AI Transformation: The Importance of Managing Goals and Accountability

By Jordan Richards, founder and CEO of AI Product Studio &above.

Tech is understandably the focus when AI transformation is under consideration. Businesses need to understand what’s available, how it works, and what it can do for their company. But while those become the all-consuming questions, other important factors often get overlooked, leaving businesses adopting AI without any clear strategy surrounding goals, onboarding, and accountability. And that inevitably leads to complications and dissatisfaction.

Why goals matter and how to identify the right ones

Hype has become the main driver of AI adoption decisions. You see something online, or that your competitors are doing, and it seems obvious that you should do it too. But without a strong strategy and a clear vision of what you want to achieve, anchored to the core value drivers of the business, AI adoption becomes directionless. And without direction, costly mistakes can be made. The only way for AI investment to deliver meaningful returns is for adoption to be built around goals with four key qualities:

Specificity

Vague goals are useless. Of course you want to “improve efficiency” and “reduce overheads,” but AI can only help if you can identify specific and measurable targets to do that. Whether that’s reducing customer acquisition cost (CAC) by 15% or cutting shrinkage by 25%. AI needs direction and scope to  deliver value.

Sequencing

AI goals need to be sequential. Having multiple goals in mind for your tech is fine, expected, but you can’t expect your transformation to deliver everything at once. When you have competing goals, things become complicated. Success comes from building and iterating, so start small, identify an easily achievable priority and go from there. Once you’ve achieved your first goal, you can move on to the second and so on, experimenting and improving until you’re confident and able to tackle more complex automations.

Value driven

AI isn’t cheap, so it needs to deliver value. A lot of businesses begin by using AI to handle simple admin, thinking it will cause the least disruption, but although it might save a little time for back office staff, it will in no way pay for itself. To deliver true value, AI needs to be used where it can best contribute to growth, efficiency, customer experience or risk reduction. So, before each phase of adoption, ask yourself whether the initiative provides revenue-related value.

People-centric

The fear with AI has always been that it will make people redundant, but while it can do some of the thankless tasks that people usually handle, it’s at its most valuable when it’s used to support people rather than replace them. AI adoption should always be viewed through a people-centric lens, so if it doesn’t change workflows, enhance collaboration, add efficiency to processes, or support decision-making, it’s probably not worth doing.

Accountability in onboarding and process management

Knowing what you want to achieve with AI adoption is the first step towards success. The second is the assignment of ownership and accountability. AI initiatives frequently fail due to poor management; because so many people are involved – IT, CTO, specific department heads – no one is made directly accountable, so no one takes action when things go wrong. This can be addressed through three core principles:

Clear ownership

The primary reason for assigning ownership of any AI initiative is to remove ambiguity. When one person knows that they are responsible, you don’t just ascribe accountability; you enable them to feel pride in a job well done, you remove conflict with other staff members, and you remove the potential for the project to simply fail through lack of guidance and interest. 

Clear measurement

You can never know if an AI initiative has been a success if you don’t have a definable way to measure it. Before AI implementation, you need to identify clear KPIs, baselines and success thresholds for each AI initiative. These will, of course, adapt and change as the project progresses. But this provides the guardrails that ensure transparency, and that’s the only way to truly ensure accountability.

Supportive governance

Governance isn’t just oversight; it’s the creation of structure and clarity, ensuring that all parties know what they should be doing and how they should be doing it, and giving them the tools they need to do it. It’s the easiest way to prevent mistakes and gain value from the initiative.

The role of accountability in AI implementation is two-fold; to ensure the project is completed in the best way possible; and to maximise the potential for learning and knowledge sharing. When you assign accountability, you create a project expert, and that can only be of benefit to both the business and the individual.

Marrying goals and accountability

When you set the right AI goals, you establish a clear target for your business. Accountability is what ensures you hit it. This is what makes AI transformation not just possible but genuinely valuable, preventing it from devolving into scattered, costly experiments.

Most AI initiatives don’t fail because of the technology; they fail because the business wasn’t prepared. The goals weren’t defined, the impact wasn’t planned, and the implementation wasn’t managed. With a structured, strategy-driven approach, AI can become the operational advantage your business has been searching for.