Building a foundation for AI success: Business strategy

This is part two of a six-part blog series—see part one and download the white paper.

Business strategy is the first step toward AI success

AI is applicable to so many different use cases, from content generation to code generation to prediction to summarizing vast amounts of data. But what makes AI valuable is the impact it can have on business goals.

In this post, we’ll focus on business strategy—the first of five categories that support the ability to deliver meaningful, sustainable, and responsible value creation with AI. Subsequent posts in this series will cover best practices for the remaining categories: data and technology strategy, AI strategy and experience, organization and culture, and AI governance.

Five steps to building a successful business strategy for AI

AI has tremendous potential to transform multiple business functions, from marketing to product development to customer service to operations. But, like any consequential technology, it needs to support business objectives to drive meaningful business value.

In our conversations with customers, partners, and external and internal experts, we identified five steps that can help you develop a strategy for AI that will help you meet your goals.

1. Define and prioritize business needs

Successful AI projects begin with a clear, prioritized—and most importantly, valued—set of business needs. “We’ve only just begun to understand the potential for AI business transformation across organizations,” said Alysa Taylor, Corporate Vice President of Azure & Industry Marketing at Microsoft. “While customer use case adoption of AI varies by industry, we are seeing clear momentum around core business opportunities like employee experience, customer engagement, and internal business processes, as well as a focus on areas where AI can help bend the curve on innovation.”

Starting with the business need is crucial because it helps pinpoint the use cases that are best equipped to drive meaningful impact and garner executive visibility, support, and, critically, resources. This can help you avoid “perpetual proof-of-concept” and scale the initiatives with the greatest potential to become a force multiplier for your organization.

2. Identify AI use cases that support business objectives

Once your business needs are clear, it’s time to identify the use cases best suited to meeting your needs. Some of the top use cases we’re seeing for generative AI include:

Business need	Generative AI use cases
Advance productivity	· Streamline employee tasks · Speed up communication with AI-generated content · Accelerate service delivery
Maximize efficiency	· Anticipate future needs with predictive analytics · Accelerate operations with amplified automation · Avoid downtime with predictive maintenance and AI-powered incident management
Improve business outcomes	· Generate new products and services · Personalize customer experiences · Enhance decision-making with intuitive business reporting

Of course, the best use cases are ones that deliver value in multiple ways. Conversational search, for example, is a great time-saver but also improves customer experience, while call summarization can help front-line employees and surface issues or opportunities that can lead to product or service improvements, or even new features and products.

3. Establish a set of criteria you’ll use to prioritize use cases

The next step is to establish a set of criteria that you will use to evaluate use cases with the highest likelihood of success. It’s critical to engage a diverse group of stakeholders and teams spanning multiple areas of expertise within your organization. These insights can help to identify use cases from different perspectives and inform the potential impact of each one, so you have the broadest possible view of success from stakeholders across the business.

Following are five criteria to consider. Implementing these can be as simple as a discussion or as rigorous as a scorecard that you use at the beginning of each project.

• Business impact. Is this project set up to deliver powerful, demonstrable results?
• Feasibility. Does your organization have the data, expertise, and budget needed to support success?
• Time to implement. Is this a quick win or a longer-term investment? Starting with quick wins can build organizational confidence, while investing in longer-term projects can yield compounding returns over time.
• Ability to measure. Do you have the tools necessary to measure the impact of this initiative? Does it support established and valued key performance indicators (KPIs)?
• Risk. What are the risks, if any, associated with this initiative? What is the risk if you don’t pursue it?

Whatever process you choose, establishing a set of prioritization criteria will help build organizational alignment and confidence over time.

4. Determine how you will measure the value of AI initiatives

The ability to measure outcomes is one of the key criteria to consider as you prioritize AI use cases, as clear KPIs are critical for driving momentum and success for any technology project—and AI is no exception. Try starting with the discrete impacts of specific AI initiatives. Here are just a few of the ways our customers have measured the value of their AI initiatives:

• Productivity. Faster and more accurate transcriptions, document processing, market summaries, and content generation, reducing manual workloads and repetitive tasks.
• Personalization. Personalizing the customer experience—higher average order value (AOV) and customer lifetime value (CLV), increased customer satisfaction (CSAT) and net promoter scores (NPS), improved customer retention and conversion rates.
• Efficiency. Forecasting demand more precisely, automating inventory management and predictions, pre-empting production line disruptions, identifying potential safety vulnerabilities, optimizing resource allocation, and synchronizing supply chains.

Ultimately, by quantifying the value of AI initiatives tied to business goals, you can build a culture of data-informed decision-making and ensure that AI becomes a strategic asset rather than a disconnected technology experiment.

5. Build a portfolio management plan to help guide your investments

Finally, one of the biggest drivers of success is a portfolio management plan that helps to guide investments in AI.

In “Quick Answer: What Is the True Return on AI Investment?”, Gartner stated: “Enterprises do not achieve maximum leverage from artificial intelligence investments, despite increased spending. Executive leaders must become keen and discerning creators of AI investment strategies in order to obtain optimum value from AI initiatives,” and that “the best return yield from AI investment will come from an extensive portfolio of AI, guided by an expansive and evolving investment thesis that is aligned to strategic priorities and helps to allocate resources based on business impact. 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

Like a personal investment plan, an effective portfolio management plan sets up clear criteria for evaluating the success of individual projects, enabling you to identify which projects are delivering the expected value and which require adjustment or reallocation of resources. It also supports effective risk management. By carefully planning and prioritizing your AI use cases within a portfolio, you can diversify your AI initiatives, mitigate the risk associated with any single project’s failure or underperformance, and optimize resource allocation over time.

Next steps

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

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

To read more from Alysa Taylor about how AI is transforming work for every individual, every business and every industry, read Shaping the future with the cloud built for the era of AI.

---

¹Gartner. Quick Answer: What is the True Return on AI Investment? By Ethan Cohen, Afraz Jaffri, Published April 26, 2023. (gartner.com). 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.