Enterprise AI: Build or Buy?
Download Guide
Most enterprise AI ROI calculations are fiction. Not because the math is wrong, but because the assumptions underneath them are.
The standard infrastructure approach falls apart when applied to AI. Timelines stretch. Scope changes. The use case you started with isn't the use case that delivers value. And by the time you're 18 months into a project, nobody remembers what the original projections were anyway.
This isn't a failure of financial rigor. It's a failure to recognize that AI projects don't behave like traditional IT investments. The cost structures are different. The value capture is different. The risk profile is different. Applying traditional ROI frameworks to AI investments is like using a road map to navigate the ocean. The tool isn't wrong, it's just designed for a different problem.
Here's what actually works.
Before we can fix the problem, we need to understand why standard approaches break down. Four dynamics make AI investments fundamentally different from traditional IT projects.
The timeline problem. Traditional ROI assumes a defined implementation period followed by measurable returns. AI projects rarely work that way. The 6-month pilot becomes a 14-month pilot. The production deployment that was "weeks away" is still weeks away a year later. The denominator in your ROI calculation keeps growing while the numerator stays theoretical.
The scope problem. AI use cases evolve in ways that traditional IT projects don't. As you do more digging into the problem, the business case evolves. The document processing system will evolve into a knowledge search platform just before it becomes an agent workflow. AI is fast changing, and solutions need to adapt to new tools, frameworks, models constantly for it to remain cutting edge.
The attribution problem. Even when AI delivers value, isolating that value is genuinely difficult. Did revenue increase because of the AI recommendation engine, or the new sales team, or the market tailwind? Did customer satisfaction improve because of the AI-powered support system, or the new ticketing workflow, or the additional headcount? Traditional IT investments often have cleaner cause-and-effect relationships. With AI, you're frequently measuring a contribution to an outcome rather than a direct cause of it.
The sunk cost problem. Most enterprise AI projects require significant upfront investment before any value is delivered. Infrastructure has to be provisioned. Data has to be prepared. Models have to be developed and trained. Integration work has to be completed. There's also the management cost (think AI observability). Models can drift in performance, which unlike traditional software systems that are built once and forever used, these AI systems need to be monitored for performance degradation. By the time you have enough information to know whether the ROI is materializing, you've already spent most of the budget. The decision point comes too late to matter.
Given these dynamics, how should you think about AI ROI? Start by reframing the questions you're asking. The first question you should consider is, what specific business outcome will this AI enable?
Not "improve efficiency" or "enhance decision-making" or "drive innovation." You have to be specific. The kind of outcome you could put on a dashboard and track weekly.
Good examples include:
Organizations that prove AI ROI start with outcomes this concrete. If you can't articulate the outcome in terms a line-of-business leader would care about, you don't have a business case yet.
The second question that needs to be answered is, how will we know if it's working? Traditional business cases answer this question at the end. You’ll hear statements like, "after 18 months, we'll measure X and expect to see Y."
AI business cases need to answer it continuously. What will we see in week 2 that tells us we're on track? What should be true by week 6? What decision point exists at month 3 where we could change course if the early signals aren't there? The best AI investments are structured to prove (or disprove) value quickly, with explicit off-ramps if early indicators aren't promising.
Even well-constructed ROI models often underestimate certain costs that consistently blow up AI budgets. Let’s cover some of the variables you’re likely not factoring into your initial projections.
Data preparation. Most enterprise AI projects spend around 60% of their time and budget on data work. Things like cleaning, normalizing, connecting, and governing. This isn't a failure of planning, it's a structural reality of enterprise data.
Integration complexity. AI that works in a demo environment is different from AI that's integrated with your actual systems, security controls, and workflows. The "last mile" of integration often costs more than the AI itself. It's also where projects stall.
Ongoing operations. AI isn't a one-time implementation. Models need monitoring for drift and degradation. They need periodic retraining as data patterns change. Governance requirements evolve and security controls need updating.
Opportunity cost of time. This is the cost that's almost never calculated. Every month an AI project takes to deliver value is a month that value isn't being captured. An 18-month project with 200% ROI may actually be worse than a 6-week project with 80% ROI because you get 16 more months of value capture with the faster project.
As you can see, the organizations with the best AI ROI aren't necessarily the ones with the biggest returns. They're the ones who got to value fastest with the lowest total investment.
Unframe's pricing model is built around a simple principle. You should see value before you commit. Which is why we help you target specific use cases. That's the premise behind outcome-based AI. It’s a model where you pay for results, not inputs. And it really is a gamechanger when it comes to maximizing ROI.
Instead of estimating ROI upfront and hoping the projections hold, you define the outcome you need, validate that the AI actually delivers it, and only then commit to a broader investment. And here are just a few of the ways we’re able to demonstrate immediate impact to your bottom line.
Solution-based pricing, not seat-based or usage-based. You pay for AI outcomes delivered, not for infrastructure consumed or users provisioned. The cost scales with the value you're capturing, not with the resources you're consuming.
Validation before commitment. Every engagement starts with a defined use case and measurable outcome. You see the AI working on your data, in your environment, before any significant investment. No 18-month projects with ROI at the end. No hoping the projections hold. You prove value first.
No upfront infrastructure cost. Unframe deploys in your environment without requiring you to build AI infrastructure first. The data preparation, integration, and model deployment that can consume up to 80% of your budget and timeline are handled by our platform. You get to skip the hidden costs.
Unlimited users, unlimited usage. Unlike per-seat models that penalize adoption, Unframe's pricing encourages broad deployment. The more people using the AI, the more value you capture.
Pretty cool, right? If you’re interested, we recommend starting with a workshop to map your highest-value use case, define measurable outcomes, and see how Unframe would deliver them. No 18-month projections. No theoretical business cases. Just a real conversation about real value.
.png)
