We started Strategy of Things in 2017 because we believed disruptive technologies like the Internet of Things would reshape industries. IoT promised new visibility into operations, new efficiencies, and entirely new business models. We wanted to be at the center of that transformation by helping organizations apply these technologies thoughtfully and responsibly.

But as we began working with companies adopting IoT, we saw something else. The technology wasn’t the limiting factor. Organizations were. The more disruptive the innovation, the harder it was for companies to translate it into meaningful impact. They weren’t unmotivated. They were unprepared.

We saw organizations struggle to think about the technology strategically. We saw gaps in infrastructure. We saw failed pilots. We saw executive teams unsure how to manage something that didn’t fit existing models. And we saw how long it could take, sometimes years, before any real business value emerged.

That realization reshaped our mission. We weren’t just going to work on disruptive technologies. We were going to help organizations build the structure required to turn disruption into real sustainable impact.

That premise, accelerating the time from innovation to meaningful results through structure and operating discipline, defines Strategy of Things. And it is the same principle we bring to AI today.

Our journey from data to intelligence was shaped by what we were seeing inside the organizations we worked with.

Our work in IoT put us close to operational data - how it is generated, where it lives, and how it connects to real business decisions. Connected systems created new visibility into machines, products, and processes. While visibility was valuable, it wasn’t enough. We saw organizations move from asking, “What’s happening?” to “Can we predict what’s going to happen?” and then to “Why did it happen?” and "How can we fix it?" On a "smart corridor" project we managed, our customer's information needs evolved from a simple status dashboard to a digital twin that will proactively model responses and predict outcomes.

That progression naturally expanded our work. First into machine learning to detect patterns and predict outcomes. Then into systems that could reason across different data types and institutional knowledge. And now into emerging capabilities that don’t just recommend actions, but help coordinate execution within defined boundaries.

AI became a bigger part of our IoT work. As the intelligence deepened, the structural demands grew with it. More data meant more complexity. More autonomy meant more governance. More capability meant more need for executive ownership.

The technology evolved. But the same factors that prevented organizations from getting value and impact remained.

Data creates insight. Intelligence creates possibility. But turning intelligence into real business impact requires the right leadership and operating structure.

Over the years, our professional and volunteer work with clients, advisory boards, standards groups, and industry associations has shaped how we think about emerging technology.

We’ve seen innovation create real transformation. We’ve also seen promising initiatives stall when leadership, structure, and readiness weren’t in place. We’ve seen powerful technologies generate excitement but fail to translate into sustained impact. And we’ve seen disciplined organizations turn the same technologies into long-term capability.

Those patterns shaped what we believe.

We believe technology creates potential, but leadership turns potential into impact.

We believe powerful technologies create the most impact when they are practical and usable across organizations of different sizes and resource levels.

We believe the future is not humans competing with technology. It is humans and intelligent systems evolving together, strengthening decision-making and expanding what organizations can accomplish.

And we believe that as technologies become more capable, responsibility becomes more important. Ethics, fairness, accountability, and trust are not constraints on innovation. They are conditions for its legitimacy.

These beliefs are not theoretical. They are grounded in what we’ve witnessed across technology cycles. They shape how we design our operating models, how we advise leadership teams, and how we approach AI today.

Strategy of Things was built by people who have spent most of their careers inside innovative organizations, and who have seen how innovation either scales properly or slowly falls apart.

Over the past two decades, our work has spanned enterprise strategy, infrastructure modernization, innovation labs, and emerging technology initiatives. We’ve worked inside firms like Gartner, Accenture, Deloitte, and Cisco. We’ve built and launched a municipal innovation lab serving 20 cities and 29 county agencies. We've examined IoT technology and non-technology gaps across nine industries to inform federal policymakers where to direct future research investments as part of a collaborative award from the United States National Institute of Standards and Technology (NIST). We've been appointed by Secretary of Commerce to chair the NIST IoT Advisory Board to recommend federal actions to accelerate IoT adoption in the United States.

Those experiences didn’t just give us exposure. They gave us perspective. We’ve seen promising technologies stall because no one owned execution. We’ve seen pilots celebrated but never scaled. We’ve seen innovation treated as an experiment instead of an operating discipline. That experience shapes how we approach AI today.

We don't want to be the biggest firm in the room. In today’s environment, access to knowledge is not the constraint. Applying it with discipline is. Rather than adding layers, we invest in our own AI systems and operating model. We use the same tools and practices we advise on, internally, to stay current, rigorous, and responsive. It allows us to deliver enterprise-level thinking without enterprise-level overhead.

When you work with Strategy of Things, you work directly with us. We stay close to the work. We take responsibility for outcomes. And when additional expertise is needed, we draw from a trusted network built over decades. We focus on getting the work right.