Artificial intelligence is everywhere.
Every software platform seems to have AI features. Every conference has AI sessions. Every business leader is being told they need an AI strategy.
As a result, many organizations are asking the same question:
“How do we implement AI?”
The problem is that this is often the wrong place to start.
Recently, Nesanel Moeller, Co-Owner of The Penguin Group, shared an observation that has become increasingly common across industries:
“People are implementing it. They’re just rushing to say that they have AI without it actually being meaningful.”
That pressure to adopt AI is real. But for many organizations, the challenge isn’t the technology itself. It’s understanding where AI can actually create value.
Most Companies Are Experimenting With AI. Few Are Creating Real Value.
Businesses today are investing significant time and resources into AI initiatives. Yet many are struggling to turn those initiatives into measurable results. According to Boston Consulting Group (BCG), only 26% of companies have developed the capabilities needed to move beyond proofs of concept and generate meaningful value from AI.
That means the majority of organizations are still experimenting. The technology is being tested. The business value is often not.
This raises an important question: If AI adoption is accelerating, why are so many companies struggling to move from implementation to impact?
AI Doesn’t Create Value. Better Workflows Do.
One of the biggest misconceptions about AI is that implementation automatically creates results. It doesn’t. Technology creates value when it improves the way work gets done.
Research from McKinsey found that organizations seeing the greatest impact from AI are not simply deploying new tools. They’re redesigning workflows, operating models, and business processes alongside the technology. In other words, successful AI adoption isn’t just about adding AI. It’s about changing how work flows through the organization. Without that foundation, even sophisticated AI tools can struggle to deliver meaningful outcomes.
That’s often where businesses run into trouble.
They start with:
- We need AI.
- We need automation.
- We need a chatbot.
- We need a new platform.
But those aren’t business problems. They’re technology decisions.
What Successful AI Projects Actually Look Like
At The Penguin Group, some of the most successful AI projects we’ve worked on didn’t begin with a conversation about AI.
They began with a business problem.
For example, one healthcare organization needed a way to ensure medical assistants always knew how many tubes were required for different blood tests.
The challenge wasn’t a lack of technology.
The challenge was ensuring employees had accurate information at the right moment.
The solution was an AI-powered workflow that automatically calculates the required tubes based on the tests being ordered.
In another case, a client was struggling with inconsistent phone number formatting across their system.
Rather than relying on employees to continually correct the data, automation was used to validate and standardize phone numbers automatically.
Neither project started with:
“We need AI.”
Both started with:
“How do we solve this operational problem?”
The technology came second.
Start With the Friction
Before evaluating AI tools, organizations should first ask:
- What repetitive work is consuming valuable time?
- What errors occur repeatedly?
- Where do delays happen?
- What information are employees constantly searching for?
- Which decisions are being made manually every day?
The answers to those questions often reveal the highest-value opportunities for AI and automation. Because the best technology projects don’t start with technology. They start with a problem.
The Companies Seeing Results Are Solving Problems First
The organizations generating the most value from AI aren’t necessarily implementing the most tools.They’re identifying specific business challenges, improving workflows, and using technology to support those improvements.
The goal shouldn’t be to say your company is using AI. The goal should be improving the way your business operates. Because when organizations focus on the problem first, the right technology solution often becomes much more obvious.
And that’s where meaningful results tend to follow.