How to Build a Scalable, Trustworthy AI Strategy

Generative AI (GenAI) is transforming how we do business. The possibilities are vast, from customer support automation to intelligent forecasting and predictive maintenance. But for every success story, there are dozens of stalled pilots and unrealized promises. What separates organizations that scale AI from those that stay stuck in the sandbox? It starts with asking the right question: Why are we doing this?

I’ve seen firsthand how the most effective AI strategies don’t begin with a tool, a model, or a vendor; they start with a business outcome. In our work at AtScale, helping global enterprises make analytics and AI scalable, trustworthy, and self-service, we’ve learned that the foundation of AI success lies not in technology alone but in alignment between people, data, and purpose. As AtScale CTO Dave Mariani said during a recent panel with top analysts at the 2025 Semantic Layer Summit,

“We’ve realized that we lost something when we gave up early-binding models and let everyone ‘roll their own’ metrics. That created chaos. The semantic layer brings back structure, consistency, and trust.”

In this blog, I’ll explain how to build an AI strategy that prioritizes clarity, governance, and accessibility so your organization can move beyond AI experimentation and into long-term impact.

A Strategy That Starts with the Business

If you’ve ever sat through a vendor pitch that started with “Here’s what our AI can do,” you know the problem.

Too many AI initiatives begin with a hammer in search of a nail. But the best strategies flip the script; they start with the nail.

What specific problems are we solving?
What inefficiencies or missed opportunities are costing us?
What metrics would tell us we’re on the right path?

Your AI strategy should emerge from the business need, not the capabilities of the latest algorithm.

At the recent 2025 Semantic Layer Summit presented by AtScale, our CTO, Dave Mariani, moderated a panel with industry analysts Andrew Brust, Founder and CEO of Blue Badge Insights; Sanjeev Mohan, Principal of SanjMo and former Gartner Research VP of Data and Analytics; and Prashanth Southekal, PhD, MBA, ICD.D, Founder and managing Principal of DBP Institute. A recurring theme was the need for AI to be tied to business outcomes from the start. As Dr. Prashanth Southekal put it:

“The biggest challenge we have today isn’t collecting or cleaning data, it’s the last mile of analytics. Converting insights into decisions. That’s where the semantic layer plays a critical role.”

Without a clear, shared understanding of what AI is supposed to accomplish, your teams will build impressive things that don’t actually move the needle.

Step 1: Define the “Why” Behind Your AI Effort

Before anything else, your leadership team must define your AI vision. This means aligning on tangible, measurable goals rooted in business priorities.

Examples:

Reduce churn by 10% through predictive modeling
Automate 30% of customer service tickets via AI-powered chat
Improve forecast accuracy across product lines

Treat AI as a business initiative, not an IT project. That reframing alone can change how the organization approaches the work ahead.

Step 2: Align Your Data and Technology for Trust

Even the best AI model is useless if trained on inconsistent or siloed data. The foundation is high-quality, governed, and accessible data. Most organizations don’t suffer from a lack of data; they suffer from fragmentation and ambiguity. Finance and marketing use different definitions of “customer.” Metrics vary across tools. Insights conflict.

This is where a semantic layer becomes indispensable.

By establishing a single source of truth, a semantic layer ensures that AI systems and those using them operate from consistent, trusted data. It abstracts the complexity of raw data, applies business logic, and makes information consumable by both BI tools and large language models (LLMs). Sanjeev Mohan summarized it well in our panel:

“The center of gravity is shifting away from dashboards to the semantic layer—because that’s where definitions live. That’s how you enable natural language queries and trustworthy answers.”

And as we move toward natural language interfaces, chatbots, voice commands, and AI agents, the need for that trust becomes even greater.

Step 3: Embrace Natural Language as the New Interface

Let’s talk about Natural Language Query (NLQ).

We’ve seen a major shift in 2024 and 2025. Business users don’t want to learn SQL or rely on data teams to answer basic questions. They want to ask a chatbot, “What were Q1 sales in EMEA?” and get an accurate answer instantly.

Here’s the challenge: Natural language is ambiguous. Business questions often rely on implicit assumptions. For an LLM to return reliable results, it must be grounded in a shared understanding of your data.

A semantic layer enables that grounding. It acts as the connective tissue between raw data and real-world meaning.

At AtScale, we’ve integrated with LLMs like Databricks Genie and Snowflake Cortex Analyst. Our internal benchmarks have improved accuracy by up to 95% when LLMs are paired with a semantic layer versus querying raw schemas. But as Andrew Brust warned during the Summit:

“Generative AI introduces risk. It’s confident, but not always accurate. A semantic layer provides the discipline and disambiguation necessary to avoid costly mistakes.”

For business leaders, this matters. Dashboards with bad numbers can tank strategy decks. LLMs with hallucinations can destroy trust. Governed NLQ is the path forward. And it only works with a transparent semantic layer in place.

Step 4: Build a Cross-Functional AI Team

AI implementation isn’t a solo sport. You need collaboration across:

Business stakeholders (to define success)
Data engineers (to ensure integration)
Data scientists (to build models)
Analysts and product owners (to translate insights into action)

Equally important is who builds the semantic models that feed your AI systems. Traditional IT teams often lack the domain expertise to define business metrics, while business users often lack the tools or training to model data safely.

AtScale and others in this space support a center-of-excellence (CoE) approach: a joint team that spans business and IT, establishing modeling best practices while empowering teams to self-serve.

Step 5: Start with High-Impact, Low-Risk Use Cases

Don’t try to do everything at once. Your AI journey should follow a crawl-walk-run path:

Start with pilots that are measurable and have a clear owner.
Choose problems with clear ROI, like churn prediction or service automation.
Validate your data pipelines and semantic model during this phase.

This allows you to test your assumptions, strengthen your foundation, and gain executive confidence. And importantly, your semantic layer grows with you. Start with a handful of metrics and build a modular library over time, which we call composable modeling.

Step 6: Prioritize Governance and Responsible AI

We’ve talked about NLQ accuracy. But AI governance goes beyond that. As your usage scales, you need to ensure:

Data privacy and compliance with regulations like GDPR
Transparency in how models make decisions
Bias mitigation through explainability and feedback loops
Access control to sensitive insights

A semantic layer supports these needs by acting as the enforcement point for:

Metric definitions
Lineage tracking
Role-based data access

And here’s the reality: your risk multiplies if your data definitions vary. Inaccurate models don’t just waste time; they can cost millions.

Step 7: Scale with Standardization and Flexibility

Once your pilot programs deliver value, the goal is to scale without chaos. That means:

Standardizing metrics across departments
Federating semantic modeling (e.g., letting teams extend core definitions)
Supporting multiple consumption layers (BI, LLMs, APIs)

AtScale’s semantic layer supports this through tool-agnostic access, meaning the same logic works across Power BI, Tableau, Excel, Python, and LLMs. We’ve spent 12+ years building that interoperability because we know business users won’t sacrifice functionality. They want the full Power BI experience and the full Excel experience, but with consistency and governance built in.

Step 8: Drive a Culture of AI Adoption

You can have the best data and tools, but fail if no one uses them. That’s why building an AI-ready culture is the final step. This involves:

Executive sponsorship that aligns AI with strategic goals
Ongoing training for business users to leverage new tools
Incentives for teams that adopt and scale AI use cases

A semantic layer helps by reducing the friction to access data, empowering non-technical users to self-serve, and ensuring that the answers they get are trusted.

Final Thoughts: Start with the Business, Scale with Confidence

As someone who’s worked with data-driven organizations for over a decade, I can say this with certainty:

AI success doesn’t start with technology. It starts with clarity.

Get your teams aligned on the “why.” Define the metrics that matter. Ground your data in a semantic layer. And give your teams the tools and trust they need to innovate responsibly.

If you’re navigating the path from experimentation to enterprise-wide AI, consider whether your data foundation is ready.

Because the truth is: you can’t scale what you can’t trust.