Prajkta Waditwar-Senior Technology Sourcing Manager at Box, focused on AI strategy & Procurement Innovation. The views expressed are my own.

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As organizations race to deploy AI across the enterprise, many are discovering that adoption is only half the challenge. The harder question is whether the value generated can keep pace with the growing cost of AI at scale.

The conversation around artificial intelligence has largely been driven by capability. Can AI write code? Improve productivity? Automate workflows? Accelerate decision-making? Those are important questions, but from my perspective in technology sourcing and procurement, a different conversation is starting to emerge: whether organizations can sustain the growing cost of AI while continuing to generate measurable business value.

I've noticed a meaningful shift in executive discussions over the past year. Earlier conversations focused on experimentation and adoption. Today, more leaders are asking harder questions about utilization, governance, vendor overlap and return on investment.

That shift is healthy. It signals that AI is moving beyond the hype cycle and entering a phase where business discipline matters as much as technical capability.

​The Costs Organizations Don't See Coming

One reason AI economics can be difficult to manage is that the costs rarely appear in one place. A software subscription is easy to track. The infrastructure, integration, security, governance and workforce enablement costs that follow often are not.

What begins as a relatively small pilot can look very different when deployed across hundreds or thousands of employees. Usage-based pricing models increase with adoption, cloud consumption grows, new security controls become necessary and additional support and governance structures must be established.​ The original business case often assumes the technology cost. The operational cost of scaling it is frequently underestimated.

I've seen this pattern before. Cloud adoption initially prioritized speed and flexibility before shifting to utilization and cost optimization. AI is entering a similar phase.

Another challenge is the rise of consumption-based AI pricing. Unlike traditional software contracts, many AI platforms charge based on usage, making costs far less predictable. As adoption grows, spending can scale much faster than expected, creating budget pressures that weren't part of the original business case.

From a procurement perspective, this introduces a new discipline. Managing AI is no longer just about selecting the right technology but also understanding how technology consumption translates into long-term financial exposure.​ The technology itself isn't the problem. It's the economics surrounding the technology that's becoming harder to ignore.

Adoption Doesn't Always Equal Value​

In 2024 alone, 72% of organizations were using AI in at least one business function, and 65% were regularly using generative AI. Organizations are beginning to realize measurable cost reductions and revenue gains, but those seeing the greatest value are not simply deploying AI faster. They are redesigning workflows, embedding AI into business processes and tracking clear performance metrics.

That distinction resonates with what I've observed from a technology sourcing perspective. The challenge is no longer deciding whether to invest in AI. It is ensuring those investments continue creating measurable value as adoption expands across the enterprise.

Interestingly, fewer than one in five organizations were tracking well-defined KPIs for their generative AI solutions. That suggests many companies are investing in AI before fully establishing how success will be measured. From my perspective, that's where procurement and technology governance become increasingly important—not to slow innovation but to ensure it translates into measurable business outcomes.

IBM's CEO Study reinforces this shift. While CEOs "expect the growth rate of AI investments to more than double in the next two years," "only 25% of AI initiatives have delivered expected ROI, and only 16% have scaled enterprise wide." The challenge is no longer enthusiasm for AI—it is translating investment into sustainable business outcomes.

This distinction matters. A department may invest in an AI platform to automate content generation while another adopts a separate tool for similar capabilities. Both decisions may be individually justified. Over time, however, overlapping investments can create complexity, duplicate spending and fragmented governance. More technology does not automatically create more value. Sometimes it creates more management overhead.

In 2025, "at least 50% of generative AI projects were abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs or unclear business value." Rather than signaling the end of AI adoption, this reflects a shift toward greater discipline in how organizations evaluate AI investments.

Why Procurement Sees This Shift Early

Historically, procurement has often been associated with contracts, negotiations and cost management. Today, its role is becoming much broader. As AI adoption accelerates, procurement increasingly sits at the intersection of technology investment, vendor strategy, governance and financial accountability. Few functions have visibility across software contracts, consumption trends, overlapping vendors and long-term commercial commitments in the same way.

That perspective becomes especially valuable when organizations begin asking difficult questions about value realization. In my experience, some of the most important AI discussions now have less to do with technical features and more to do with business outcomes. Are employees actually using the tools? Have workflows meaningfully improved? Is the technology solving a problem that couldn't have been addressed more simply? Will the costs scale at the same rate as the benefits? These are the business questions that will likely determine which AI investments succeed.

The Next Competitive Advantage

I don't believe the AI bubble is bursting. I believe the AI accountability era is beginning.

To optimize your AI strategies over the next several years, I recommend developing the discipline to connect AI investments to measurable business outcomes. That requires balancing innovation with governance, experimentation with accountability and adoption with economic reality.

The AI era is becoming more deeply embedded in how organizations operate and is increasingly important to effectively translate AI investment into sustainable business value. Because ultimately, the most expensive AI deployment is not the one that costs the most. It's the one that never delivers the value it promised.​​

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