The AI Boom Is Real. But So Is the Frustration
In 2025, it’s no longer a question of whether organizations are using generative AI. According to McKinsey, over 70% have introduced it into at least one part of their business. But far fewer can point to real results.
Despite all the energy and investment, only 13% of companies describe themselves as having truly embedded AI. And a recent MIT study found something even more sobering: 95% of enterprise gen-AI pilots fail to make a meaningful impact on the bottom line.
That’s not a technology problem, it’s a way of working problem. When organizations plug AI into legacy workflows, outdated decision-making models, and unclear roles, the result is inertia. The tech might be new, but the operating model is still stuck.
New Tools Won’t Fix Old Ways of Working
We’ve seen this story before: A company invests in a new IT system to automate a business process under the assumption it will bring efficiencies and free up teams to do more strategic work. A great idea on paper, but if the underlying process isn’t fixed before implementation, the tool will simply amplify the broken parts of the workflow.
Too often, AI gets treated as a standalone solution, not as a trigger to rethink how work actually happens. Processes remain unclear, decision rights stay ambiguous, and data remains siloed. People keep waiting for approvals instead of taking ownership and acting with confidence.
One McKinsey study showed that nearly half of AI innovation time is lost to internal hurdles like compliance, slow governance, or organizational readiness. So instead of becoming a force multiplier, AI just magnifies the inefficiencies already in place.
Here’s the reality: If your foundation is shaky, AI is not going to fix it.
At Proteus, We Start Where The Work Happens: In the Process
We help companies bridge the gap between vision and execution and between the promise of AI and the messy reality of day-to-day work. That means addressing not just the technology, but the workflows, structures, and habits that support it. Our approach to process improvement isn’t theoretical. We engage directly with the people who do the work to cocreate a process that meets the new demands AI brings.
It unfolds in three phases: Discover, Design, Deliver.
Discover: Start by Seeing Clearly
We start by listening. And not just to senior leaders, but to the people actually doing the work. We map how work happens in reality, not just what’s documented in the standard operating procedure. That includes identifying friction points and the areas where decisions stall or get endlessly kicked around. Often, we uncover habits that aren’t documented, but shape the way work gets done every day. We also assess the organization’s readiness for AI, not just in terms of data or systems, but in governance, role clarity, and a desire for change.
You can’t transform what you don’t fully understand, so we help to make the invisible visible.
Design: Build for What’s Coming.
With a clear view of current-state realities, we co-create a future-state design. This isn’t about process diagrams that live in binders for the rest of time. We help define what will actually change once AI is introduced: how hand-offs shift, who validates machine outputs, where exceptions go, and how data moves through the system. Just as important, we tie the process redesign back to strategic goals, so the “how” stays anchored to the “why.”
We also build in adaptability from the start. As AI tools evolve, so should the workflows around them. That means iterative loops, feedback mechanisms, and smart flexibility.
Deliver: Go Beyond the Launch.
Finally, we partner through the entire rollout. We don’t just hand over a playbook and wish you luck. We support implementation through training, communication, habit-building, and active change management. We help teams adopt the new way of working, not just understand it conceptually, but live it day-to-day. That includes embedding role clarity, decision routines, and governance rhythms that help the process stick. We also help build internal capability so your people can improve and evolve the process long after we’re gone.
This is where transformation becomes durable and AI becomes less of a pilot and more of a platform for long-term performance.
Why It Works and Why It Matters Now
What makes our approach different? It centers not just on the tool, but on the people and processes that use and power it.
Most AI projects fail not because the tech isn’t ready, but because the organization isn’t. The risk of rolling out AI without taking process into account is simple: you lock in inefficiencies and confusion at scale.
The companies that will win are the ones that ask not just ‘what tool should we use?’, but ‘how should we work differently because of it?’
Don’t Just Adopt AI, Activate It
If you’re integrating new AI tools or planning to, now is the time to rethink how it impacts your operation.
Before the code is written, the chatbot goes live, and before you push “go,” let’s make sure your processes are set to deliver the True Transformation you expect.
Connect with Proteus to realize the full potential of your AI initiatives.
