30 Apr 2026
4 min read
AI innovation is picking up speed, and so are the risks that come with it. As AI becomes easier to adopt, organizations are under pressure to move quickly while still being thoughtful and responsible.
In a recent TAG Data Talk episode sponsored by Wavicle, Dr. Beverly Wright spoke with Rohit Lal about how leaders can pursue AI innovation while putting the right controls in place.
This blog shares five lessons from their conversation on balancing curiosity with accountability and innovation with integrity.
Speaker details
- Host: Dr. Beverly Wright — Executive in Residence, Institute for Insight, Georgia State University
- Guest speaker: Rohit Lal —Chief Information Officer, Saia
1. AI is powerful but needs control
AI can deliver valuable insights, but it is not always predictable. Rohit explains that models can change over time, introduce bias, or produce results that are difficult to validate. Without proper oversight, organizations may place trust in outcomes that are not always accurate.
“It’s hard to validate, and how do you know it’s the right answer? Over time, if it’s self-learning, you might land up with the wrong answer or even hallucinations.”
Responsible AI starts with visibility into how systems work and regular checks to ensure results remain reliable.
2. Not every problem needs AI
Rohit stresses that one of the most common mistakes teams make is assuming AI should be used everywhere. AI is only one tool among many, and simpler approaches can often work better.
“AI is a tool. Not everything is an AI solution waiting to happen.”
By focusing on the problem first, organizations can avoid unnecessary complexity and risk.
3. Start internally before going external
Customer‑facing AI requires a more cautious approach, as errors can directly affect trust and reputation. Rohit explains that internal use cases offer a safer environment to learn, test, and refine AI capabilities.
“We’re trying to be much more cautious about anything that’s customer or outside facing because if you give the wrong answer, you’ve got a reputational risk.”
By starting internally, teams can experiment and adjust while limiting potential impact.
4. Governance and guardrails are essential
As AI becomes more accessible, more teams begin using it, sometimes without full visibility. Rohit emphasizes the need for clear rules around data usage, ownership, and oversight.
“Where does the data reside, who owns the data, and what are they doing with the data?”
Clear governance and simple guardrails help organizations innovate responsibly without losing control. Responsible AI is not just about guardrails up front; it also requires ongoing validation and regression testing as models and inputs evolve.
5. Balancing innovation and responsibility is a shared effort
Responsible AI cannot sit with one individual or role. Rohit explains that AI affects many parts of the organization, which means decisions must be shared across business, technology, data, and leadership teams.
“It’s a partnership. It’s not an individual.”
When ideas flow from across the organization and priorities are aligned at the leadership level, teams can innovate while staying grounded.
Final takeaway
AI is evolving quickly, and the pressure to innovate is real. But moving too fast without clear guardrails can create more risk than value. Organizations that treat AI as a tool, start with real business needs, learn in controlled environments, and share responsibility across teams are better positioned to innovate with confidence and integrity.
Responsible AI is not a one-time approval step; it requires ongoing testing, monitoring, and adjustment as models and business conditions evolve.
Watch the full podcast below:
If you’re planning to modernize your data platform while adopting AI responsibly, Wavicle can support you. Connect with us to explore clear, outcome-driven approaches to data and AI transformation.
Disclaimer: Quotes in this blog are excerpted from a longer conversation and have been edited for length and clarity.
