“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.
- 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. - 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.) - 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.
- 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.Less is more when you are working with AI agents, because less means more guardrails. Without defined use cases aligned with your values and strategy, AI will be stalled in “efficiency” mode — helpful, but only realizing a fraction of its potential. You’ll probably also experience low adoption rates as people try it, then revert to their old habits. The key to increasing ROI across your entire organization is working with each team, deploying agents that work for them, then building out more complex and customized use cases over time.
- Real-Time Risk Management of AI
Stop AI problems in real time. Small problems can become big problems in minutes — whether cybersecurity, emergent behavior or a malicious actor inside your AI platform. Real-time AI risk management monitors all AI prompts and responses, agent actions, etc., looking for prompt injections and other types of attacks, unauthorized use, emergent behavior and hallucinations. At some point, you may need to redact confidential or personal information. An integrated reporting system helps you manage everything in real-time. - AI Lifecycle Governance
Capture and catalog all model use cases, changes and activity. Having an updated AI inventory, including owners, is critical as AI expands from single-use cases to company-wide adoption. Additionally, having a record of AI usage, changes and activity can help you comply with financial services regulations, including the GLBA, SEC/FINRA, and the OCC, as well as the NIST AI Risk Management Framework (RMF). - Innovation
AI is not standing still, and neither will your AI solutions. Taking advantage of new AI capabilities requires constant innovation. As your corporate strategy evolves and AI improves, what you can and want to use AI for will change. That is why using a flexible, secure enterprise AI platform is so important. Solutions can be developed on the platform or pulled in from third parties.
Artificial Intelligence Risk Inc. (AIR)
AIR can help you with all seven steps using our combination of an enterprise AI software platform, change management and expertise. Get 80%+ adoption of Gen AI at your organization with AI governance, risk management, compliance and cybersecurity built in. Visit www.aicrisk.com, find us on LinkedIn, read our blog at stayblog.substack.com or listen to the AI Risk Reward Podcast on YouTube, Apple Music and Spotify.
