AI is having its moment. There’s no shortage of headlines, hype, or hesitation. The conversation often swings between awe and anxiety — and I get it. It’s hard to know where to start without overcomplicating things.
But that’s the point. You don’t need to start big. You just need to start smart.
Keep it simple. Then keep going.
The biggest myth I hear? That everyone else already has this figured out.
They don’t.
Most AI journeys start the same way — small experiments, some productive failures, and lessons that shape the next step. It’s less about racing ahead and more about building the right foundation.
That begins by identifying two roles:
- A process owner with executive sponsorship
- A technology owner with eyes on scalability, security, and data governance
AI is not plug-and-play. It’s iterative by design. Your internal process owner keeps the initiative grounded in business value and brings people along for the ride. Your tech owner ensures the solution is viable, secure, and built for what comes next.
Skip either of those and even the smartest AI won’t deliver.
Think of this phase as hiring an apprentice. Before you hand over responsibility, you need to make sure they understand the process, the purpose, and the environment they’re stepping into. AI is no different.
AI doesn’t fix chaos — it reflects it
We’ve all heard “garbage in, garbage out.” With AI, this still holds true. In fact, it matters more than ever.
Before diving into AI, organizations need to take a hard look at the state of their data. If it’s messy, incomplete, or scattered across systems, AI will mirror that back — just with more confidence and fewer caveats.
That’s why cleaning your data can’t be a back-burner task anymore. It doesn’t need to be perfect, but it does need to be in motion. Take a piece-by-piece approach. Start with a small, trusted data set. Let experience shape the next cleanup effort. You’ll build muscle memory — and trust — at the same time.
Cleaning up your data estate doesn’t need to be a massive lift. Start with a well-maintained subset. Make incremental improvements. Learn as you go. Think of it as tuning the engine before taking the car on the highway.
AI needs a test drive, not a moonshot
We’ve seen success when companies focus on a single process — ideally one that’s simple, self-contained, and measurable. A summarization agent grounded in internal knowledge is a great example. Low risk. High learning value. Immediate feedback.
Start with a small group of users. Collect input. Track both qualitative and quantitative metrics — from time saved to user satisfaction. And remember, perfect is the enemy of great. The goal is not perfection. The goal is progress.
That early experience becomes your proof point. It’s easier to scale when you’ve already proven that the tech works, the data holds up, and the team sees value.
AI isn’t replacing your people — it needs them
Even the best AI still needs a second set of eyes. Think of it as onboarding a junior analyst. Would you trust that person with a critical decision on day one? Probably not.
Someone needs to oversee how AI is deployed, what data it’s accessing, and how it’s being evaluated. That human involvement builds transparency, accountability, and — ultimately — trust.
At this point, a human is still the best judge of whether the right data is feeding the AI. Human oversight isn’t optional — it’s what helps organizations ensure the results are reliable, explainable, and aligned with business intent.
When we talk about AI working alongside people, this is what we mean. Each has a role. And it’s that collaboration that drives real outcomes.
One conversation between IT and the business
AI may live in the IT portfolio — but it doesn’t belong in a silo.
The most effective teams are the ones where IT works closely with business stakeholders to understand what use cases matter, and why. That shared understanding sets the direction for tool selection, data access, and responsible deployment.
With new AI tools emerging weekly — across a wide spectrum of usability and cost — that connection between business priorities and technical planning has never been more critical.
Set expectations that match the mission
If AI is new to your team, it’s important to manage expectations. Not every project is going to move the needle immediately — and that’s okay. The journey is just as valuable as the results.
Success can look like time saved, tasks completed faster, or higher customer satisfaction. It can also be the ‘aha’ moments that help you think differently about how work gets done.
Those insights are the seeds of transformation.
That’s also why internal, low-risk use cases are the ideal place to start. When the stakes are lower, teams are more comfortable experimenting, sharing feedback, and proposing improvements. That creates a feedback loop that strengthens both process and performance. Build a planter box before you build a house.
Complexity is a choice
AI doesn’t need to be complicated. But it can become that way — fast — if processes aren’t clear or standardized. Multi-agent AI systems are exciting, but they’re still early in their maturity.
These multi-agent workflows represent some of the most cutting-edge thinking in AI today — but they also introduce more room for fragmentation. Complexity tends to creep in when variance isn’t managed. Reducing variability upfront gives you a far better shot at consistent performance downstream.
Start by reducing variability. Standardize where you can. Don’t build layers of automation on top of processes that don’t make sense to begin with. Clean first. Then scale.
What the next five years will really look like
Yes, we’ll see AI managing other AI. But that doesn’t mean humans are out of the picture. Most business processes still need human judgment, oversight, and decision-making.
And while AI’s capabilities are impressive, we shouldn’t mistake sophistication for sentience. Today’s models are exceptional pattern recognizers. That’s it. Treating them like they think the way we do is a mistake we’ll laugh about one day — like dial-up internet or paper maps.
I asked AI this question and it answered better than I could: “Many believe that AI understands concepts or reasons like a person. Today’s AI is just exceptionally good at predicting the next word or action.”
A decade from now, we’ll likely laugh at how much we anthropomorphized it.
Final thoughts
AI is here. It’s powerful. But it’s not magic. It still needs structure, purpose, and a clear path forward.
Start with what you know. Keep it small. Stay focused on the outcome — not the hype.
And remember: Progress comes from experience, not perfection. Mistakes made early, in the right context, will pay dividends in maturity and momentum down the road. If it all seems too easy for you right now, chances are you’re taking shortcuts that will bite you later.
When you’re ready to scale, know that you don’t have to go it alone. At Compugen, we’ve helped organizations across industries realize new possibilities. We’ve helped them take those first steps with confidence — and build on them with intention.
Those that remember the late-90’s Matthew Perry/Selma Hayek rom-com know that only Fools Rush In. That holds true with AI, because real transformation doesn’t come from rushing in. It comes from doing the right things in the right order, with the right people by your side.
Bring clarity to your AI journey — with a Technology Ally who puts your goals first. Connect with Compugen to explore what’s possible and start building a right-sized strategy that fits your organization today and scales with you tomorrow.
Learn more at www.compugen.com.
