There is no shortage of discussion about what generative AI could change. The more important question is how to turn that potential into better work and better decisions.
That is a question we have been working on at PMP Strategy since 2022.
We started early, developing our own secure internal platform so that our consultants could experiment with AI while protecting the confidentiality of client information. This gave us a strong understanding of both the opportunities and the limitations of the technology.
It also taught us an important lesson. A central team can build useful AI applications, but it cannot anticipate every need across a business. If people must wait for a specialist team to create every new solution, adoption will always be limited.
The answer is not to hand everything over to users without structure. It is to give people room to experiment, while creating the governance and expertise needed to turn the best ideas into something useful for the wider organization.
That is the approach we have taken at PMP Strategy. Dust is one of the platforms our consultants use to build and test AI applications, alongside the more complex solutions developed by our Agentic Studio. The platform matters, but the way we have introduced and managed it matters more.
Today, 97% of our consultants use AI, and more than 500 agents have been created across the firm. Those numbers are encouraging, but they are not the main point. What matters is that our consultants are becoming more confident in identifying where AI can improve their work, deepen analysis or help them approach a problem differently.
Adoption is a leadership issue
Our experience has shown us that AI adoption does not happen simply because a new tool is made available.
It starts with leadership. Senior people need to understand the implications for their own work and for the organization as a whole. They also need to make it clear that AI is a strategic priority, not a side project for the technology team.
It depends on people as well. Our AI champions help colleagues move from curiosity to practical use. They share examples, answer questions and help connect the technology to the realities of client work.
Finally, it requires a degree of discipline. We have created a governance model that helps us identify the applications worth developing further, share successful approaches and maintain appropriate standards around quality and confidentiality.
This combination has allowed us to move from isolated experimentation to a more consistent firm-wide capability.
More time for the work that matters
The benefit of AI is not simply that a task can be completed more quickly. Its greater value is that it can change the quality and ambition of the work.
Our consultants can review more material, test more ideas and identify gaps or inconsistencies earlier. Work that might previously have been limited by time can often be explored in greater depth.
That gives our teams more time to do what clients value most: understand the situation, challenge assumptions, make connections and bring judgement to difficult decisions. AI supports that work. It does not replace it.
Our own experience has also shaped the way we advise clients. Many organizations begin with a question about which platform to choose. That is important, but it is not the hardest question.
The harder questions are about priorities, leadership, governance and behavior. Where will AI make the greatest difference? Who will lead the change? How will people learn to use it? How will successful experiments be shared? How will the organization know whether it is creating real value?
These are the questions we had to answer for ourselves, and they are the questions we now help clients address.
Our conclusion is simple. Technology is the starting point. The real work is building an organization that knows how to use it.
