Strategy and Execution, on parallel tracks
explore2Value (e2V) is our accelerated model for moving AI projects from concept to adopted workflow in ten weeks. Strategy and execution happen at the same time, so your team is involved from the beginning and knows how to use and manage the solution by the time it is delivered.
We focus on three things in every engagement
First, we understand how the business works and where it's losing time, money, or opportunity. Then we use data to identify where AI can create measurable value. From there, we build a solution that fits the way your organization works.
Business-First
We solve for your business problem before making technology choices.
Data-Informed
The data informs what to build.
Execution-Centered
We build solutions your team can operate, govern and continue to develop without depending on us
Our methodology is built to solve the four problems that keep AI projects from reaching production.
Unresolved data problems, technology-first thinking, executive support that fades during execution, and users who aren't involved in building the solution.
Data as a product
Most AI projects treat data as an input. We treat it as something your team builds, owns, and grows over time. When data is designed as a product from the start, it isn't rebuilt for every new use case. Instead, each new project builds on the last. This lets us deliver results with quicker time to value.
Data and AI as one connected stack
AI is only as good as the data behind it. We connect the solution to the organization's data foundation from the beginning, rather than treating data as an afterthought. That immediate solution has a stronger foundation and makes it easier to add new ones as the business changes.
Strategy through the lens of execution
Most AI engagements begin with a technology choice. We begin by understanding the problem. We map how the business works, where decisions are made, and what a measurable outcome looks like. Only then do we determine what technology is needed to solve it.
Adoption-first, via e2V
Most firms separate strategy from execution. We don't. Your team works alongside ours from the first week. Ideas start as hypotheses and are tested against real business outcomes before significant time and money are invested. The people who use the solution help shape it as it is built. By the time the solution is ready, adoption is already underway.
We don't build AI on top of a data problem
When the underlying data isn’t ready, AI can produce results that are inconsistent, inaccurate or difficult to trust. When don't trust the output, they stop using the product.
We address the data foundation before development gets too far. Connecting the AI solution to the organization’s broader data foundation early makes the product easier to maintain as the business changes and provides a base for new use cases.
Why the first use case matters more than you think
The first use case creates more than a single AI solution. It also creates clean, governed data that your team owns and can use again. The second use case starts with that foundation already in place. By the third or fourth, more of the underlying work has already been done. Each engagement can build on what came before it.
With the right data, every AI investment can build on the last one
We start with the business, not the technology.
Most AI consultants start with a tool. We start by identifying where AI can create a measurable business outcome. The data and the business requirements then help determine what to build.
After ten weeks, you have working MVPs in production that your team knows how to run and a roadmap for what comes next.
Explore & Evaluate
We map the business and identify where AI could create measurable value. Working with your team, we evaluate potential use cases and narrow the list to the ones worth pursuing.
By the end of week four, we know what we're building, why it matters and how we'll measure success.
Validate
Solution design and prototyping happen alongside user involvement. The people who will use the solution help shape it as it develops.
Working MVPs give the team something real to test and evaluate rather than asking them to react to a finished product.
Value & Scale
We deliver the working AI solution, a validated 12-month roadmap, and a quarterly execution plan. We also begin the process of putting the solution into use.
At this point, your team has experience with the solution and a clear understanding of what comes next.
Most AI programs don't fail because the technology is wrong
They fail because the foundation wasn't built right. If that sounds familiar, let's talk.