AI Center of Excellence: Brad
Brad is an experienced transformation leader with a background spanning the federal public sector, global enterprises, and new startup businesses. Here he talks about how he is introducing and implementing AI at clients, and why with AI adoption, it's important not to forget about the cultural layer.
Q. How do you describe your independent consulting offering and the typical focus of your work?
Gen AI adoption and enablement. AI adoption fails when use cases are fuzzy, trust is thin, skepticism is ignored, and skills are missing. I work with execs and leaders to translate messy priorities into a short list of solvable problems, then design AI-backed ways to tackle them. I start by selecting one strategic pain point that's meaningful and demonstrates the power of AI, then rebuild how you deliver it and lock the win into a standard play. And sometimes, you need to create breathing room before the breakthroughs and embed the most basic AI into daily tasks across teams. There are so many examples of these and the challenge is establishing new operating norms and ways of working. I also believe there is another layer to to AI adoption, and it’s cultural. Upskilling teams builds a foundation for creating a culture of safe experimentation. I help teams learn to try, compare, and keep what works without jeopardizing client work or data. As a transformation and change leader, I’m helping remove friction where it costs the most, then teaching organizations how to keep improving on their own, team by team.
Q. What are the specific benefits that you, as an independent, offer? How do you explain the value your expertise brings and the specific situations in which you should be engaged
Benefits
- Hands-on enablement. I build the play, train teams, and codify it in tools and SOPs.
- Operator’s lens. I tune delivery models, not just tools, so improvements stick.
- Vendor-agnostic advice. No quota, no platform bias. The stack serves the workflow.
- Speed without theater. I cut to one must-win use case and ship something real.
- Trust and safety built in. Clear guardrails for data privacy, review, and human oversight.
- Measurable value. I seta baseline, define the target, and track lift in hours, accuracy, or cycle time.
Engage me when
- You need one high-value win to prove AI’s worth.
- Strategic work is stuck behind repetitive tasks and you need immediate capacity back.
- Pilots are scattered and you want a simple, repeatable enablement loop.
- Delivery breaks at a specific point, like intake to quote, referral to appointment, or close to cash.
- Leadership wants guardrails for privacy and quality without slowing teams down.
- You have a willing team but limited new thinking and need practical guidance to shift the narrative.
Q. What made you realise that AI was going to be significant in the next stage of your independent career?
I’ve spent 25 years helping organisations use technology to solve real business problems. The lesson has always been the same. Tools matter, but out comes matter more. At Nestlé, working across innovation and delivery, I saw how small, targeted changes could remove bottlenecks in planning, research, and decision making. The wins came when we tied tech to a clear pain point, measured the lift, and taught teams to run the new way of working.
GenAI is significant to me because it compresses the path from question to action. It reduces the cost of analysis, drafting, and knowledge retrieval, and it lets non-technical teams improve core workflows without a platform overhaul. The pattern I’ve used for years still applies. Find pain points, redesign how you deliver it and then make the improvement repeatable. That is why I built my independent practice around AI adoption and enablement. I’m optimistic with a healthy amount of skepticism and understand what it takes to enable human-led transformation.
Q. How do you start the conversations with clients about where AI is going to impact them?
I start by setting the frame. Gen AI is a lever for tasks and a lens for insights, and works when you think of it as an assistant or junior teammate. I ask about uses and if there’s a structured program in place to enable teams and uncover opportunities. I typically cite studies many have seen showing 2x–4x productivity gains with AI, while also acknowledging the stumbling blocks and high failure rate. My recommendation is to get the help and support needed to bridge the gap.
Q. Describe a recent assignment in terms of success and outcomes and any personal learnings.
In a recent assignment, I led an inventory assessment project focused on aligning stock levels with real sales patterns, promotions, and space constraints. I built a custom GPT-based tool that analyzed sales history, vendor lead times, and seasonality to forecast ordering demand and flag overstock or slow movers.The model automatically adjusted recommendations for promotions and spacing, generating precise, context-aware order plans. The outcome was measurable: we reduced excess inventory, improved precision in ordering decisions, and saved roughly a day of manual effort each week. The project also sparked new ideas for using AI to streamline other operational decisions beyond inventory.
Q. What advice would you give to anyone about to embark on an independent career as to how they can provide the best results for a client, while fulfilling their own career path?
My three keys are personal discipline, continuous learning and authentic engagement. Every week I set aside time to learn, build, and publish what I find. I keep the scope small and the cycle fast. Pick a challenge. Write down what worked, what failed, and what felt clunky. Share the notes.
I test new AI tools constantly. I’ve built a working CRM in hours to see what “good”looks like. I’ve produced podcasts and videos with AI tools to map the real workflow, not the marketing promise. Doing the reps taught me where platforms and models are strong, where they break, and how to combine them without heroics. I am public and realistic about results. I post benchmarks, costs, and caveats. I say when something is brittle. That transparency builds trust and helps clients make decisions based on evidence, not hype. I also learn the client’s business like it is my own. Experiments only count if they map to revenue, risk, and customer experience. Knowing the model lets me point tests at the right constraints. You get faster answers and fewer dead ends. This practice compounds. Weekly experiments become a library of patterns, reusable components, and default choices. I can move from idea to demo quickly, teach teams what matters, and avoid the common traps. That is how I deliver better work while staying on my own growth path.
My personal rules:
- One dedicated learning block every week.
- Log the lesson. Keep a living “tool radar” and a playbook of patterns.
- Keep it pragmatic, human-centered and authentic.
Q. Finally, what do you like to do when you're not working in the depths of AI Consulting?
When I’m not deep in AI adoption, I’m usually outdoors or in the kitchen: golf in the summer, skiing in the winter, tennis whenever I can, and cooking most nights.