Here’s a pattern I see over and over.
A CEO or board says, “We need an AI strategy — now.”
A committee gets formed. A few licenses for a shiny new tool are handed out. A handful of people are told to “go explore.”
And then… not much happens.
No measurable return. No real adoption. No meaningful change.
It’s not because the people involved aren’t smart or capable. It’s because AI isn’t just another software rollout. It’s an organizational shift. And if you don’t lay the right foundation, you end up with a lot of activity …and very little impact.
We’re seeing big names learn this the hard way:
- Johnson & Johnson spent a year experimenting with nearly 900 AI pilots before realizing that only 10–15% of them drove 80% of the value. CIO Jim Swanson summed it up: “That was a pivot we made after about a year of learning. Now we’ve moved from the thousand-flowers to a really prioritized focus on GenAI”.
- Walmart is doing its own course correction, consolidating dozens of disconnected AI agents into just four “super agents” designed for customers, associates, engineers, and sellers. As CTO Suresh Kumar put it: “It became very clear that we could dramatically simplify… you shouldn’t have to remember that and switch between those two”.
- And at P&G, the story is less about scaling back and more about proving AI’s value in teamwork. An internal study found that “AI can be a great ‘teammate’… teams working with AI were about 12% faster”.
The message is consistent: tools alone aren’t enough.
The real edge isn’t in the tool — it’s in the readiness.
I believe that over the next year or two, AI technology will continue to advance rapidly, but access to those capabilities will become universal. Everyone will be able to use similar tools. The companies that pull ahead will be the ones that have prepared their people, their processes, and their data to take advantage of them.
Build the foundation first, then focuson the technology. That’s why at SoftSnow, we don’t begin with tools, we begin with the foundation. We assess where the organization stands today across:
- People – Are roles, skills, and culture aligned for change?
- Process – Have workflows been updated so AI can be part of them?
- Data – Is the information complete, clean, and connected?
By clarifying these fundamentals, we identify the right entry point for AI adoption and create a clear path forward. Without that foundation, even the most advanced AI becomes just another underutilized technology investment.
People: From job descriptions to working AI agents
Most organizations underestimate how important it is to update job descriptions and standard operating procedures before rolling out AI. If you can't clearly define how a job gets done, the AI has nothing reliable to work from.
Here's what we do, our approach is thoughtful and structured: we map each role to its responsibilities, then design AI agents that enhance the actual workflows. This way, the technology is calibrated to how work really gets done — creating a natural integration between human expertise and AI capabilities.
When we take this approach, even initial wins create powerful momentum. I've watched teams light up when they see routine checks automated, information retrieved instantly, or anomalies spotted in real time. People experience the benefit in their day-to-day work, and resistance drops fast.
Process: Embedding AI into the way work really happens
The fastest way to kill AI adoption is to make it feel like “extra” work instead of building it into the work itself.
We roll out department by department, plugging AI into existing tools and workflows so there’s no extra hoop to jump through. Along the way, we track where teams are on the adoption curve:
- Explorers – Just getting started
- Accelerators – Testing and experimenting
- Advocates – Regular users who help others
The goal is to move people naturally along that curve without forcing it. In my experience, when you make AI feel like a natural part of the work, change management takes care of itself.
Data: The foundation for everything
Here's the truth nobody wants to hear: AI is only as good as the data you feed it.
We look at three things to measure readiness, our Data Readiness Score:
- Coverage – Do you have the right data?
- Consistency – Is it clean and standardized?
- Connectivity – Can your systems talk to each other?
If your score is low, AI will just automate bad processes faster. I've seen it happen: garbage in, garbage out, but now at machine speed.
From “committee mode” to real transformation
I’ve seen plenty of organizations spend months, even a year, running pilots across different parts of the business. They test multiple tools, but without a clear adoption plan, process alignment, or data strategy, everything just stalls out.
When we help them reset, we start small, often with a single department. We map roles, document SOPs, and design targeted AI agents that solve obvious pain points. These investments pay for themselves as our clients consistently report significant improvements in workflow efficiency, reduced error rates, and measurable time savings on critical business processes.
That early success changes how leadership thinks about AI. It’s no longer “a tech experiment”, it becomes “the way we work.”
AI Readiness: The questions I hear most
- How long does AI implementation take?
We focus on creating an AI first mindset and culture, which is an ongoing effort. But we also help clients begin using AI agents as soon as they’re ready.
- What's the biggest AI adoption mistake?
Treating it like a software rollout instead of an organizational transformation.
- When should companies start preparing for AI?
Now. If you wait until 2026 to build your AI foundation, you may not see results until 2027. In AI time, that's a lifetime.
Your next move
You don't need another massive strategy deck gathering dust. What your organization needs is a practical path to building an AI-first mindset throughout your culture. This shift starts with targeted implementations that deliver clear, measurable value, build internal champions, and set the foundation for ongoing innovation.
Our work is about creating real transformation, not just short-term wins. While the first results come quickly, the real power is in the cultural and operational shifts that follow. Teams begin to see their work through an AI lens, continuously finding new opportunities for impact.
The organizations that thrive in this new era won't just use AI tools, they'll fundamentally transform how they operate, making AI-enhanced decision-making and workflow optimization part of their DNA.
If you're ready to move beyond experimental pilots into meaningful transformation that reshapes how your organization works, let's talk. Because in this race, the winners won't be the ones with the fanciest tools – they’ll be the ones who built an AI-first culture that continuously delivers results.
— Larry Fisher
Co-Founder & CEO, SoftSnow AI
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