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Rajesh Iyer
Global Head of ML & Generative AI for Banking, Capital Markets, P&C, Life & Healthcare at Capgemini
Rajesh Iyer is a prominent figure in the field of artificial intelligence and machine learning, currently serving as the Global Head of Machine Learning and Generative AI for Financial Services at Capgemini. In this role, he focuses on transforming enterprises by leveraging data and AI to enhance business outcomes. His expertise encompasses a wide range of financial services, including banking, capital markets, and insurance.1
In addition to his role at Capgemini, Iyer is also an Advisory Council Member at Harvard Business Review, where he contributes insights into the intersection of technology and business strategy. His professional background includes significant leadership positions, such as being a member of the Forbes Technology Council and previously serving as Senior Vice President and Head of Data Sciences at Xceedance.1
Iyer's approach emphasizes the integration of AI across various business domains, aiming to improve efficiency, customer experience, and revenue growth. His extensive experience in advisory consulting for the financial services sector allows him to engage effectively with senior executives and stakeholders.1
Highlights
Paternalistic, Prodigal & Pernicious Approach to Human Capital Return on Talent for Enterprises Requires a Rethink
I have been thinking and talking about this a lot but see the picture even more clearly right now . . . and right when I am about to doze off for the night.
In the picture below, we can see that both enterprises and Big Tech are filled with energetic talents pursuing all manner of ideas (dreams), with some even being awesome. There is a difference from that point on though:
Enterprises bet on naked ideas as talents don’t get to experiment with ideas and bring the results to funding discussions.
BigTechs allow and even facilitate experiments with ideas and end up bankrolling just those experiments that are promising or proven.
The BigTech approach is vastly more powerful as it brings more ideas to life so that value realization can happen faster and cheaper. The lesson is as simple as it is old - Experiments and high levels of failures need to be tolerated to drive value maximization.
This is especially true in AI where a majority of use cases don't easily work out for one reason or another. The alternative is scary; where a small number of blessed ideas refuse to die as there are no better ideas to crowd them out of existence.
In the end, AI@scale is dependent on high levels of low-cost or no-cost failures.
#ml #ai #GenAI4FS #inc81starch
🔥 Kill the Endless Cycle of Data Capture—Let Gen AI Take Over! 🚀
We’ve been stuck in a rut, pouring endless resources into capturing structured data in our core systems. Every time a new business need pops up, we’re back to square one - trying to fit it more data (field and/or grain) into our systems. But here’s the thing: this endless cycle isn’t just exhausting; it’s unsustainable.
What if we could break free from this grind? What if, instead of constantly chasing after more structured data, we let Generative AI create that data for us?
Gen AI for Data, with structured outputs in APIs, is the revolution we’ve been waiting for. Rather than continuously upgrading our core systems to capture and manage new data elements, we can use Gen AI to generate the high-granularity data we need, directly from the unstructured information that we already have available.
Let’s be real—it’s simply not feasible to keep investing in capturing structured data every time our analytics needs change. The solution? Flip the script:
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End Data Capture Madness: Instead of trying to capture every possible data point upfront, let AI generate the structured data you need from the unstructured notes, descriptions, and free-form fields already in your systems.
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Unencunber Data Creation: No more waiting for the next data capture project. Gen AI can analyze unstructured text and produce the exact structured data needed for your analytics, for your needs.
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Keep Core Systems Simple Stop overloading your core systems with more and more structured data. By shifting the data creation burden to AI, you keep your systems lean and agile, reducing the complexity and cost of maintenance.
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Be agile in Decision-Making: With AI-generated data, your analytics and decision-making processes become more responsive and adaptable, allowing you to keep up with changing business needs without constantly reworking your data capture strategy.
#ml #ai #GenAI4FS #inc81starch
