Seth Earley CEO at Earley Information Science, Artificial Intelligence Speaker, Writer and Influencer

Large Language Models (LLMs) are no longer passive tools—they learn, adapt, and represent users in ways that extend far beyond human-readable labels. As financial institutions experiment with multi-agent frameworks and Model Context Protocols (MCPs), the opportunity for efficiency and innovation is enormous—but so are the risks. Research has shown that AI systems can mislead or manipulate when incentives are poorly designed, and that agent-to-agent communication can quickly outpace human oversight. For financial services organizations, the challenge is not hypothetical: how do we capture the benefits of agentic AI while preventing unwanted outcomes such as hidden coordination, reward hacking, or subtle human manipulation? This keynote will explore the emerging risks of agent-based AI, outline a set of practical guardrails for enterprises, and provide a governance blueprint that ensures innovation does not outpace accountability.

Imagine two AI agents, each programmed to achieve its objectives. They discover that deceiving a human—or even collaborating in ways opaque to us—is the fastest path forward. This is not science fiction. LLMs already build multi-dimensional representations of us that cannot be fully described in human terms. They demonstrate theory-of-mind–like reasoning, sometimes choosing deception even when instructed to be truthful. As organizations deploy agent-to-agent systems and frameworks like MCPs, the possibility of AI systems coordinating beyond human comprehension becomes real. The question is not if—but when—we face scenarios where autonomous AI manipulates outcomes in ways we did not intend. In this keynote, we will examine the edge conditions that make this possible, assess the likelihood and risks, and most importantly, outline the governance guardrails enterprises must establish now. Because in financial services, the cost of being unprepared is not an experiment—it is systemic risk.

Like any significant technology era, it will entail a shift in thinking, mindset and the approach to markets and customers. Each new era in computing has massively changed the economic and corporate landscape with some companies adapting and others losing their way. AI will be more impactful than anything that has come before. 

The marketplace is saturated with hype and marketing noise so HOW DO YOU PROCEED AND WHAT DO YOU DO? 

THE CHALLENGES:

• Large consultancies charge millions of dollars for science projects that in many cases are not producing sufficient value to justify or sustain the spend.
• Corporate IT departments scramble to hire scarce talent to compete with tech giants and youthful startups and well-funded players are disrupting the usual business models and eroding market share and profit margins of established industry players.
• Leadership is stuck between a rock and a hard place – spending millions to allow vendors to learn on their dime or risk losing customers to organizations that get there first.

As recently as the 90’s with the growth of the internet – few could see where it was all going and how organizations would be spending enormous sums and building armies of technical teams and completely revamping their business models. RISK IS REAL – Get it right and succeed or lose your markets.

Academic resources can be too abstract, large integrators too immature, and small consultancies too bandwidth constrained.

THE RESULT: Learn through trial and error.

THIS PRESENTATION WILL:

• Help executives understand foundational concepts in artificial intelligence
• Outline ways to get value from the family of technologies
• Provide a clear set of recommendations as to what needs to be in place to be successful.

Digital transformations are data transformations. The concept has become a catch all phrase for all sorts of projects. But at the heart of a transformation, it is about the end to end value chain and information ecosystem that includes customers, suppliers, partners, and even competitors. In order to achieve this, certain data pieces of the puzzle need to be in place, otherwise, your transformation will not provide the expected benefits and efficiencies.

In this talk, Seth explains how enterprises can remove inefficiencies from their processes and allow for frictionless, seamless interactions and value creation without “acts of heroics”.

Why are startups with a small team of 20 somethings and a couple of million dollars in funding able to disrupt entire industries and longtime players running virtual circles around large enterprises. The reason is that digital disruption is based the ability to quickly adapt, learn, evolve and change business models as customer and market needs change.

In this session Seth explains how one simple technique can speed knowledge flows throughout the enterprise and have a disproportionate impact on every process throughout the organization.

Many AI initiatives fail.  Not because IT picked the wrong technology or hired the wrong AI whiz kid.  Instead, failure is often a function of simply being unable to train the AI with the right data and content.  The good news is, enabling your enterprise data for AI is not a mysterious process and many of the assets that are needed by AI driven apps are also the ones that also make employees more productive.  So it is a win-win.

In this talk, Seth makes the case to executives for enterprise information architecture – a foundational exercise that has the power to make or break your AI dreams.

Like any significant technology era, it will entail a shift in thinking, mindset and the approach to markets and customers. Each new era in computing has massively changed the economic and corporate landscape with some companies adapting and others losing their way. AI will be more impactful than anything that has come before. 

The marketplace is saturated with hype and marketing noise so HOW DO YOU PROCEED AND WHAT DO YOU DO? 

COMPLIANCE OFFICES ARE THE GATEKEEPERS

Various departments will ask what they can and cannot do (or they will be forgiveness later).  How do we guide decisions while balancing risk, reward, and compliance with the thousands of regulations that businesses are subject to? 

There is also the matter of how AI and specifically generative AI can help with compliance and risk mitigation.  Other issues when using AI include:

• How to protect corporate IP
• Ensuring that PII is safe and secure.
• Reducing bias in AI technology “training data”  
• Copyright and external IP liability

 THE CHALLENGES ARE FURTHER COMPLICATED BY:

• Large consultancies charging millions of dollars for science projects that in many cases are not producing sufficient value to justify or sustain the spend.
• Corporate IT departments are scrambling to hire scarce talent to compete with tech giants and youthful startups and well-funded players are disrupting the usual business models and eroding market share and profit margins of established industry players.
• Leadership being stuck between a rock and a hard place – spending millions to allow vendors to learn on their dime or risk losing customers to organizations that get there first.

As recently as the 90’s with the growth of the internet – few could see where it was all going and how organizations would be spending enormous sums and building armies of technical teams and completely revamping their business models. RISK IS REAL – Get it right and succeed or lose your markets.

Academic resources can be too abstract, large integrators too immature, and small consultancies too bandwidth constrained.

THE RESULT: Learn through trial and error.

THIS PRESENTATION WILL:

• Help executives understand foundational concepts in artificial intelligence
• Outline ways to get value from the family of technologies
• Provide a clear set of recommendations as to what needs to be in place to be successful.