As enterprises race to implement AI solutions across their operations, a critical challenge is emerging: the disconnect between technical teams and governance stakeholders threatens to derail even the most promising AI initiatives.
Without proper testing frameworks in place, organizations risk lengthy deployment delays, compliance issues, and missed business opportunities.
Shannon Kehoe, Head of Innovation at QuantPi, offers valuable insights into how bridging this gap through robust AI testing can transform implementation outcomes and drive tangible business results.
Building Trust Through Rigorous Testing
At the heart of successful enterprise AI deployment lies the critical issue of trust between technical teams and governance stakeholders.
Shannon identifies this as a fundamental challenge organizations face when implementing AI solutions:
One of the biggest things that we’ve seen is a lack of trust between the technology expert teams, the data scientists, and the AI governance teams, which could be compliance, legal, or the business side.
There tends to be a lack of understanding on each side of what they’re doing. And one of the ways to solve that is through extensive testing.
According to Shannon, this trust deficit can greatly slow down AI deployment, ultimately affecting an organization’s bottom line.
By implementing robust testing frameworks, companies can accelerate their path to production while maintaining necessary governance standards.
From Theory to Real-World Value
The tangible benefits of proper AI governance extend beyond compliance considerations. Kehoe shares an example of how improved AI testing helped transform business outcomes for a predictive maintenance company.
Konux is a predictive maintenance company for railways that doesn’t charge customers if their accuracy isn’t above 90%.
As Shannon explains:
They were having some edge cases that the data scientists didn’t get time to address. They couldn’t charge for these edge cases, but if they could solve them, there was real value there.
Through QuantPi’s testing platform, the company was able to “find the flaws in the dataset or in the model, rectify them, and then deploy quickly to production,” allowing them to tap into previously unrealized revenue streams.
The Universal Value of Well-Tested AI
While some industries may seem more natural candidates for sophisticated AI testing platforms, Shannon emphasizes that the benefits are widely applicable.
A well-tested AI benefits everyone, right? That goes from e-commerce to finance to energy infrastructure. When you test the AI before it goes to production, before it goes live for customers, you have a better idea of who you’re serving and who you’re not. And you’re therefore able to eke more value out of the model when it gets to production.
The Future of Enterprise AI: Smaller, Smarter, More Autonomous
Looking ahead, Shannon foresees significant technological shifts that will reshape enterprise AI implementation over the next three to five years.
The trend toward more efficient and compact models is already evident in developments like distillation techniques and quantized models that maintain performance while reducing computational demands.
This aligns with industry-wide efforts to make AI more sustainable and accessible beyond tech giants with massive computing resources.
This reduction in computational requirements will likely drive another key development – the proliferation of truly autonomous AI agents.
The Essential Role of Cross-Functional Understanding
For organizations seeking to implement AI effectively, bridging technical and business perspectives remains critical for success. This cross-functional alignment must extend across data infrastructure, strategic planning, talent development, and change management.
A 2024 McKinsey Global Survey found that organizations with strong cross-functional AI teams were 2.5 times more likely to report significant value from their AI investments than those working in technical silos.
While testing and governance create crucial foundations for AI success, they represent just one piece of a complex implementation puzzle. Research from MIT Sloan suggests that the most successful AI implementations balance technical excellence with organizational readiness, including clear data strategies, executive sponsorship, and thoughtful change management.
Organizations that proactively address these factors early in their AI journey avoid the exponentially larger challenges that emerge when scaling poorly designed solutions – reinforcing the wisdom that addressing implementation holistically from the start prevents firefighting later.
