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Lucent Search research | AI & Data Leadership Report 2025–2026
AI investment and return on investment
How will your organisation know whether its AI investment is delivering sufficient business value?
An AI investment case needs to explain what will improve, what achieving that improvement will cost and who is responsible for realising the benefit.
Those questions become more demanding as initiatives progress. Early funding may support experimentation, while operational use requires further spending on integration, people, assurance and changes to working practices.
Lucent’s research found that securing budgets and measuring return on investment were significant challenges for the leaders surveyed. For executive teams, the findings raise questions about how investment decisions connect to business priorities, delivery requirements and accountability for results.
RESEARCH FINDINGS
What the research found
71% found securing budgets for AI initiatives challenging
23.7% of respondents rated securing budgets as very challenging, while 47.3% found it moderately challenging.
The finding establishes that funding was a difficulty for much of the surveyed group. It does not establish whether the cause was limited capital, competing priorities, an unclear business case or other factors.
For an individual organisation, understanding that distinction matters. Each explanation calls for a different response.
85% found measuring return on investment challenging
The report records 85% of respondents finding the measurement of return on investment moderately or very challenging.
Difficulty measuring a return does not establish that an initiative has produced no value. Equally, it does not demonstrate that the investment is justified. It identifies a problem in assessing and evidencing the contribution.
Together, these findings make the relationship between funding, measurement and delivery an important subject for executive scrutiny.
BUSINESS IMPLICATIONS
What this means for the business
Leadership teams need sufficient evidence to decide whether an initiative deserves further investment, requires changes or should stop.
That evidence can become difficult to interpret when the intended benefit is loosely defined. “Improved productivity”, for example, leaves several questions unanswered: whose productivity, in which process, measured against what starting point, and with what effect on business performance?
Reported time savings also require interpretation. Released capacity may support additional work, better service or lower costs. The business needs to establish which outcome is intended and what operational changes will make it possible.
Costs deserve the same attention. Development expenditure is one part of the investment. Integration, training, oversight, support and ongoing use may affect the economics substantially.
The executive task is to bring those elements together in a business case that can be tested and updated as evidence develops.
VALUE
Rebecca’s perspective: establish the route into use early
I would want the business sponsor, finance team and AI or data leader to agree how the investment will be judged before significant delivery commitments are made.
They need a shared understanding of the intended outcome, the assumptions behind it and the evidence required to assess progress.
Some initiatives will produce measurable benefits early. Others will build capabilities that support later improvements. The investment case should make that distinction explicit, with appropriate milestones and decisions about continued funding.
I would examine four areas.
The outcome and starting point
Define the business improvement in terms that can be assessed.
An initiative might aim to reduce processing time, improve forecast quality, increase capacity or reduce costly errors. Establish the current position and identify the information needed to judge whether performance changes.
Where other initiatives or external factors may affect the result, explain how those influences will be considered.
The full cost of achieving the benefit
Include the work required to put the solution into use and sustain it.
That may involve data preparation, integration, specialist capability, operational training, assurance and ongoing support. Consider internal time and the demands placed on other functions as well as direct expenditure.
The investment decision should reflect a credible view of what delivery requires.
Responsibility for realising the benefit
Assign ownership of the business result.
The AI or data team may deliver a capability, while another function controls the process changes needed to achieve its intended benefit. Both responsibilities should be explicit.
For example, if an initiative releases staff capacity, an operational leader needs to decide how that capacity will be used. The financial case should reflect the outcome the business can reasonably deliver.
Evidence for further investment
Agree when the initiative will be reviewed and what would justify continuing, changing direction or stopping.
Early indicators might include usage, processing time, error rates or service quality. Where these are used, explain their connection to the intended business outcome and what further evidence is needed.
Update the investment case as costs, performance and operational requirements become clearer.
LEADERSHIP QUESTIONS
Questions for your leadership team
Use these questions when reviewing a pilot or a programme that is proving difficult to scale.
What specific business outcome are we funding?
Is it sufficiently defined to assess progress?
What is our starting point?
Do we have a credible baseline and a way to examine the contribution of the initiative?
Have we accounted for the full delivery cost?
Does the case include integration, adoption, assurance and ongoing operation?
Who owns the benefit?
Does that person control the operational changes needed to achieve it?
What do our current measures demonstrate?
Are we reporting activity, operational improvement or realised financial value?
What would change our investment decision?
Have we agreed the evidence that would lead us to continue, revise or stop?
HIRING IMPLICATIONS
What this means for leadership capability
AI and data leaders need to explain how technical work contributes to business priorities and what resources delivery requires.
The appropriate depth of financial responsibility will depend on the role. A leader accountable for a substantial programme may need experience building investment cases, managing budgets, evaluating trade-offs and working with finance to assess benefits.
Assessment should explore the candidate’s contribution to previous outcomes. What did they own? Which assumptions did they challenge? How did they respond when the evidence changed? What benefit was realised, and how was it established?
The organisation also needs to define the responsibilities around the role. Where benefits depend on operational change, those commitments should form part of the delivery model.
Lucent’s Leadership Requirement Diagnostic connects the expected business outcomes to the capability, authority and relationships required to deliver them. That gives the search and assessment process a clearer basis for evaluating commercial judgement.
EXPLORE RESEARCH
Explore related findings
AI ownership, accountability and executive sponsorship
How cross-functional commitments and decision rights support delivery.
Scaling AI from pilot to production
The integration, resourcing and operational requirements involved in sustained use.
The skills AI and data teams need to deliver
The technical, commercial and organisational capabilities required across the team.
ABOUT
About this research
This page draws on the organisational challenges section of Lucent Search’s AI & Data Leadership Survey Results 2025–2026, including questions about cross-departmental collaboration and organisational buy-in.
The findings reflect the participating leaders’ experiences at the time of the survey. They should be read alongside the respondent profile, question wording and methodology.
Research and interpretation by Rebecca Hastings, Founder of Lucent Search.
CONNECT
Defining the leadership needed to deliver business value?
If you are reviewing an AI or data leadership role, clarify the outcomes the person will own, the investment decisions they will influence and the evidence by which their contribution will be judged.
Speak to Rebecca about the business priorities, the capability already in place and what the next appointment needs to achieve.
Lucent Search · AI & Data Leadership Report 2025–2026