The AI wave is everywhere — but visible, lasting value is still elusive. AI initiative failure rates hover around 85 percent. I watched the following exchange among an executive team of a Fortune 500 financial services firm. Despite spending $3.7 million on its AI initiative and deploying some genuinely impressive technology, the team couldn’t agree on whether its AI investment was a success or a failure:
“That’s not what we asked for,” the CTO pronounced at the executive review of its AI initiative.
“Actually, it’s exactly what you asked for,” the data science lead shot back. “And we built a system with 94 percent prediction accuracy — currently the best across our industry.”
The CFO cut in, “But where are the cost savings? We’re six months in, and we haven’t seen a penny of the $12 million in operational efficiencies you promised.”
The COO shook her head. “Your insights are just not actionable. And they don’t fit our operational workflows.”
The CMO added, “And customers are complaining about the new process. Our satisfaction scores dropped eight points last month.”
Such conversations illustrate AI’s value problem. Each executive was judging success through an entirely different lens.
Here’s the truth most AI conversations miss: Value isn’t just one thing. It’s many things happening simultaneously — often pulling in opposite directions. When we talk about AI value as if it’s singular and universal, we’re setting ourselves up for confusion, disappointment, and, ultimately, failure.
The most successful AI implementations address multiple value dimensions simultaneously. Multi-dimensional impact is what separates transformative AI from merely incremental improvements.
Consider these different dimensions of AI value in your initiative planning:
1. Economic value beyond simple return on investment
Most conversations about AI value start and end with dollars and cents. That’s understandable, as economic value from AI typically manifests in:
- Cost reduction as tasks become automated and resources are optimized
- Revenue growth from new offerings, improved targeting, or enabling price optimization
- Margin expansion resulting from improved operational efficiency
Take the experience of an insurance client who implemented an AI-powered fraud detection system. The risk department celebrated reduced fraud losses of approximately $167M annually. The operations team valued the 73-percent reduction in false positives that was lowering their workload. Customer service appreciated the 29-percent drop in legitimate transaction declines. Crucially, all three departments experienced genuine improvements.
This represents the ideal scenario for multidimensional AI value — where the system creates positive outcomes across different stakeholder groups simultaneously.
2. Strategic value in competitive position and market differentiation
Beyond immediate financial returns, AI creates strategic value that’s harder to quantify but often more important. For example:
- Competitive advantage that creates brand recognition or capabilities competitors can’t easily replicate
- Market differentiation by offering unique experiences or solutions that stand out
- Innovation acceleration through compressing development cycles and enabling experimentation
3. Operational value of an AI transformation
Operational value often creates compound effects. Better quality reduces rework, which
improves throughput, which enhances capacity utilization. This creates a value chain that’s greater than the sum of its parts. These impacts translate to:
- Efficiency by completing tasks faster with fewer resources
- Quality by reducing errors and inconsistencies
- Improved decision-making via better choices and better information
4. Customer value with regard to experience, personalization, and trust
When customers benefit from AI, they rarely appreciate the technology — they just value the outcome. This distinction is crucial when measuring and communicating AI’s impact.
AI has the potential to create distinct value dimensions for customers in:
- Enhanced experiences and making interactions smoother, faster, and more satisfying
- Personalization by tailoring offerings to individual’s needs and preferences
- Trust through creating consistency and reliability that strengthens relationships
5. Employee value through greater productivity, learning, and fulfillment
The employee value dimension is often overlooked in AI planning, but proves especially critical to adoption. When a hospital implemented AI-assisted diagnosis, it found better adoption when it was framed as “decision support,” rather than “diagnostic assistance.”
People support what benefits them and resist what threatens them – regardless of the technology’s brilliance.
Yet, when frontline workers and end-users’ concerns are addressed, AI creates specific value dimensions for the workforce:
- Augmenting and enhancing human capabilities and productivity
- Skill development as it creates opportunities for growth and learning
- Satisfaction through removing tedious tasks and enabling more meaningful work
6. Stakeholder perspectives regarding minimizing negative impacts
Stakeholders, including regulators, community members, and other external parties, tend to ask: “Is this technology being used responsibly?” and “What are the broader implications beyond the organization?” Their concerns focus on:
- Fairness and non-discrimination
- Transparency and accountability
- Social and environmental impacts
- Privacy and data protection
These impacts often remain invisible in traditional value assessments but are increasingly scrutinized by external stakeholders.
Organizations that recognize that AI implementations must honor multiple value dimensions simultaneously without prioritizing one over another develop compounding advantages. Their AI initiatives will move faster, deliver broader impact, and create more sustainable competitive differentiation.
A chief digital officer for a global healthcare system observed, “The most valuable AI implementations we’ve done aren’t the ones with the most sophisticated algorithms. They’re the ones that connected across functional boundaries and created value that no single department could achieve alone.”
Written by Edosa Odaro.
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