Every era of technological change begins with outsized expectations.
In the early days of electrification, companies expected overnight transformation-factories humming more efficiently, profits soaring. But for decades, productivity gains lagged as firms struggled with rewiring, redesign, and rethinking how to use electricity to truly change the way work was done. Even more recently, the Internet faced this same hype-cycle crisis, with outsized initial confidence and taking much longer than everyone expected to deliver on value promises.
Artificial Intelligence is following a similar path.
The often quoted MIT study indicated that only about 5% of AI pilot programs achieve the projected margin or revenue outcomes expected; with the vast majority stalling, delivering little to no measurable impact on P&L. A recent New Yorker article, “The AI Profits Drought and the Lessons of History,” reminded us that technological revolutions rarely deliver instant payoffs. Gartner’s Distinguished VP and Fellow, Rita Sallam, echoed this point in a recent session: while business leaders and CIOs want bigger returns, faster, especially from AI, the reality is more complicated. Most AI investments are about building value for the future-not instant gains.
This tension-between the pressure for near-term ROI and the reality of delayed outcomes-is where the battle for AI’s business value is being fought.
As a GTM and value leader, AI will only matter to the enterprise if it turns into money-whether through savings, efficiency, or growth. For GTM and value leaders, the challenge is to arm sellers and CS reps with the tools and narratives to prove that case. Done right, they help customers move from AI hype and stalled pilots to trusted, outcome-driven investments.
Here’s what you need to know, to do this right.
The Reality Check: AI Costs Before It Pays
For many enterprises, the story starts with big upfront costs. AI projects that never launch. Pilots that stall. Initiatives that limp forward only to struggle with a lack of proper change management, little to no training and adoption challenges. The result: an ROI narrative that feels more like a drought than a flood.
Gartner is blunt about the challenge: the value of AI is highly specific to the use case, and proving that the benefits outweigh the costs is not easy. Resistance is growing-especially around generative AI-because funding approvals now hinge on demonstrating value today, not tomorrow.
This leaves executives with a critical question: How do you convince stakeholders that investing in AI today is worth the payoff tomorrow?
A Framework for Value: Defend, Extend, Upend
Gartner’s answer is to organize AI use cases into three categories: Defend, Extend, and Upend. Each represents a different pathway to value-and a different challenge in proving ROI.
- Defend is about maintaining your competitive position-focusing on productivity and efficiency. Think AI assistants and content generators. These tools promise savings, but not instantly. They require rollouts, training, and change management (all too often forgotten in the race to build and deploy). The savings show up over time, often indirectly: fewer new hires, smoother onboarding, happier employees. Yet because these benefits are diffuse, require education and behavioral change, and slow to measure, they’re difficult to monetize-and harder still to defend in front of a CFO looking for quick returns.
- Extend improves existing processes. Adding AI into enterprise applications like customer support or finance optimization. These use cases are less glamorous but more practical. They’re easier to pilot but expensive to scale, because every use case consumes resources differently. Still, they often deliver solid ROI-reducing spending, improving efficiency, and driving measurable process improvements. Think of Salesforce’s recent announcement of 1,000 customer support folks who are no longer needed as a result of AI automation, and your on the right track with Extend use cases, and ultimately the business justification.
- Upend is where Gartner indicates that the real promise resides: using AI to disrupt markets, transform industries, and create entirely new products and services. Here, ROI is not measured in short-term cost savings but in strategic outcomes: new markets, increased customer lifetime value, and stronger retention. In sectors like insurance or finance, these use cases also represent risk avoidance-preventing losses that would otherwise be catastrophic. The payoff can be immense, but it’s always in the future.
Why CFOs Need a New Lens
This is where many AI projects stumble. CFOs demand spreadsheets with clear returns, fast. But AI doesn’t conform neatly to those models. Costs are often hidden or underestimated-especially at scale. Training, integration, data management, and governance all add friction.
Gartner advises that CFOs and CIOs alike need a reality check. AI’s value case must include all costs, visible and hidden, alongside risk-adjusted expectations of time to value. For most organizations, that means reframing the conversation: not “how fast will this pay back,” but “what is the risk of not investing now, and what outcomes can we unlock tomorrow?”
The GTM & Value Leader Angle
The core issue both Gartner and the New Yorker highlight is this: AI isn’t delivering instant ROI, but leaders are pressured to show quick wins. For GTM and value leaders, this is exactly where they step in. Their role is to help sellers and customer success teams bridge the gap between expectations and reality.
- Set the Narrative Early
- Buyers want fast returns, but most AI use cases-especially “Defend” and “Upend”-require time, adoption, and scaling before value shows.
- GTM teams must tell a story that acknowledges the reality of delayed ROI, while framing the long-term upside and the risks of inaction.
- Quantify Value in Business Terms
- The pressure point for CIOs and CFOs is proving AI’s worth against hidden costs. Value leaders can equip teams with frameworks, calculators, and success stories that link AI directly to outcomes like reduced spend, improved retention, and new market growth.
- This shifts the conversation from “AI hype” to “AI as money.”
- Position AI as Strategic, Not Tactical
- The difference between stalled pilots and transformative programs often comes down to how AI and business value is positioned.
- GTM motions must show how AI use cases fit Gartner’s three categories: Defend, Extend, Upend – so that AI is always tied to business strategy, not just a technical project. And the biggest rewards go to those who can stack the business value, with near-term rewards to begin offsetting investments ASAP, as well as longer-term more strategic value outcomes.
When it comes to value quantification, here are some examples you can use to get started:
| Category | AI Use Case | Value Drivers | Key Metrics | Example Calculation |
|---|---|---|---|---|
| Defend (Efficiency / Productivity) | AI Meeting & Knowledge Assistant (e.g., auto-summarizing calls, drafting emails, prepping proposals) | • Reduce wasted time • Avoid incremental hiring • Improve employee productivity | • Hours saved per employee • Cost per FTE • Avoided headcount growth | Example: If 500 employees save 1 hour per week, at $80/hr loaded cost = 500 × 52 × $80 = $2.08M annual productivity gain |
| Extend (Process Improvement) | AI Customer Support Automation (chatbots, ticket routing, knowledge search) | • Faster resolution times • Lower cost-to-serve • Higher customer retention | • Cost per support ticket • Average handle time (AHT) • Retention rate / NPS | Example: If AI automates 100K tickets annually, reducing cost from $6/ticket to $2/ticket = $400K savings. If NPS improves, yielding 1% higher retention on $50M revenue = +$500K retained revenue |
| Upend (Transformation & Growth) | AI-Driven Personalized Product Recommendations / New Service Models (e.g., subscription bundling, predictive offers) | • New revenue streams • Higher customer lifetime value (CLV) • Market share growth | • Upsell/cross-sell % • Average deal size • CLV uplift | Example: If AI increases cross-sell rate from 10% → 13% on $200M revenue base = $6M uplift. If churn drops from 12% → 10% on $200M base = $4M retained. Total = $10M impact |
The Inspired AI Value Storytelling Framework
Here’s an example of what Inspired Value Storytelling using PIVOT should look like for effective AI messaging and selling:
| Element | What to Include | AI “Defend” (Efficiency / Productivity) | AI “Extend” (Process Improvement) | AI “Upend” (Transformation & Growth) |
|---|---|---|---|---|
| Pain & Impact (Why change?) | Highlight buyer struggles, risks, and costs of inaction. Show urgency by quantifying the AI value drought. | • High costs from stalled pilots • Wasted spend on unused tools • Employees frustrated with manual work | • Operational inefficiencies • Rising costs in service / finance • Slow, error-prone processes hurting customer experience | • Stagnant growth • Competitors innovating faster • Missed opportunities for new revenue streams or market entry |
| Vision (What if things could be different?) | Paint a credible, emotionally compelling future state. Buyer-centered, not product-centered. | • Knowledge workers supported by AI assistants • Faster onboarding, reduced burnout • Cost curve bent down over time | • Customer issues resolved in seconds • Finance closing books in hours not weeks • Processes streamlined across silos | • Entirely new products/services enabled • Personalized offerings at scale • AI-driven disruption reshaping markets |
| Outcome (What results can be delivered?) | Quantify business value in buyer terms – savings, growth, risk reduction. Provide CFO-ready outcomes. | • Cost savings from reduced hiring • Higher productivity per employee • 10–20% efficiency gains | • 20–40% process cost reductions • Measurable spend avoidance • Higher customer retention / NPS | • New market share gained • Increased Customer Lifetime Value (CLV) • Reduced risk exposure in regulated sectors |
| Trust (Why believe us?) | Provide evidence, proof, and credibility to make the buyer confident. | • Case studies showing gradual but real efficiency gains • Benchmark ROI calculators showing long-term payback | • Proof-of-value pilots • Industry analyst validation (e.g., Gartner’s Defend/Extend/Upend) • Public reference cases (e.g., Salesforce reducing 1,000 support roles) | • Visionary success stories • Strategic partnerships • Risk-adjusted ROI models acknowledging upfront costs but showing future payoffs |
The Bottom-Line
The current drought in visible AI profits doesn’t mean the well is dry. It means we are still in the messy middle-where costs are front-loaded, adoption is slow, and the value curve takes time to bend upward.
The companies that thrive will be those that:
- Defend their position by carefully rolling out AI productivity tools with robust change management.
- Extend their operations by embedding AI into processes with proven efficiency gains.
- Upend their industries by betting on transformational use cases that change markets, not just margins.
History shows that the biggest payoffs come to those who endure the drought and invest wisely in the future. AI is no different.
For marketing, sales, value and customer success leaders, the challenge is to help clients see beyond the immediate ROI drought – to the fields of value that will grow when AI finally delivers at scale.
Learn how you can meet the AI ROI mandate – empowering your messaging, sellers and customer success with tangible business outcomes and great value stories: click here to schedule a review with us.
Sources:
MIT Study – https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/
The New Yorker: The AI Profits Drought and the Lessons of History – https://www.newyorker.com/news/the-financial-page/the-ai-profits-drought-and-the-lessons-of-history
Gartner: Quantifying the Value of AI: https://webinar.gartner.com/754791/agenda/session/1708672?login=ML