Presented by Insight Enterprises
Organizations today are trapped in proof-of-concept purgatory because yesterday’s models don’t work for today’s AI challenges.
Everyone’s racing to prove what AI could do. But the real winners are those who have realized that AI deployment is not a technology project — it is a core operational capability.
Success depends on execution, not just far-reaching visions of optimization.
At Insight, we’ve seen this cycle before. For more than 35 years, from our roots as a Value-Added Reseller (VAR) to our evolution as the leading Solutions Integrator, we’ve helped clients cut through the hype and make emerging technology actually work.
AI is following the same pattern. But this time, the stakes are higher, and the timelines are tighter. The organizations making real progress aren’t chasing pilots. They’re building the muscle to deploy, turning experiments and early momentum into measurable outcomes for the business.
What every technology “era” has taught us about AI success
MIT research estimates that 95% of enterprise AI initiatives fail to deliver measurable business value. This isn’t a failure of ambition. It’s a failure of deployment.
Too often, leaders are stuck in the “what”, obsessing over which model to use or how fast they can automate a single task. They get locked into long, costly discovery phases with traditional consultants that are all about theory and very little action.
We know this because we’ve lived it. When Insight first began experimenting with generative AI, our early pilots suffered from the same issues we see in the market: they looked great on slides but failed to scale.
We also hit cultural resistance and skills gaps. To overcome this, we had to stop treating AI as a “tool” and start treating it as a “capability.”
We started asking questions like, “Where will AI truly change how our people work and how our business performs — and how do we get there now?” OR “Given the AI tech advances, what is the art of the possible? How can we re-imagine our business processes and the work our people do to drive 10x improvement?
Now, 93% of our 14,000+ teammates are using generative AI tools in their daily work, saving more than 8,500 hours every week through automation and productivity gains.
Building AI that actually delivers value
If there’s one thing we’ve learned from decades of transformation, it’s that success isn’t born from strategy decks or proofs of concept.
It’s earned in the details.
As we brought together our AI experts from across our business, we saw that the most successful client engagements shared three common traits, but not the kind that fit neatly into a diagram. They’re about how work gets done:
Fees tied to outcomes. The old model of billing for time and material is broken. Commercial models need to put skin in the game. We win when you see measurable business value, not when we complete project.
Use tech to accelerate past theory. Instead of manual, multi-month discovery phases, look for partners who can accelerate your journey. We do this by providing our clients with an inventory of high-value use cases on day zero, so our consulting engagement starts with a roadmap to action, not just a listening tour.
Look at internal transformation. You cannot successfully deploy for your customers what you haven't mastered internally. At Insight, we built our suite of AI offerings by first transforming our own business. Our internal story isn’t just a data point. It’s our proof of concept for cultural and operational change. It’s how we break the old perceptions and prove we understand the human side of deployment. In our 2024 survey of IT leaders, 44% identified skills gaps as a top barrier to transformation, and 74% said they have focused time and budget on building custom AI tools. Yet most still lack the deployment discipline to embed them.
That’s the real craft of deployment. It’s not theory, and it’s not hype. It is execution at scale.
And over the past few years, we’ve built on those lessons to give organizations a clear roadmap from ideation to ROI. Real success comes from connecting expertise, tools, and a robust delivery engine to get beyond vision and experimentation.
The 70% that separates talk from transformation
I love this concept from Boston Consulting Group (BCG) called the 10-20-70 rule.
10% of success comes from algorithms, 20% from data and technology, and 70% from people, process, and culture.
Most companies invest nearly all their energy in the first 30%. But the real advantage (yes, the durable kind) lives in the 70%. That’s where execution happens.
At Insight, we’ve built our entire business around that principle. From cloud to AI, our mission hasn’t changed. We turn technology into a capability that clients can scale and continuously improve.
Turning AI potential into real-world results
The “AI theory” era is ending. This next chapter belongs to the doers. To organizations ready to apply intelligence the same way they operationalized cloud or digital transformation.
It requires a delicate balance of innovation and governance, and certainly bold ideas with disciplined execution.
In fact, that philosophy is exactly what inspired Prism, our way of helping organizations bring clarity to complexity. Clients can get a full inventory of AI use cases for their entire business on day zero, skipping the months-long discovery phase of traditional consulting and prioritizing opportunities for immediate impact.
We know that transformation doesn’t begin with algorithms. It begins with mastery, and it’s the kind we’ve earned through decades of deploying and scaling what’s next.
How are you moving from hype to how?
Joyce Mullen is President & CEO at Insight Enterprises.
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