Executive Spotlight: Michelle Bonat of AI Squared is Unlocking the Power of AI for Enterprises
In our first edition of Executive Spotlight, we turn our attention to Michelle Bonat, Chief Technology Officer of AI Squared—an emerging player in the field of enterprise AI solutions. With a genuine passion for technology and a deep understanding of the transformative potential of data and AI, Bonat has carved out a reputation as a respected industry leader.
We asked Bonat to share the journey of her career, from her early days as a software engineer to her impactful role as the head of AI innovation at Chase Bank. In this interview, you’ll gain valuable insights into her perspectives on the future of AI, the intriguing realm of Generative AI, as well as her vision for the short-term and long-term success of AI Squared and any technology company working in AI.
You're very passionate about data and AI—how did you first become interested in those areas?
MB: I started my technology career in software engineering in product management, without really leveraging data and AI. I was able to scale technology to global reach for large enterprises at Oracle leading financial web applications globally. Later as I became a data scientist and saw the possibilities there, leading my own fintech, it was a natural fit to blend the benefits of data science with software. As the head of AI innovation for Chase Bank, and later as a CTO leading new AI and data technology and scaling these efforts at Chase, I appreciated first hand how even the largest and most sophisticated organizations can accelerate their AI journey.
Out of all the opportunities out there, why did you choose to work for AI Squared?
MB: My goal personally and for any company I’m helping is to create the most impact, with a step change improvement in the status quo…unlocking the power of AI for the entire enterprise. With AI Squared, I saw an early-stage company that could rise to meet this opportunity.
Tell us about your first impression of the company and your new team.
MB: I’ve been very impressed with the energy and dedication of the team. The enthusiasm to go the extra mile for their customers is really great to see. I’ve also been impressed with the team’s ability to seek out and incorporate learnings and feedback quickly along the journey.
Where do you see AI headed in the future?
MB: We’re in the midst of the “iphonification” of AI. What that means is that for the first time for many people they can see and use AI right in their hand. What we also see materializing quickly is the enterprise uptake of AI and specifically for Generative AI (GenAI). GenAI is a type of artificial intelligence that is capable of generating text, images, or other media in response to prompts. These GenAI models learn the patterns and structure of their input training data, and then generate new data (responses) that have similar characteristics, powered by LLMs (large language models).
“Soon every enterprise will need to be a data and AI company…An enterprise’s proprietary data is their most defensible moat. It is what sets them apart and can differentiate them and help them win.”
AI for the Enterprise: We’re entering the golden age of productivity for enterprises, powered by AI. Soon every enterprise will need to be a data and AI company, whether they want to be one or not. It can help drive change and enable business. An enterprise’s proprietary data is their most defensible moat. It is what sets them apart and can differentiate them and help them win.
The opportunity is that business operations create a lot of knowledge (across text, images, audio and more), but previously it’s been difficult to consume all that knowledge when you want to get a job done or make a decision. This data is locked in silos and formats that are difficult to find and consume. The use cases and opportunities here are vast. From content personalization, autonomous coding, summarizing documents, review automation, Q&A on specialized topics, powering customer recommendations, monitoring cyber threats and logs, and more. The list goes on and on.
I see the uptake of AI and specifically GenAI in the enterprise as one of the quickest developing trends I have seen in a very long time. It’s extremely exciting the pace at which innovation is happening here.
What does success look like to you in the short term and long term at AI Squared?
MB: The AI market especially in the enterprise is still in the early chapters. Today we’re seeing lots of attention around generative AI (also referred to as conversational AI or chatbots). We’re also hearing the need to accelerate the delivery and measurement of AI projects, and a focus on measuring and driving ROI for AI projects, particularly in this economic environment. In the future, I expect that the pace of technology innovation will continue to be even more rapid with more AI innovations coming to market. Large and small organizations will be able to harness these innovations and accelerate their data and AI journey through the use of technology.
Success for AI Squared in the short and long term looks like developing technology that enables organizations to capitalize on AI (and individuals, with our community edition) in a way that meets their business needs and solves their problems.
The success of any AI is its ability to solve customer problems. In business these problems typically aren’t new. How should I convert a prospect to a customer? What should I recommend to my customers to meet their needs? In general, what is the optimal decision based on a large amount of data? However, it’s bringing in the ability to enable organizations to leverage AI. The most important part of the equation is meeting the business need in a usable and lovely way.
“You could have the most accurate machine learning model in the world, but if your end users don’t trust it or aren’t comfortable using it, the adoption will be low. Understanding and providing for cultural change needed to embrace AI is a critical part of AI success.”
Further, an important piece of success for AI Squared and any technology company working in AI is to manage the culture change well around AI. You could have the most accurate machine learning model in the world, but if your end users don’t trust it or aren’t comfortable using it, the adoption will be low. Understanding and providing for cultural change needed to embrace AI is a critical part of AI success.
This includes having the end user be part of the process and giving them an opportunity to give feedback on machine learning results, where that feedback is incorporated into the machine learning models to improve their experience and decision making.
Learn more about AI Squared at squared.ai. Stay up-to-date with the latest news on Ridgeline’s portfolio by subscribing to our monthly newsletter!