Don’t start with AI. Start with the work

There is a lot of discussion about deploying AI across the enterprise. Most of it starts with the model, the platform or the tool. I start somewhere else: the work.

What decision are we trying to improve? What information does a person need at that moment? What gets in the way today? And what should the technology do when the situation does not fit the standard process? Those questions matter whether the employee is working at a desk, supporting a customer, writing software or managing a shift.

The work is different across those environments, but the technology challenge is connected. AI creates value when it is grounded in the context of the work and built into the workflow. It does not create value simply because it has been deployed.

That distinction is important for CIOs. At UKG, our own employees are primarily desk-based. My responsibility is not to run frontline operations. But we build technology for organizations whose work includes both corporate and frontline employees, so we have to understand both environments. The CIO’s role is to create the architecture, standards and operating model that allow AI to be useful across them.

Start with the outcome

The first question should not be, “Where can we add AI?” It should be, “Where would better information or faster execution change the outcome?”

For a desk-based team, that might mean reducing the time required to find the answer to a customer question, helping an engineer understand a complex codebase or giving a finance leader a clearer view of a business trend. For a frontline manager, it might mean identifying a staffing risk before a shift begins or helping resolve an exception without moving between several systems.

The use cases are different, but the method is the same. Map the workflow end to end. Identify the decisions, handoffs, systems and exceptions. Then decide what should be automated, what should be augmented and what should remain a human decision. If the process does not change, AI often becomes another layer on top of the existing friction.

Build the context layer

AI is not plug and play. A model can generate an answer, but a reliable answer depends on the context behind it: the right data, the user’s role and permissions, the current operating state, relevant history and the policies that govern the decision.

That context looks different depending on the work. An employee in an office may need project, customer, financial or technical information. A frontline employee may need current staffing, scheduling, compliance or operational information. In both cases, disconnected data creates the same problem: The system may be able to produce a response, but not necessarily one that is relevant, trusted or actionable.

This is why I think of the context layer as an architectural priority. CIOs need clear systems of record, reliable data, appropriate access controls and a defined way to retrieve and validate information. They also need to understand the difference between an answer, a recommendation and an action. Those are not interchangeable.

Redesign the work, not just the interface

There is a productivity gap that shows up in many AI programs. An organization deploys a tool, people use it and leaders expect the return to appear. But the work, roles and decision rights have not changed. That is not transformation. It is an additional tool.

If an AI system identifies a risk, who is responsible for acting on it? Can the person closest to the work resolve it? What constraints apply? When does the issue require escalation? A useful AI experience has to answer those questions as part of the workflow.

That requires close partnership among IT, business leaders, HR and the people doing the work. The technology team can provide the platform, integration and controls, but the business owns the outcome. The design also has to reflect how people actually work: different devices, different levels of access, different amounts of time and different levels of technical comfort.

Design with people, not for them

The people closest to the work are an important source of context. That includes an analyst who knows where a reporting process breaks, a customer-support employee who sees recurring issues or a frontline manager who understands what a staffing recommendation would mean in practice.

Representative employees should be involved in mapping, testing and improving the workflow. They should have a clear way to identify an incorrect answer, challenge a recommendation and report when a new process creates unintended work. That feedback is not a change-management afterthought. It is part of building a reliable system.

The CIO also has to be clear about the role of AI. The objective is not to remove human judgment from every process. It is to use technology where it improves speed, consistency and decision quality, while keeping people accountable for the decisions that require expertise, empathy or judgment.

Make the “3 Ts” decision

When we look at an AI use case, I find it useful to make what I call the “3 Ts” decision. Does the work require Talent for judgment or expertise? Can existing Tools solve it efficiently? Or do Tokens create enough value to justify the investment?

The answer will vary by workflow. A high-volume task may be a strong candidate for automation. A complex decision may need AI to surface the relevant information while a person remains accountable. In some cases, the best answer is not another model; it is a simpler process or a better integration.

This is also where CIOs need discipline around measurement. Prompts, licenses, logins and interactions are signals of activity. They are not proof of value. Measure whether the work is better: whether decisions are stronger, cycle times are shorter, errors are reduced, customers are better served or employees can spend more time on high-value work. Then pair those measures with trust and experience signals.

The CIO agenda

If I were evaluating an AI opportunity today, I would ask these questions:

• What business outcome are we trying to improve?
• What context does the workflow require and can we provide it reliably and securely?
• What needs to change in the process, role or decision rights?
• Have we designed with the people who will use the system and the people affected by its decisions?
• How will we know the work is better, rather than simply knowing that the tool is being used?

Enterprise AI will not be one experience for every employee. A desk-based employee and a frontline employee may need different interfaces, information and workflows. But they should be supported by the same underlying discipline: trusted context, clear accountability, human judgment and a focus on outcomes.

That is the work of the CIO. Simplify where we can. Standardize where it creates leverage. Scale what proves valuable. The goal is not to deploy more AI. The goal is to help people make better decisions and do better work — wherever that work happens.