Chief AI Officer and AI Team: Responsibilities, KPIs and Tools

The Chief AI Officer is the executive responsible for artificial intelligence in the company. They set the strategy, manage the use case portfolio, own the AI budget and report adoption and return to leadership. Where the role does not exist under this name, the same responsibilities are often given to the CIO, the head of innovation or an AI center of excellence.

This article describes the role's responsibilities, how to organize the AI team, which KPIs measure its work and which tools it needs.

What are the Chief AI Officer's responsibilities?

The responsibilities fall into five areas.

• Strategy and priorities. Which processes to transform with AI, in what order, with which goals.
• Use case portfolio. Inventory, assess, start, move into production and, when needed, close use cases.
• AI budget. Propose the budget, control actuals, reallocate resources based on results.
• Adoption and training. Make sure tools are actually used, and used well, together with HR, including for the purposes of article 4 of the EU AI Act on AI literacy.
• Compliance and risk. Coordinate with the DPO and CISO the review of GDPR, NIS2 and EU AI Act obligations for each tool.

The common thread is accountability for results. The Chief AI Officer answers not only for how many projects are started, but for how much the AI investment returns.

How does the role differ from the CIO?

The CIO governs infrastructure, systems, security and IT vendors. The Chief AI Officer governs AI use cases, adoption and return. The overlap is in tools and contracts: choosing between vendors, discussed in Copilot, ChatGPT or Claude: how to choose, and rationalizing licenses require both. It is worth writing down who decides what, so choices do not stall between the two functions.

How is the AI team organized?

The AI team is usually small and works with the functions, not instead of them. A typical setup includes these profiles, which in smaller companies may be covered by the same person.

• Portfolio manager: follows use cases from idea to measured results.
• Value analyst: builds business cases, baselines and impact measurements.
• Technical leads: integrations, agents, local models, in coordination with IT.
• Training lead: AI literacy and advanced usage programs, with HR.
• Function contacts: one person per area who brings needs and answers for local results.

A model that works is the center of excellence: the AI team sets shared methods, tools and rules, while functions deliver use cases with its support.

Which KPIs should the AI team use?

The AI team's work is measured on three levels. Indicators should be defined with formula, source and threshold, as explained in AI KPIs.

Portfolio

• Use cases inventoried, started, in production and closed in the period.
• Average time from approval to production.
• Share of the AI budget allocated to use cases with defined targets and baselines.

Adoption

• Active adoption rate by department and role, always in aggregate form.
• Inactive licenses as a share of the total.
• Share of people trained by role compared with identified needs.

Return

• Use cases with impact measured against a baseline, as a share of those in production.
• Ratio of measured benefit to full cost, use case by use case.
• Variance between budget and actuals, with the main items explained.

Avoid activity indicators, such as the number of prompts or projects started, used as targets. They measure movement, not results.

What tools does the AI team need?

The tools exist to keep together information that today lives in different places.

1. Use case register, with processes, technologies, owners and status. See the AI use case register.
2. Single view of spend and budget, with licenses, usage and projects mapped to functions and processes.
3. Usage data by department and role, read from vendor consoles and complemented by surveys for usage on personal accounts.
4. A shared method to measure impact, with a baseline before and a comparison after on well-defined processes.
5. A map of regulatory obligations by tool, with coverage status.
6. An up-to-date AI policy that people know, as described in company AI policy: how to write one.

What are the priorities for the first six months?

A newly appointed Chief AI Officer usually inherits a fragmented situation: tools bought by different functions, pilots without measurements, partial usage rules. There are four priorities for the first six months.

1. Make the current state visible. Inventory tools, contracts, usage and use cases, and map spend to functions and processes. Without this foundation every later decision is a guess.
2. Measure real adoption. Read usage data by department and role and complement it with a short survey on tool use, including usage on personal accounts.
3. Pick a few use cases to measure well. Three or four well-defined processes on which to build a baseline and prove the method, instead of measuring everything roughly.
4. Set rules and cadences. Usage policy, approval procedure for new tools, monthly actuals and the first quarterly report to the AI committee.

At the end of six months the role should be able to answer three questions with data: how much do we spend on AI and on what, how much are the tools used, which use cases deliver. The answers do not need to be complete, but they must be verifiable. From there, the work shifts from reconstruction to management: reallocating budget, consolidating overlapping tools, extending the use cases that work.

A common mistake at this stage is to start many new projects to give the role visibility. The opposite works better: close or scale back initiatives without clear goals, and focus the team on a few solid measurements.

How does the role report to leadership?

Every quarter the Chief AI Officer brings the AI committee a report on investment, adoption, return, risks and priorities, and every month an update on spend and variances. The format is described in the AI report for the board of directors. The principle is to always separate what is measured from what is estimated: the credibility of the role depends on it.

Where Delphis fits

Delphis is a platform to govern AI investment, independent of vendors, built for leadership and the AI team. It links budget, spend, real usage, use cases and regulatory obligations, provides adoption and return KPIs in aggregate form and prepares the information for reporting to leadership. See adoption and ROI.