Why Leadership Needs a Clear Understanding of What AI Is and What It Isn't

In the summer of 2024, JPMorgan Chase rolled out an internal AI platform called LLM Suite. Within eight months, 200,000 employees were using it. Investment bankers who once spent hours building pitch decks were producing them in thirty seconds.

This is not a hypothetical scenario. This is one institution, moving at scale, while much of financial services is still debating whether to fund a pilot.

The chief actuary raises the idea of machine learning and is told to focus on reserves. The CIO proposes a proof of concept and loses the funding to a platform upgrade. The terms feel abstract. The urgency is unclear — until a competitor makes it undeniable.

Insurers, asset managers, and corporate finance teams are already deploying AI across pricing, underwriting, fraud detection, and customer engagement.

The question facing senior leadership is no longer whether AI will reshape your industry. It already is. The question is whether you will lead that transformation or be overtaken.

This twelve-part series is designed for one purpose: to give you, as a senior leader, the conceptual foundation you need to make informed, confident decisions about artificial intelligence. You do not need to write code. You do not need to understand calculus. What you need is clarity — and that is exactly what we will build together, starting now.

Strip away the hype and the science fiction, and AI becomes surprisingly intuitive to understand.

Artificial intelligence follows the same principle, compressed into silicon. An AI system is software that has been exposed to large volumes of data and, through mathematical processes, has extracted patterns from that data. When presented with a new input, the system uses those patterns to produce an output: a prediction, a classification, a recommendation, or in the case of generative AI, entirely new text, images, or code.

The critical distinction is scale and speed. Where you might have reviewed a few thousand financial reports across your career, an AI model can process millions. Where your pattern recognition is shaped by personal experience and cognitive biases, an AI model's patterns are shaped entirely by its training data — for better or for worse.

If two analysts are trained at different firms with different philosophies, they will evaluate the same deal differently. The same principle applies to AI.

A model trained on general internet text can discuss nearly any topic but lacks deep expertise in any one domain. A model trained specifically on regulatory filings, actuarial tables, and insurance policy language will be far more precise in that domain but may struggle with tasks outside of it.

This is where fine-tuning becomes relevant. Organizations can take a general-purpose model and further train it on proprietary data to specialize its behavior. A life insurer, for example, could fine-tune a language model on its claims correspondence, policy documents, and underwriting guidelines so that the model produces outputs aligned with the organization's specific terminology and business logic.

The data you feed an AI system determines the quality and relevance of what you get back. Poor data does not produce poor results by accident. It produces them by design.

Investing in AI without first investing in data quality is like hiring a brilliant analyst and handing them a filing cabinet full of incomplete records.

AI's capabilities are impressive, and the pace of advancement is genuinely remarkable. But the gap between public perception and technical reality creates real risk for organizations that fail to understand the boundaries.

Because AI models learn from data, they inherit whatever biases exist in that data.

The risk extends further. Training data can be manipulated — either intentionally by bad actors or unintentionally through poor data governance. A model trained on incomplete, outdated, or corrupted data will produce outputs that reflect those deficiencies.

For regulated industries like insurance and financial services, this is not an abstract concern. It is an operational, reputational, and legal risk.

AI models are trained using several approaches, each suited to different types of problems. We will explore these in depth in the next article, but a brief introduction will ground you in the landscape.

Each method has strengths and trade-offs that influence where and how AI should be applied effectively. Article two will give you the depth to evaluate these approaches for your own organization.