Building a foundation for AI success: A six-part series on AI leadership

It’s been nearly 70 years since John McCarthy coined the term “artificial intelligence,” but the pace of innovation in the past year—most notably the momentum of generative AI—is challenging leaders to move quickly to develop or update their AI strategies.

In our conversations with customers, the questions we most often hear are: What should an AI strategy look like? What are the best practices? How do we create the most impact?

To begin to answer these questions, we developed “Building a Foundation for AI Success: A Leader’s Guide” as a framework to share what we are learning and hearing about the emerging best practices for driving business value with AI. It is drawn from conversations with customers, partners, analysts, AI leaders inside and outside of the company, and published research, as well as from our own experience.

We are sharing it as a resource to help inform your own AI strategy, whether you are just beginning to consider AI, are testing and deploying, or are well along the path.

How to accelerate your company’s success with AI

“The very first step of the journey is not even technical. It’s to establish a great partnership with the business. The number one goal is to deliver value to the company and to our customers.”

–Andy Markus, AT&T Chief Data Officer

“Building a Foundation for AI Success: A Leader’s Guide” lays out the five categories that, collectively, support the ability to deliver meaningful, sustainable, and responsible value creation with AI. While there is no one answer for all organizations, we’re starting to see best practices emerge across five discrete categories. They are:

1. Business strategy
2. Technology strategy
3. AI strategy and experience
4. Organization and culture
5. AI governance

This series will explore each of these topics in depth. In the meantime, here are a few highlights.

Business strategy

The consensus—from conversations with customers as well as other external experts—is that a clear set of business objectives for AI, with prioritized use cases and key performance indicators (KPIs), is closely correlated with success. An investment strategy for AI is also key, as it as it entails a structured process by which the organization assesses the business impact of AI against strategic priorities, uses those findings to inform investment decisions, and enables the organization to build a common understanding of how AI accrues to business value. According to Gartner, “Organizations that follow a portfolio management plan to determine most AI use cases are 2.4 times more likely to reach ‘mature’ levels of AI implementation.”¹

Technology strategy

From a technology perspective, the top priority is an AI-ready application and data platform architecture that will meet your organization’s requirements. It’s also crucial to align parameters for build versus buy decisions, as well as plans for where to host data and applications, to optimize outcomes.

AI strategy and experience

While previous experience in building, testing, and realizing AI value across multiple business units, use cases, and dimensions is extremely valuable, other elements of AI strategy are important to consider as well. Customer-centricity, and taking a systematic approach to AI, are both emerging as key contributors to AI success. The 2023 Gartner® report Survey Analysis: AI-First Strategy Leads to Increasing Returns found that “41 percent of mature AI organizations use customer success-related business metrics.”¹

Organization and culture

Organization and culture are also widely agreed to be significant success factors for AI. From an organizational perspective, having a clear operating model can make the difference between AI initiatives that are viewed as science experiments and those that are understood to be value drivers. From a culture perspective, we have also observed—and been told by customers and other experts—that embracing a culture of change management is key to building organizational capacity for AI.

AI governance

As with any consequential new technology, AI must be built on a foundation of security, risk management, and trust. As a result, organizations seeking to reap the greatest benefit from AI must develop their understanding of the data governance, security, and responsible AI implications of their decisions and implement the processes, controls, and accountability structures needed to govern it.

The paper also includes a set of stages—from exploring potential to realizing value—that you can use to map your own progress against these pillars, as well as a “Getting Started” guide that includes suggested next steps.

Next steps

Stay tuned for the next post in our series: “Building a business strategy for AI,” in which we will explore the factors that contribute to a successful AI strategy and transformation plan. We will follow it with dedicated posts focusing on technology and infrastructure, AI strategy and experience, organization and culture, and AI governance.

Download a copy of “Building a Foundation for AI Success: A Leader’s Guide.”

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¹Gartner, Quick Answer: What is the True Return on AI Investment? By Ethan Cohen, Afraz Jaffri, Published April 26, 2023. (gartner.com). Survey_Analysis_ An__795644_ndx.pdf.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.