Frequently Asked Questions

If your AI strategy is just adopting the same vendor stack as your competitors, you’re not building an edge. The real advantage comes from proprietary data, domain expertise, customer relationships or a distinctive operating model. That’s where lasting value lives.

The most effective AI strategies start by defining clear boundaries, where AI adds genuine value, and where the risk, cost or complexity simply isn’t worth it. This forces discipline and concentrates investment where it actually matters.

Hours saved makes a nice slide, but it’s not a business case. Factor in licensing, data preparation, change management, governance and new risk exposure. Then define proper baselines and control groups. If you can’t measure it properly, you can’t manage it.

AI changes who holds information, who makes decisions and who gets credit. If you don’t update incentives, performance metrics and job descriptions alongside the technology, adoption will quietly stall, or provoke active resistance from the people you need most.

Your people are already pasting customer data, strategy documents and code into external AI tools. Pretending it isn’t happening is a governance failure. The real question is: what will you explicitly allow, monitor and educate around, rather than hoping nobody notices?

This forces a hard conversation about trade secrets, regulated data and sensitive internal signals. It leads naturally into decisions about private models, on-premises deployment or simply not using AI for certain use cases. If you haven’t had this conversation, your data may already be at risk.

"The human is always responsible" sounds reassuring until something goes wrong and nobody knows who actually signed off. You need clear ownership for each class of AI-assisted decision, defined escalation paths and a realistic picture of what “due diligence” actually looks like.

Small hallucinations, subtle bias and slightly off-brand content accumulate into reputational and compliance damage before anyone notices. You need ongoing human review, quality scorecards, feedback loops and explicit kill switches for every use case..

Beyond “upskill everyone”, which roles will shrink, which will grow and what are your obligations to affected people? This needs a proper plan for role redesign, redeployment, updated hiring profiles and honest internal communication. Avoiding the conversation doesn’t make it go away.

A bad output, a data leak or a biased decision will eventually surface. If you’re scrambling to explain after the fact, you’ve already lost. You need a prepared story: what you use AI for, how you govern it, how you respond and what protections were already in place.

This treats AI as a feature to bolt on rather than a capability to solve specific problems. It leads to vanity pilots and “AI-washing” instead of focusing on a few high-value, well-designed use cases tied to strategy. Ask instead: Where will AI create the most measurable value for us?

Obsessing over a percentage before defining the use case is a distraction. You need to decide which processes to target, who will manage them and what training is required. Ultimately, the impact depends on the quality of the implementation, not just the tool.

Framing AI primarily as a redundancy lever both underestimates the change effort and damages trust across your organisation. It also misses the bigger prize: revenue growth, innovation and quality improvement usually deliver more value than blunt cost-cutting. Ask instead: How can AI help our people deliver more value?

Tool-first thinking skips over business objectives, data readiness and process health. Organisations end up with expensive platforms layered on top of broken workflows, then blame the technology when nothing improves. Ask instead: What business problem are we solving, and what does our data look like?

Understandable but not actionable at an organisational level. The more useful lens is: which tasks within which roles will shift, and how do we redesign jobs, skills and structures accordingly? The answer is almost always augmentation, not wholesale replacement.

Generative AI will always have some rate of hallucination and bias. The sharper question is: what level of error is tolerable for each specific use case, and what human oversight and controls are required to manage it?

AI amplifies whatever you already are. It doesn’t magically fix poor data governance, unclear ownership or broken processes. If you automate chaos, you just get faster chaos. Fix the foundations first, then apply AI to genuinely improve them.

Benchmarking is useful, but copy-paste thinking ignores your unique customers, assets and constraints. It pulls attention away from the harder work of defining where you specifically win with AI. Your competitive advantage won’t come from doing the same thing as everyone else.

Pushing AI off to a technical silo frames it as a technology project rather than a business and operating model shift. Without executive ownership of strategy, risk, incentives and culture, even technically excellent work will stall at the pilot stage.

Total avoidance feels safe but can be more dangerous, it means missing the learning curve while competitors build capability and institutional knowledge. The better approach is controlled experimentation with clear guardrails and governance, not a blanket freeze.