Getting funding for an AI project can be an uncomfortable conversation.
You may know the project has potential. You may have a working prototype, interested users, or a strong technical case. But if you cannot explain it in terms leadership expects to hear, there is a good chance you will not get the support, or the budget, you are asking for.
That conversation is becoming more difficult now that we are moving past the “use AI everywhere” phase.
For a while, experimentation was the priority. Companies needed to understand what AI could do, where it could help, and how people would use it. Cost was often treated as something to figure out later.
Now, the AI bills are starting to become part of the conversation.
A recent Inc. article on rising AI costs, drawing on a KPMG survey of 2,145 executives, reported that one-third of respondents had only a limited understanding of their AI usage costs. As more providers shift toward usage-based pricing, companies are beginning to look more closely at what they are consuming, what they are getting in return, and where that spending should actually go.
This is the tokenomics reconsideration.
That does not mean companies will stop investing. It means that projects will need a more complete business case. Saving a few hours per week or month is probably not enough to move an executive decision. The challenge is to show how AI will redesign an inefficient process, remove steps, or change the cost of getting work done.
It is also a FinOps exercise. The AI bill has to remain reasonable in relation to the savings the automation produces.
That is what led me to ask Reddit’s CIO community: What AI use cases are actually “material” enough to get leadership buy-in?
The discussion generated more than 10,000 views and brought together several different perspectives. It was not a formal survey, and there was no universal answer.
But the comments did reveal a few useful patterns. The projects that received leadership buy-in tended to share certain characteristics.
1) Saving time is not the same as capturing value
Many companies begin their AI journey with individual productivity. They deploy copilots, assistants, and other tools that help employees write, search, summarize, analyze, or code faster.
And those tools may work. A task that used to take three hours may now take one.
But what happens to the other two hours?
One member of the r/CIO community described the problem clearly:
“Most companies can tell you the copilot was deployed and that people are using it. Very few can tell you whether the hour saved on Tuesday morning showed up as deeper focus work on Wednesday, more meetings on Thursday, or just an earlier logoff on Friday.”
This is where many ROI calculations fall short. License counts tell us whether the tool was deployed. Usage data tells us whether people opened it. Employee surveys tell us whether they feel more productive.
None of those metrics tells us whether the company captured the time saved.
That does not mean individual productivity has no value. It means that the value is distributed and difficult to trace. If the extra capacity is not intentionally redirected, it can disappear into more meetings, administrative work, or tasks that have little effect on business performance.
For someone trying to secure funding, this creates a difficult business case. Leadership is being asked to invest in an outcome the company may not be able to observe.
This is the distinction between soft ROI and hard ROI. Individual productivity gains can be real and still remain difficult to capture at the business level. The value becomes more tangible when AI changes a process, removes operational friction, reduces cost, or creates measurable capacity.
If you want to explore this distinction further, you can download our whitepaper, Understanding AI Types of Value: Soft ROI vs. Hard ROI in AI.
2) The same tool can produce different returns
The same commenter introduced another important problem: company-wide averages can hide the only useful signal.
“The same AI tool rolled out to two teams in the same org will produce wildly different results, because work shape isn’t uniform. One team gets compound returns. Another absorbs the time into meeting load.”
AI does not create value in isolation. Its return depends on the context in which it is implemented: the team, the workflow, the quality of the underlying information, and what managers do with the capacity it creates.
Imagine that two departments adopt the same copilot. One uses the recovered time to process more cases each week. The other continues producing the same output and fills the available time with internal meetings. At the company level, the average may suggest a modest productivity improvement. In reality, one implementation may deserve to scale and the other may need to be redesigned.
This is why the team or workflow is often a better unit of analysis than the entire company.
Before requesting more funding, look at what changed inside the area where the tool was implemented. Compare output, cycle time, workload, quality, and cost before and after deployment. Then connect those changes to a result leadership already cares about.
Adoption only becomes meaningful when it is evaluated in context.
3) Leadership backs business outcomes, not AI activity
One of the most direct comments in the discussion offered a simple filter:
“Start with initiatives that change throughput or cash, not just speed. The quick filter I use is pipeline, margin, or risk. If it does not touch one of those in a traceable metric, it rarely makes the board deck.”
This does not mean that every AI project needs to generate revenue directly. It means that the link between the technology and the business outcome should be clear.
Dynamic pricing may be evaluated through conversion or basket size. Automated invoice matching may be evaluated through processing cost, exception rates, or cycle time. Compliance monitoring may be evaluated through coverage, response time, or reduced exposure.
In each case, AI is connected to a metric that already has meaning outside the AI program.
That changes the funding conversation. Instead of asking leadership to support an interesting capability, you are asking them to support a measurable change in the business.
4) Fewer steps can be more valuable than faster steps
Another comment made an important distinction:
“Workflow automation that removes entire handoffs. Think ticket triage to resolution, claims adjudication, invoice matching. Not faster steps. Fewer steps.”
A copilot may help an employee complete one stage of a process faster. But the work may still wait in the same queues, pass through the same approvals, and require the same manual handoffs.
At the process level, the opportunity is different. AI can classify an incoming request, validate the information, route it to the right person, and identify the exceptions that require human review. The objective is no longer to accelerate one task. It is to change how the full workflow operates.
That makes the return easier to see. Removing a handoff can reduce cycle time. Eliminating a queue can increase throughput. Automating a review can reduce the number of cases requiring manual intervention.
These changes are often more visible in operating metrics and therefore easier to defend in a budget conversation.
5) Some projects are attractive because they change the capacity model
One participant described a customer service use case involving product recalls. During a recall, the company needed to process a sudden volume of emails and phone calls, collect serial numbers and batch codes, validate claims, and reimburse customers. In the past, it relied on agency resources to handle the spike.
AI created value because it allowed the process to scale faster, deliver a more consistent experience, and reduce the need for temporary external capacity.
The interesting part is not simply that each interaction became faster. The company changed the relationship between demand and cost.
This kind of operating leverage can make an initiative attractive to leadership. If transaction volume increases by 50%, does the company also need 50% more resources? Or can the system absorb part of that growth without a proportional increase in cost?
For workflows with seasonal demand, unpredictable spikes, or large document volumes, that question may be more useful than estimating how many minutes each employee saves.
6) The cost of keeping AI in the loop matters
Not every comment argued for putting more AI into business processes.
One member offered a useful warning:
“Use AI to build bridges, not be the bridge.”
The same person described using AI to investigate a network problem that had occupied a team for two weeks. With read-only access, the AI helped design a framework, write log queries, and produce a script that verified the issue in less than 20 minutes.
We think that the cost question starts with the choice of technology itself.
Not every workflow needs an agent. Not every task needs a large language model. In some cases, deterministic automation, computer vision, predictive models, or a combination of more traditional AI techniques can be cheaper, more reliable, and easier to govern.
The question should not be, “How can we add AI to this process?” It should be, “What type of technology is best suited to this problem, and what will it cost to run at scale?”
Our SAP Document AI workshop in Dallas offered one example of a strong fit. The use case involved an energy company processing more than 8,000 operational documents every month. Employees had to identify document types, locate sales order numbers, rename files, validate data, and attach documents manually to SAP records.
In that context, Document AI made sense. The process involved high document volume, variable formats, repetitive extraction tasks, and a clear manual baseline to compare against. After modeling the automated workflow with SAP Document AI and SAP BTP, the projected monthly cost fell from approximately $54K to $29K.
But the right answer can look very different in another industry. For Jose Cuervo, the challenge was not extracting information from documents. It was predicting what would happen in the field.
Inclusion Cloud combined drone imagery, humidity sensor data, and historical crop records to monitor agave plantations and predict future yields and risks. That is a predictive AI problem. The goal was to identify patterns in the data and make better decisions about irrigation, pesticides, and crop management. The project delivered 35% cost savings in irrigation and pesticides, along with a 65% improvement in monitoring frequency and accuracy.
Both examples involve AI. But they require different technologies, different operating models, and different cost structures. Choosing the right approach is part of the funding case. A project becomes easier to support when the technology matches the problem, the cost of operating it is reasonable, and the outcome can be measured in the business.
If you want to explore this decision further, download our whitepaper, Predictive AI vs. Gen AI: A Decision Framework for the C-Level.
7) Sensitive data can change the economics
The data involved can also change whether an initiative remains attractive.
As another community member noted:
“If you touch data that is business confidential, personally identifiable information, protected health information, or business-critical, the materiality changes.”
Once AI begins working with sensitive information, the potential savings must be weighed against the additional cost of governance, compliance, security, and human oversight.
A process may become faster, but one mistake involving sensitive data could offset or exceed the efficiencies it creates.
This does not automatically make the initiative a bad investment. It means the original productivity calculation is incomplete. The funding request should account for the controls required to operate the system responsibly.
There is no magic formula, but some characteristics keep coming up
To answer the question I started with, there is no perfect formula for getting an AI initiative funded.
That may sound like a safe answer, but there is a “but.” Across the responses, several characteristics kept appearing in the projects that participants described as receiving leadership support and, more importantly, funding.
Characteristic 1: A clear business outcome
The initiative connects to a metric that leadership already understands: revenue, throughput, unit cost, margin, risk, service level, or working capital.
“Improve productivity” is too broad. “Reduce invoice processing time by 40% while lowering the cost per invoice” gives the project a result that can be reviewed and challenged.
Characteristic 2: A specific context
The initiative is attached to a defined team, workflow, and owner.
This makes it possible to establish a baseline, compare the result after implementation, and avoid hiding different team outcomes inside a company-wide average.
Characteristic 3: A change in the process
The AI does more than help someone complete the same task faster. It removes a step, reduces a handoff, changes how exceptions are handled, or allows the process to absorb more demand.
Process-level changes are usually easier to connect to cycle time, capacity, and cost.
Characteristic 4: A plan for the capacity recovered
The company decides what should happen to the time saved before the project scales.
Will the team process more cases? Spend more time with customers? Reduce a backlog? Avoid a planned hire? Without that decision, the value may be real for the employee but remain invisible to the organization.
Characteristic 5: A realistic view of the full cost
The business case includes more than licenses or API consumption. It considers human oversight, governance, security, data quality, monitoring, integrations, and the cost of handling failures and exceptions.
Leadership is more likely to support an initiative when the economics still work after these costs are included.
Before you ask for funding:
If you are preparing to take an AI project to leadership, I would start with five questions:
- Which business metric should change if this project works?
- Which team or workflow will show that change first?
- What will be removed, redesigned, or scaled, not merely accelerated?
- Where will the recovered capacity go?
- What will the system cost to operate and govern at full adoption?
You do not need a perfect answer to every question before running a pilot. But you should know how the pilot will help you answer them.
Boards and leadership teams are not necessarily looking for the most advanced AI initiative. They are looking for a reason to believe that the value will appear somewhere they can see, measure, and manage.
Do you agree with these characteristics? Is there another one you would add or one you disagree with?
If you are trying to move an AI idea beyond the initial conversation, we can help. At Inclusion Cloud, we work with teams to turn early ideas into focused PoCs, validate the right technology, model the costs and expected ROI, and create something concrete to take back to leadership. Book a discovery call with us!