Digital Transformation and AI in the Industry
AI is changing how industries build products, improve processes, and serve customers.
But the biggest lesson from this discussion is simple: don’t start with AI, start with the problem.
Start With The Problem, Not The Technology #
AI is exciting, but that excitement can make teams rush into projects without knowing what they want to improve.
Fabiana Piazza puts it clearly:
The first big challenge is always to define the problem to solve. Everybody wants to implement something with AI, but I always step back and say: What’s the problem we want to solve?
Before starting an AI project, ask:
- What customer or business problem are we solving?
- Which KPI should improve?
- How will we measure success?
- Does AI actually help solve the problem?
This approach is useful for both large enterprise projects and small developer experiments. A technically impressive application that solves no real problem won’t create much value.
AI Accelerates Transformation, It Doesn’t Replace It #
Digital transformation isn’t new. AI is making many parts of it faster.
Fabiana explains that modern AI enables faster prototypes, quicker testing, and faster iteration. This is especially important in industries where development and testing traditionally take months.
However, AI can’t fix a broken process by itself:
AI empowers a solution, but it’s not the ultimate solution.
For developers, this means you shouldn’t add an AI feature just because competitors have one. First understand the workflow, then find where AI can genuinely improve it.
Measure Results With Real Data #
One of the strongest examples from the discussion involved engineering data.
A team used AI to reduce a process from four months to five weeks. Instead of immediately applying the solution across the entire organization, they started with a focused pilot, measured the results, and expanded only when the numbers looked good.
That gives developers a practical model:
- Pick one specific problem.
- Define measurable success criteria.
- Build a small prototype.
- Measure time, cost, quality, or productivity.
- Expand only when the results justify it.
This is much safer than launching an AI project across an entire company based on hype.
Don’t Ignore AI’s Risks #
More AI applications also mean more things to monitor. A chatbot or AI agent can improve productivity, but a poorly controlled system can damage the customer experience.
Fabiana shared an example of an AI assistant that scheduled a customer appointment when the business owner wasn’t actually available. The technology worked as designed, but the overall solution failed.
That’s an important distinction: a working AI system isn’t necessarily a successful product.
Developers should think about:
- Human approval for sensitive actions
- Clear limits for AI agents
- Monitoring and logging
- Security and privacy
- Cost of running models
- What happens when the AI is wrong
Think Smaller Before Thinking Bigger #
The future of AI may not be about putting the biggest possible model everywhere. Smaller, specialized models may make more sense for specific tasks, especially when cost, speed, privacy, or infrastructure matters.
For developers, the best strategy is to stay flexible. Learn how different models and architectures work, but keep the business problem at the center of your decisions.
The Developer’s AI Mindset #
The most useful takeaway is not a specific AI tool or model. It’s a way of thinking:
Problem → Measurement → Small Experiment → Results → Scale
AI can help teams move faster than ever, but speed without direction can simply create bigger mistakes, faster.
As Fabiana says:
How do we actually execute? How do we actually measure and bring this benefit?
That’s the mindset developers need in the AI era: build less for the hype, and build more for measurable impact.