From the Data and AI Expert Labs

By | 4 minute read | November 13, 2020

IBM’s Data and AI Expert Labs help guide organizations on their AI journeys. Expert Labs consultants represent years of data and AI expertise and offer best practices and business strategies to meet each organization’s unique needs.

Last time, we covered the simple formula we use to help organizations approach success with AI (Outcome = Technology + Skills + Methodology). It’s all about understanding where you want to go. Now let’s talk about AI as a tool for transformation.

II. AI is thinking globally but starting locally

AI is not a discrete thing to itself; it’s one part of a whole. But when it’s a part of a more extensive system, it can apply itself to the whole to redefine what that whole can do. We teach our clients to recognize that AI isn’t an isolated “technology” but a tool that is applied across the systems they are already using. The bottom line is: AI is a way to improve processes throughout your organization. It’s analogous to electricity because it’s not a discrete power that flows to a single location in your organization; it’s infused throughout. It is essential to understand that AI can transform different parts of your organization in different ways. Conversational AI can completely streamline your customer service offerings, while enterprise search can change how your agents and employees find information from chaotic information repositories. It’s about solving pain points.

Once an organization understands that AI is a tool, it can focus on what best practices to use to create AI applications and scale them, iterating fast, but starting with the end in mind. When you start with an understanding of what goals you’re looking to achieve, the process for solving your initial problem becomes clear. You can also think one level higher about how AI can improve how the problem is solved. How does an organization bring AI in to solve a problem? The biggest mistake we see organizations make is when they decide to implement AI without considering their outcomes first. That’s a sure-fire way to fail.

Your result will be local and unique to your business needs, so your strategy needs to match. Technology with an understanding of business value is an accelerator for transformational success, and only by embracing this mindset will you get what you’re trying to achieve.

An organization needs to have a thorough understanding of the information their AI solution provides because it’s through those learnings that they’ll be able to drive outcomes. Creating a center of AI excellence enables your company to scale knowledge wherever it’s most beneficial across the organization. Everyone needs a center of excellence until they can establish the necessary skills in a ubiquitous way throughout the company. A center of excellence allows you to balance those processes and learnings to provide a scaled impact.

For those who are new to the idea of an AI Center of Excellence, in short, it’s a dedicated unit within your organization whose mandate it is to support AI use-cases throughout your company. Your Center of Excellence will help create a holistic vision for AI, identify business-driven use-cases, determine a “road map” for AI growth, target the best data for your use-case, manage scale, and develop a network of experts to support and champion new applications of AI.

When an organization creates an effective center of excellence around AI, they get the focused approach that leads to success. Take the example of our client Crédit Mutuel, one of France’s leading banks. Leadership studied how client advisors were spending their time and found that a significant part of their work involved answering repetitive and straightforward questions via email. It doesn’t get more local than that when it comes to methodology.

With this in mind, the bank turned to IBM for a solution that could relieve client advisors of the burden of answering the same emails each day and give them time to address more complicated and nuanced problems. First, Watson was trained using banking and insurance vocabulary, then the solution was tested by rolling it out progressively across different lines of business. When the solution launched, it had already been assisting 20,000 customer advisors, strengthening customer relationships in 5000 branches. Now Crédit Mutuel has a virtual assistant analyzing and routing all email in the organization, instantly addressing at least 50% of the 350,000 emails received daily. Building on this success, Crédit Mutuel is using IBM technology to scale larger data science initiatives and continue to enhance their workflows and methodologies.

Another fantastic outcome comes to us from a multi-national public-sector bank and financial services hub. Since 1973, the bank has been engaged in non-profit community services banking. To service the needs of local communities, every branch sponsors and participate in a large number of community welfare activities and social causes.

Wanting to extend the full capabilities of the ever-growing global digital marketplace to every one of their users, and with help from the Data and AI Expert Labs, the bank developed and implemented an on-premises digital platform. Through this platform, which now has over 20 million registered users, local communities can now access services, including online shopping, travel, taxis, education, and more.

Having the means and ability to focus on a problem will undoubtedly lead to its solution, and an AI Center of Excellence will net you the dedicated resources needed to see success. Still, it’s not the only thing to keep in mind: you will need specific skills in your organization. Technical skills are in high demand in today’s AI marketplace, and the bad news is that the supply does not meet the demand. However, the good news is that with Watson, you don’t need to rely solely on data scientists and other technical skillsets. In the next part of this series, I’ll dive into how AI can narrow the skills gap.

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