Building Trustworthy AI for Enterprise Workflows - with Amar Akshat of PaySafe

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Amar Akshat, SVP and Chief Architect at Paysafe. Amar joins us to discuss bridging the consistency gap to move AI from risky demos to reliable production systems. He explains why embedding guardrails and evaluations early is the only way to protect AI sponsorship and ROI. Our conversation also covers shifting to a know-your-agent workflow, treating prompts as version code and ensuring every agentic decision remains fully auditable and defensible. The views expressed by Amar on today's program do not reflect that of Paysafe or its leadership. Position your brand alongside the Fortune 500 leaders defining the enterprise AI roadmap. For the opportunity to showcase your solution to the executives currently funding and scaling global initiatives, partner with Emerge to reach the decision makers holding the strategic mandate. Secure your partnership at go.emerge.com/partner. That's go.emerge.com/p-a-r-t-n-e-r.
Let's get into our conversation with Amar. Amar, welcome to the AI in Business Podcast with Emerge.

**Amar Akshat** (1:17)
Hey, Yolande. Thanks for having me.

**Daniel Faggella** (1:19)
It's a big pleasure. So I know that our conversation today will speak directly to senior executives from multiple industries. And we know that one of their biggest concerns will always be trust. Now, for an executive, trust is not an emotion or a feeling for the rest of us. To them, it's having that certainty that they are avoiding lawsuits, regulatory fines, PR disasters.
And with AI, all of that fear comes, right? So when we consider enterprise AI programs, where do we see the trust breakdown? Where do we find the moments where leaders realize that the systems behave differently in the real world than what they did in the very controlled demo that we normally see?

**Amar Akshat** (2:02)
So when I look at enterprise systems and large enterprise systems especially, I realize that it is rarely the trust breaks because the LLM is bad or the LLM has a lower quality or low bar in that sense. And even if it was that, LLMs are becoming better on a minute by minute basis. Most of these trust parameters break because of the systems which are using the LLM or the wrappers around it are immature. And that is what I sometimes call as the consistency gap. The LLMs are also distributed systems and they must be consistent across different business inputs and outcomes. Otherwise, it is not a production system, it is just a demo system really.
So, trust collapses the moment predictability and determinism disappears.
And that is why it is very important that we embed guardrails and evaluations early in the process. It cannot be an afterthought. Most of the demo-based systems today are a result of an outcome, a result of an excitement from the teams which are building it, experimenting with AI. And often, just like the older world, the authentication, the guardrails, the evaluations, the testing of it, the pipelines, the data set management is kept to when we go production. But then, if you do not have these things before production or use those things to even classify the results, then your predictability will break and your trust will break.

**Daniel Faggella** (4:01)
Absolutely. It sounds like a very basic concept of just predictability and knowing what's happening and knowing where it's going. So unpredictability is obviously a big risk. And I recently heard you say that blocked access creates blocked visibility. Is that also something that could have an effect on the consistency gap or not really?

**Amar Akshat** (4:20)
Definitely. The systems need to be able to discover all the dimensions available for its workflow today. If you block access or if you hide visibility into systems, both of them will cause problems. If you block access, a lot of shadow AI may come up on developers or users' desktop.
And eventually, they will find merit and they will find usefulness in those shadow AIs that they would want to make it production ready. At that point, you are unequipped to quickly find the right solution or find all the consistency parameters to qualify that AI solution. If you block visibility, if you do not start observing these things at an early place, if you just go with the right solution outcomes, and if you keep it a demo system until you make a production, then you are too far in the process. The exec suite is really excited about the solution, but then they realize we don't have determinism, we don't have verification baked in, we don't have evaluations baked in, and then they feel that, well, the AI is not that fast enough.
So the sponsorship will be lost for AI as soon as the first production failure happens. It's very important for us to know that.