Seven Strategic Steps to Scale AI “Artificial intelligence is the new electricity.” — Andrew Ng, AI pioneer How do you adopt and scale AI effectively? The finance sector is quickly going from 0 to 100 in embracing Gen AI. Nevertheless, many organizations have fallen into the “ready, fire, aim” trap. Some are disappointed with Copilot, while others have seen poor adoption of AI, and in some cases, it simply isn’t working. Here, I lay out seven steps to successfully navigate the adoption of AI in banking and finance. The secret is that it is just as much about people and process as it is about technology. These steps will be more or less applicable based on the size of your organization and what you are trying to accomplish. Nevertheless, regardless of your size, the Artificial Intelligence Risk Inc. (AIR) software and the AI Risk team can help you lead your AI transition and complete every one of these seven steps. Seven Strategic Steps to Scale AI Source: Artificial Intelligence Risk Inc. in Banking and Finance By Alec Crawford, Founder and CEO, Artificial Intelligence Risk Inc. 1. Strategy Alignment You must align the values and strategy of your organization with your goals for AI. Adopting “efficiency tools,” broadly defined, is not a strategy. What are the top one or two priorities for your organization? Growth? Cost savings? A major business transformation, like completing a merger? AIR will help you determine the most effective way to utilize AI to achieve those goals more efficiently and effectively. We do this by leveraging our deep AI, banking, investing and finance expertise to identify the best AI model, data and tools to help you accomplish your goals. 2. Change Management The human side of AI adoption fails more than the technology side. Rolling out AI without change management is like giving the keys to your new car to a 16-year-old before he’s started Driver’s Ed — he’ll likely either crash it, or not drive it at all. We have heard stories of how new AI tools see woefully low adoption before companies choose AIR as their strategic partner for AI. Change management helps align everyone in your organization with your values and their alignment with your AI strategy. Doing this correctly should result in 80%+ adoption. For companies with more than 50 people, you will need change management to get widespread adoption of AI. (Speaking of change management, it took me four tries with AI to get the graphic in this article correct.) 3. Data Integration Without data, AI is nothing. Without your data, AI won’t be of much use to your company. The more data and apps you can connect your AI to, the more useful it will be to a wider range of people within your organization. Nevertheless, AI governance requires that AI not necessarily have access to all your most sensitive data, nor should everyone be able to access all your data via AI. These constraints are part of AI governance — an area of expertise that AIR is happy to share with you. You must own your data for AI! Owning your data is one of the fundamental tenets of the AIR philosophy. If you are giving it away to public AI models, meeting notetakers or your CRM, they will figure out ways to sell it back to you later. With the AIR platform, you can integrate all your corporate data, email, third-party tools, etc., simultaneously. This is a critical observation, because in most cases, you do not need perfect data or to set up a “data lake” or “data warehouse” before starting with AI. AIR can guide you on how to begin integrating your existing data into AI, and then how to ramp up with more or better data over time. 4. Use Case Creation Narrow use cases and AI agents are another AIR philosophy. This tenet will facilitate the adoption of AI, as people will know what to use it for. One of the biggest failures of AI is the “failure of imagination.” Also, narrow use cases reduce the risk of hallucinations and data governance issues. Colorado Banker 20
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