The promise of AI is everywhere. From predictive analytics to autonomous decision-making, AI solutions are transforming industries, accelerating innovation, and reshaping competitive landscapes. Yet, despite the excitement and beyond a few implementations, overall B2B AI adoption remains a challenge.
The reason? AI isn’t just another software solution—it demands clear, quantifiable value to break through. Without a compelling value narrative, AI risks becoming another expensive experiment rather than a must-have business enabler.
The AI Hype vs. AI Justification
Gartner’s latest research underscores this challenge: while 82% of organizations plan to increase software spending in 2025, the number of buyers prioritizing the need for AI value and risk assessment has surged by 70% year-over-year. These buyers are still patient when it comes to AI, but are indicating less return on investment (ROI) patience recently, with 54% now saying they need breakeven in less than 3 years.
This signals a major shift—business leaders are no longer buying AI simply because of its potential. They need hard evidence of its impact on revenue, efficiency, and competitive advantage. However, ROI results are not there for most. Gartner reports that at least 30% of generative AI projects will be abandoned after “proof of concept” due to poor data quality, deficient risk controls, escalating costs or unclear business value. BCG indicates that only 1 in 4 companies are seeing ROI right now with their AI, with the rest not having a business case for their AI in order to ever prove value returns. Buyers are screaming for AI ROI help.
For Chief Revenue Officers (CROs) and AI Chief Product Officers (CPOs), this presents a pivotal challenge: how do you move beyond AI’s promise and prove its value in a way that resonates with buyers? The answer lies in shifting from AI Product-Led Growth to a Value-Led Growth approach.
The Unique Value Considerations for AI
Unlike traditional software, AI requires a different lens when it comes to demonstrating value. Here’s why:
- AI Works as a Portfolio, Not Just a Product: Many AI solutions don’t deliver value in isolation—they function as part of a broader ecosystem. Vendors must articulate how AI enhances their entire product suite, not just one feature.
- AI as a Multiplier: AI amplifies existing capabilities, improving decision-making, efficiency, and automation across multiple workflows. The value story needs to reflect AI’s role as an accelerator, not just an incremental upgrade.
- Innovation Signaling Matters: Adopting AI is often a strategic move that signals innovation leadership. AI vendors should position their solutions not just as functional improvements, but as enablers of digital transformation and future readiness.
- Soft Benefits Can Be Hard to Quantify: AI often improves experiences—whether through better customer interactions, reduced manual effort, or faster insights. While these benefits are real, vendors need frameworks to quantify them effectively.
How AI’s Value Is the Same as Any Other Solution
At the same time, AI must still stand up to traditional business value measures.
Having clear use cases which tie to specific business objectives., address key challenges and solve important pain points with a high do nothing cost. Can demonstrate clear business benefits from the use case is essential.
Just like any other enterprise investment, AI needs to:
- Reduce Costs: Automating repetitive tasks, reducing operational inefficiencies, and optimizing resource allocation are direct cost-saving levers AI should demonstrate.
- Improve Productivity & Process Efficiency: AI should enable teams to work smarter, not harder. From streamlining data processing to accelerating decision-making, AI’s productivity gains need clear metrics.
- Reduce Risk: Whether through fraud detection, compliance monitoring, or cybersecurity enhancements, AI’s ability to mitigate risks is a powerful selling point.
- Capture Growth & Market Opportunities: AI’s ability to personalize experiences, optimize pricing, or improve customer targeting can drive revenue growth, making a strong case for investment.
The Need for Value Propositions Beyond Product-Led Growth
AI vendors can no longer rely solely on a try-it-and-see approach alone. While product-led growth (PLG) remains valuable, AI now demands an additional layer: value-proof. Prospective buyers need to see AI’s impact before they buy, and that requires:
- ROI Models & Business Cases: Vendors must help buyers quantify AI’s financial impact, from cost savings to revenue gains.
- Proof of Success: Real-world case studies, benchmarks, and customer success stories must reinforce AI’s value.
- Risk Mitigation Strategies: Buyers are concerned about AI adoption challenges—addressing security, bias, and scalability concerns upfront builds confidence.
The Bottom-Line: The Shift to Value-Led AI Growth
For AI solution providers, moving beyond AI Product-Led Growth to Value-Led Growth isn’t optional—it’s essential. AI buyers are becoming more sophisticated, and their expectations are rising. By focusing on clear, quantifiable business outcomes, AI vendors can not only justify their solutions but also establish themselves as strategic partners in enterprise transformation.
The AI market is evolving, and those who prove their value—beyond the technology itself—will be the ones who win.
Sources:
