Eighteen months ago, most boards were asking leaders whether they were moving fast enough on AI. Today the question is harder: are our AI investments generating business value? Across industries, AI budgets have grown faster than the value leaders can point to, and education is no exception. Education technology (EdTech) and education leaders are spending aggressively on AI, and many struggle to tie that spend to outcomes. The numbers bear this out: McKinsey’s Enterprise AI FinOps Survey (May 2026) found that 93 percent of organizations were exceeding their AI budgets, as spend moves from isolated pilots to enterprise-wide adoption.
This isn’t a reason to slow down. Amazon Web Services (AWS) 2026 research, Reimagine: Turning AI into Value, drawn from 9 months of conversations with more than 150 C-suite leaders, reaches a pointed conclusion: the organizations getting the most from AI are those who best understand what AI is for, and what it is not. The organizations pulling ahead are the ones who can connect AI spend to value and decide where to invest next.
This is the pattern of every major platform shift. In cloud, software as a service (SaaS), and streaming, the winners kept investing but did it strategically, redirecting spend away from what wasn’t working toward what was. At AWS, we increasingly see our role not only as the place customers build, but as the partner who helps leadership optimize, prioritize, and govern what they have already built.
What follows is a straightforward, durable way to structure AI investment decision-making. It’s intentionally high level, a way of thinking rather than a prescription, and it reflects how we work with education leaders today.
The portfolio no one has evaluated as a portfolio
The first challenge is that “AI spend” is no longer one thing. It spans at least four areas of the business, usually owned by different teams and rarely looked at together:
- Customer-facing AI features: The AI embedded in the products you sell, from tutoring and adaptive learning to content generation and grading
- Internal AI-powered solutions: AI used to run the business, such as content pipelines, internal search, support automation, and analytics
- Employee AI tooling: The coding assistants and chat tools your teams use every day, often purchased outside central IT
- AI infrastructure and platform: The foundational compute, models, and platforms underneath all of it
Most organizations have decent visibility into one or two of these categories and almost none into the rest. You can’t optimize what you can’t see or prioritize what you’ve never compared.
A framework for the conversation
Getting a return on AI doesn’t require a perfect measurement model. Instead, implement a shared way of looking at the whole picture and making decisions together. We organize the conversation around five principles. First, you need to visualize the whole portfolio. Then tie your spend to the value you get out of it. Use a shared grid for decision-making, looking both forward and backward. Finally, build a repeatable mechanism.
See the whole portfolio
Put every area of AI spend on one page: customer features, internal solutions, employee tooling, and infrastructure. Don’t get caught up trying to make it perfect. Make it a categorized view leadership can see at one time. That single view is often the most valuable output of the exercise.
The good news is that much of this is straightforward to see on AWS. Tools such as AWS Cost Explorer, cost allocation tags, and the Amazon Bedrock built-in cost attribution break AWS AI spend down by team, application, and environment in days, with no new tool to buy. And because a full view has to include spend that doesn’t run on AWS, our team helps extend that same picture to third-party models, employee AI tools, and vendor licenses, so leadership gets one complete view rather than only the AWS slice.
In practice, this first step is where the surprises surface. The problem is rarely that a company is investing too much in AI. Usually, enterprises discover that the spend is unmanaged and only loosely tied to value. Across our engagements, we see the same three concerns again and again:
- Costs climb faster than expected, as introductory pricing ends and usage scales.
- Adoption outruns governance, with teams building on AI company-wide before shared standards or guardrails exist.
- Investments no one can connect to outcomes, where the spend is real but the value is unproven.
Tie spend to value, not only cost
Your total bill says almost nothing about whether an individual feature or tool is worth it. The harder, more important question is value: which investments are people using and which drive outcomes that matter. Attaching even a directional sense of value changes the conversation from “what does AI cost” to “what is AI worth.”
Decide with a shared grid
A single grid displaying estimated cost and value gives a cross-functional team a common language for choices previously made in isolation: accelerate what works, refine what is valuable but expensive, reduce what isn’t earning its place, and retire what is costly and underused.
One more question increasingly belongs on that grid: how flexible each investment is. A feature that is cheap and valuable today but tied to a single provider carries hidden risk as prices and capabilities shift. Putting flexibility next to cost and value lets leadership see that exposure now rather than discover it later.
Look backward and forward
The same lens applies to what you have built and what you plan to build. Looking backward calibrates your assumptions against what investments delivered; looking forward scores the roadmap against that reality. This is how a roadmap becomes a set of prioritized bets rather than a list of good ideas.
Build a repeatable mechanism
AI will keep changing, so a one-time review isn’t the answer. The lasting value is a repeatable way for your organization to evaluate future AI investments as the technology, costs, and opportunities shift.
Other ways AWS helps
Structuring the conversation is the start. Acting on it is where AWS goes deeper, in a few practical ways:
- Consolidation for visibility and governance –When it helps, bringing fragmented AI usage, including third-party models and tools, onto a common platform such as Amazon Bedrock creates cost visibility, guardrails, compliance controls, and team-level budget tracking, which makes every downstream return on investment (ROI) conversation more credible. It’s an option the review might surface, not a precondition for it.
- Data-informed roadmap decisions –Product teams prioritize better when they have aggregated context, signals from customer conversations, support, delivery systems, and competitive trends in one place rather than relying on instinct.
- Accelerating the path from build to production –Speed only matters if it’s safe. Automated quality assurance (QA), evaluation, compliance, and security checks shorten the cycle between a finished build and general availability, which matters most in regulated environments like education and the public sector.
None of these is a single perfect solution, and customers rarely need all of them at one time. But when a leadership team can see its AI portfolio clearly, the next moves become obvious.
Why AWS
Plenty of firms will offer to help rationalize AI spend. AWS starts somewhere different: with the customer’s problem. Customer obsession is a core Amazon leadership principle, and it means we work backwards from what our customer needs, not forward from a product we want to sell. Right now, the need we hear most is that leaders can’t see their whole AI portfolio at one time, let alone judge what each part is worth. This review was built backwards from that problem. We bring no stake in which of a customer’s features wins, a point of view grounded in our Reimagine: Turning AI into Value research (drawn from more than 150 C-suite leaders across 128 organizations), and the experience of having run this same discipline on our own AI portfolio.
Getting started
If your board is starting to ask what it’s getting for its AI investments, this is the right moment to dive deeper. You don’t need perfect data to begin. Start by putting the right people who have a shared way of looking at the whole picture in a room together.
A few practices from our research make the first pass more productive: put cost and value accountability with the same owner rather than leaving cost with IT and value with the business; pair every efficiency metric with a quality or outcome counterweight so speed never hides a value problem; and give every AI investment a named owner and a measurable outcome, retiring the ones that have neither. None of this requires audit-grade precision, only the discipline to look honestly and decide together.
To initiate your AI portfolio conversation, connect with the AWS EdTech team.
