STRATEGY · RESPONSIBLE AI · TRANSFORMATION
By Albina Vazquez – Head of Agile and Quality
Business, technology, risk, and compliance working together from day one.
An AI project cannot succeed on a good business idea alone, a good model alone, or strong controls alone. It succeeds when all four perspectives work together from the outset. At Strata, we bring together business specialists, technical data and cloud teams, and risk and compliance professionals, involving them from day one rather than in separate phases.
Today, almost every company is experimenting with artificial intelligence: a pilot here, an assistant there, a new tool every month.
And a fair question arises: how do we know we’re heading in the right direction?
Our approach rests on four ideas: start with the business, measure value, build responsibility in from the beginning, and help our clients prepare their people. These are the same principles behind our team’s certifications, including AWS Certified AI Business Strategist, and we apply them to every project with all perspectives at the table.
1. We Start with the Business, Not the Technology
The question isn’t “where do we put AI?” but “what does this process need to work better?” That means validating the use case, the baseline, and the KPIs.
Sometimes the answer is to redesign the process, organize the data, or automate a simple rules-based task. AI comes in only where it adds real value.
At Strata: We begin with an assessment lasting a few weeks, bringing business and technical teams together. This lets us prioritize use cases by impact and effort, with a realistic understanding of what they will cost to build, and make informed decisions about whether to build, buy, or partner. We also tell you when it’s better to wait or set an initiative aside.
For the business: Fewer scattered pilots and more initiatives tied to a concrete goal.
2. We Measure Value from Day One
An AI project without a baseline cannot be evaluated: if we don’t know how long something took or how much it cost before, we can’t say what improved.
At Strata: We define the indicators with your team before implementation so that every initiative has a measurable before and after.
| What to Measure | Example | Why It Matters |
|---|---|---|
| Baseline | Hours per task before AI | Provides the point of comparison |
| Tangible benefits | Time savings, lower costs, increased revenue | Can be translated into monetary value |
| Intangible benefits | Better customer experience, fewer errors | Real value, even if it doesn’t show up on an invoice |
| Early indicators | Adoption and usage in the first few weeks | Show whether the project is on track before the final outcome |
| Costs | Consumption-based, per-instance, or per-user pricing models | Help avoid surprises when scaling |
For return on investment, we use a simple formula:
ROI = (benefits − cost) / cost × 100
For the business: AI stops being an innovation expense and becomes an investment with verifiable results.
3. We Build Responsibility into Planning
Responsibility isn’t added at the end of a project; it’s designed from the beginning. Fairness, explainability, privacy, security, transparency, and robustness are decision-making criteria, not boxes on a compliance checklist.
At Strata, we address it on three fronts:
- Governance and compliance by design. Risk and compliance specialists work alongside technical teams from the planning stage. We define who can access what, how each use case’s risk is classified, and what your industry’s regulations require—before writing the first line of code.
- Human oversight. We design processes so people can review, correct, and escalate decisions, especially when those decisions affect others.
- Post-launch monitoring. We define what to observe and when: bias, hallucinations, model drift, and data degradation.
For the business: Lower regulatory and reputational risk, and greater trust from customers and teams.
4. We Prepare the Organization, Not Just the Model
The hardest part of AI is rarely the model itself; it’s preparing the organization to use it well.
That’s why our support goes beyond technology:
- We help assess maturity in leadership, data, culture, infrastructure, and governance so gaps are visible before investing.
- We help organize data: our technical teams address quality, access, silos, and data ownership. Without trustworthy data, AI answers quickly—but incorrectly.
- We support change through transparent communication about the impact on work and training that helps roles evolve.
- We scale in stages: proofs of concept, quick wins, and moving from pilot to production, with a contingency plan.
For the business: Adoption that lasts because people understand and trust the technology.
5. Four Questions We Answer Together
Before approving any AI initiative, we work with your team to answer four questions:
- Value: What business problem does it solve, and how will we measure it?
- Data and technology: Do we have quality data and the right solution?
- Risk and responsibility: What could go wrong, who oversees it, and what do we monitor?
- Organization: Are the people, leadership, and culture ready to adopt it?
If one of the four has no answer, it’s not a definitive “no”: it’s a signal of what needs to be addressed first. Because each question calls for a different kind of expertise, at Strata we answer them together—with business, technical, risk, and compliance professionals working as one team.
Is Your Company Evaluating an AI Project?
At Strata, we bring business, technology, risk, and compliance together in one team so you can move forward with a clear path. Get in touch and let’s talk.
About the Author
Albina Vazquez is AWS Certified AI Business Strategist and Head of Agile and Quality at Strata Analytics Group, a data analytics and artificial intelligence company that helps Latin American organizations develop accessible, context-aware, and culturally adaptable AI systems across the telco, finance, retail, and healthcare sectors.
