Succeeding with AI depends less on the technology and more on the fundamentals of good delivery. The terms are new and unfamiliar, but the process to deliver tangible outcomes is not. Don’t wait too long, build fluency and knowledge across your people, deliver use cases through managed projects, and the rest is disciplines you probably already have in your organisation.

The Pressure to “Do AI”

AI is changing what is possible and the speed at which the capabilities are evolving and becoming available is profound. Uniquely, the capability has rapidly become available for mainstream consumption in both professional and personal aspects of life.

Many decision makers quietly worry they are too slow to embrace AI. The speed of change in the space can be overwhelming, the potential AI investment costs are high, the benefits aren’t always clear cut, and AI has the scope to steal focus from people’s day jobs. A quick glance at tech blogs or LinkedIn will show peers, suppliers or competitors heralding their new complex agentic AI solution they’ve just gone live with and it’s so easy to think “oh no, we’re going to be left behind”.

Decision makers and risk owners face weighty decisions around AI adoption, often within regulatory, financial or operational constraints that limit how quickly they can move. Typically, we’re seeing businesses fall into one of the below categories:

• Keen AdoptersBusinesses aggressively pursuing benefits through dedicated AI initiatives or programmes of work focused on AI enablement in products, services and people.
• Passive AdoptersBusinesses passively adopting AI, self-labelling themselves as “pro-AI”, buying licences, setting guardrails, and letting teams figure it out as they go, hoping for benefits.
• Cautious AdoptersBusinesses doing very little to nothing in the AI space, fearing the risks, not knowing where to start and end up limiting access to AI while they figure out the plan.

None of these positions are wrong, but if you’re in the passive or cautious categories, don’t stall for too long, you may already have skills in your business to start to navigate it.

User Adoption Adds More Pressure

AI is unique as a technology advancement, it’s so easy for non-technical users to grab hold of. This has resulted in an unprecedented amount of general user adoption.

The enablement of business teams having hands-on access to self-service tools to build AI solutions is an incredible capability. It will generate enthusiasm and bright ideas and will support AI fluency growth in your organisation, but it comes at a cost if use cases are not chosen and shaped properly.

The fundamentals of delivering change and creating tangible benefits within business still apply, and many business teams may not hold those capabilities, let alone experience in designing end-to-end processes with technical considerations along the way. There is a risk of blind faith that AI will solve the problem, charging forward delivering use cases without validation on the root cause of the problem they’re trying to solve, and without benchmarks for what good looks like in the end.

Irrespective of your posture for AI implementations in operations and services, AI fluency and safety among your people should be a priority, via initiatives to deliver user education into teams. Pave the way for later by giving your teams a light framework for identifying and shaping use cases, so enthusiasm is pointed at the right problems that have the potential to unlock real benefit later. This should be a dedicated initiative that isn’t conflated with AI implementation projects.

AI Will Inherit Existing Issues

AI provides capabilities other technologies could not, the capability is unparalleled. It’s easy to understand why AI should be considered the solution to many inefficiencies or challenges that might exist in your world.

The common mistake is assuming that AI will singlehandedly deliver all of the benefits you’re looking for. Poor data will stay poor and AI can only go so far. Broken or ineffective processes that have organically grown will continue to deliver poor outcomes, AI may just make them faster.

That said, AI is not powerless against the problems it inherits. It can fill gaps older tools could not, such as drawing insight from unstructured data, and that is a genuine reason for optimism. So the foundations do not always have to be perfect before you begin, but the key message is to always consider that AI in isolation might not be enough.

This era of AI implementation echoes the arrival of robotic process automation (RPA) a decade ago. Where it worked, organisations hunted valuable use cases, fixed the process first and then automated something worth automating. Where it disappointed, teams automated the mess and were surprised when the mess just came out faster. The technology then was far less capable than AI is now. While the technology has changed, the pattern is familiar and the failure modes haven’t changed.

AI will not be forgiving on weak or absent change and delivery skills in your organisation, it will expose it faster and at a greater cost.

Treat AI Projects Like Any Good Delivery

What decides whether AI will pay off for you is not necessarily having the right AI expertise, it is having strong delivery disciplines. These are the same disciplines that decide whether any change succeeds. The tooling is new, but the underlying skills to unlock benefits are familiar ones, and those skills and experience transfer directly.

If your organisation already delivers change well, spend time applying your change and delivery principles to the context of an AI world. If change management and delivery is not a strength in your organisation, this is the moment to address it. AI will not be forgiving on weak or absent change and delivery skills in your organisation, it will expose it faster and at a greater cost.

Either way, when approaching projects introducing AI capability, the core message is the same. Find something worth solving, define and measure what success looks like, and work through delivery with rigour and governance.

Don’t let speed to deliver negate the need for the following core principles:

1. Find a valuable problem, not just “Adopt AI”. Be clear on the problem and what success looks like, and start with something you can measure.
2. Define your pathway to success. Decide up front what good looks like for the future, so you agree what you’re aiming for. Identify the metrics, telemetry and frameworks that can support this, baseline today to compare later.
3. Do not shortcut the requirements. The temptation is to jump straight to building, but vague requirements are something you pay for later. AI will not fix a flawed process, fix that first or stop. The quality of output is only ever as good as depth to what you asked for.
4. Keep hold of your outcomes. AI can take on the work, but it cannot hold the accountability. Decide where it acts alone and where a person stays in control, especially where decisions carry financial, customer or citizen impact.
5. Test it like you would anything else. Build a test plan with known scenarios and known outcomes, and check it is right every time, not just once. In some settings you also need to show why it is right.

The fundamentals haven’t really changed. Clear intent, good inputs, defined outcomes, a check on quality. AI changes what is possible, but it does not change what disciplines are required to ensure good outcomes.

What Will Actually Determine Success

AI is going to reward organisations that were already disciplined in change, and it compounds those strengths quickly. Clear briefs, well managed data, and a proper definition of the benefit you’re looking for should offer substantiated and sustainable results, particularly important in environments where outcomes are scrutinised, audited or externally accountable. AI is new, but the principles of effective outcomes still apply here.

The common risk is feeling pressure to move quickly and being tempted to shortcut the delivery lifecycle for quick results. Moving fast and applying the basics are not opposites, they can coexist.

Where those delivery disciplines are missing completely in your business, AI will not supply them. It strengthens the need for ensuring you introduce change management and delivery capabilities to your business. Your AI adoption journey will expose those gaps, make them harder to ignore, sooner and at a greater cost. That is not a reason to wait, but it is a reason to consider the skills in your teams and what your operating model looks like going forward.

New capability. Familiar disciplines. The organisations that execute AI implementation delivery and change with brilliant basics, are the ones that will achieve sustainable benefits for the long term.