AI Readiness at Ippon, Measuring Impact, Building Momentum

Introducing a Three-Part Series by Laurie Lay

“THE future is here—it's just unevenly distributed.” - William Gibson

Never has a phrase applied with more emphasis to the current time than this one, including the “broadband revolution” Gibson coined to describe it. Ippon USA is a frontline witness to this, since, as a consulting company, we see projects across a wide range of modernity and maturity levels. These range from COBOL to cutting-edge AI agents functioning as corporate workers. We are seeing projects sealed in on 20+-year-old stacks, projects across modern data clouds, projects that provide their own healing, and projects that are replacing entire financial services operations systems.

Like most consulting firms, we’re highly involved in not just adapting to these changes ourselves but leading them and advising our customers. The AI future is unevenly distributed at Ippon, too. Our early work was supported by select experts and early adopters and has since grown across our company as demand for and the relevance of AI capabilities have increased (like a hockey-stick graph).

Like many smart firms, we wanted to understand our team’s adoption modes. In our case, to inform our strategy for connecting highly proficient, product-minded engineering consultants across our practices to AI adoption models that augment their capabilities for themselves and their customers. Ippon is a firm that celebrates practice and centers on science, so our Center of Excellence for AI responded by launching a comprehensive study. We used our findings to plan our strategy and offerings for today.

Laurie Lay introduces our methodology and will walk you through our results in her three-part series, which launches today alongside this article. The open-source reflex at Ippon dates back to our founding, and we share this with you now in the same spirit. We welcome you to reach out to me, Laurie, or any Ippon leader if you’d like to learn more. And now, over to Laurie!

Andy LaMora - CTO, Ippon Technologies

Part 1: Establishing the Baseline

Over a 12-week period, we tracked AI use across our consulting teams, collecting both quantitative and qualitative data. We wanted to know how AI was shaping productivity, focus, and daily work patterns. The goal was to establish a clear baseline to see where AI was delivering value, where it wasn’t, and what it would take to become truly “AI-ready.”

With over 80% participation across consultants, Ippon’s experiment showed clear signs of progress, a strong cultural readiness for AI, and tangible gains in productivity. The findings demonstrate a clear and positive return on investment. Beyond quantitative gains, the study found strong positive sentiment throughout the 12-week period, with 70% of participants agreeing that AI makes their work more productive and less frustrating. While barriers such as client policies and project-specific needs exist, the overall trend is one of growing proficiency and significant efficiency gains. This paper details the study's methodology, key findings, and strategic recommendations to build on this momentum and foster a truly AI-ready organization.

Artificial intelligence is fundamentally reshaping how we do our jobs. Across industries, companies are no longer asking whether they should use AI; rather, they are asking whether they are using it effectively and what measurable outcomes they can achieve.  At Ippon, we chose to confront these questions from the inside out. We believe that to guide our clients effectively, we must first build a deep, data-driven understanding of AI's impact on our own teams.

To achieve this, we launched an initiative to track AI use among our consultants. Our primary goal was to establish an honest baseline of AI adoption, to see where it delivers value and where it falls short. By quantifying AI usage and capturing feedback, the study provided a snapshot of where Ippon’s consultants stand today and what it might take to reach the next level of AI maturity. The results paint a picture of a company embracing change, with strong cultural readiness for AI and tangible gains in productivity.

The Study: Scope and Approach

The engagement ran for 12 weeks, during which we incentivized participation and actively tracked AI usage through weekly surveys. The participants included 40+ consultants from our four main areas of practice: product engineering, product management, data, and DevOps/cloud. More than 84% of consultants participated across the 12 weeks, reporting both their hours spent using AI tools that week and their estimated time saved for that same week, along with sentiment around their level of productivity, frustration level, and feelings about getting unblocked, as well as an open-ended question to detail the tools and use cases.

Based on a quick analysis of our findings, consultants logged 882 hours actively using AI tools and reported 962 hours saved as a direct result, or just over 2 hours of work produced for every hour worked. Said another way, using AI yielded a net gain of 80 consultant hours, equivalent to two full weeks of work, without increasing our consultant headcount. This reflects a clear positive return on investment: consultants earned back more time than they invested, even as many were still learning to use these tools effectively. The numbers also show that the benefits of AI were tangible from the outset and that proficiency increased as familiarity grew. We can explore this further when we break down the results in Part 2 of this blog series. We also found that our consultants were using a range of AI tools, including conversational assistants (ChatGPT, Claude, Gemini, and Perplexity), code-generation tools (GitHub Copilot and Cursor), and specialized applications such as Grammarly, Snowflake Cortex, and NotebookLM.

Establishing this baseline is essential for understanding where Ippon stands today and where additional value can be unlocked. These results provide a quantitative foundation for tracking ongoing usage, a clearer picture of where AI is delivering meaningful impact, and a deeper understanding of where training, integration, and policy updates will be required. Most importantly, the baseline offers a starting point for building a more mature, consistent approach to AI across client engagements. This is only the first chapter. In Part 2, we’ll take a closer look at what changed for consultants over the course of the study. We’ll also provide insights into how habits evolved, where sentiment shifted, and where we identified the biggest growth opportunities. Stay tuned for Part 2!