AI’s core mission to emulate human intelligence? What makes the difference in implementation is not the shiniest model architecture or the latest algorithm—it’s a deep understanding of humans: their workflows, pain points, and how they make decisions.
Innovation is a team sport
The most inspiring AI transformations I’ve witnessed didn’t happen at companies known for cutting-edge technology. They happened at organisations that cultivated genuine collaboration between technical teams and domain experts, where innovation emerged from inclusive problem-solving rather than top-down technology mandates.
These companies understood that AI doesn’t transform organisations—empowered teams do.
The path forward
Effective AI value extraction requires a fundamental shift in approach, here’re some tips:
- Engage the front lines. Your best AI use cases will come from people closest to operational pain points.
- Build cross-functional teams. Combine technical capability with domain expertise and decision-making authority.
- Create a learning and sharing culture. AI is not your regular tech project—everyone has the responsibility to learn, try, and experiment. The best way to build consensus and understanding is by sharing knowledge and learning together.
- Start with problems, not solutions. Stop asking vendors “what are the use cases.” Identify specific inefficiencies, discuss the ideal state, then evaluate different tools or engage AI consultants to assess feasibility.
AI is fundamentally reshaping how we approach problems and democratising capabilities that were once exclusive to specialists. But here’s the real transformation: AI success is no longer confined to IT departments or tech teams. It requires every person in your organisation to become curious, collaborative, and willing to experiment.
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