What Does Data And AI Strategy Create, Add, And Solve For Businesses?

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Strategy is the evaluation of tradeoffs, so the inescapable question is, should we have a data and AI strategy? This question came from the audienceduring a recent discussionwithAndreas Welsch, VP at SAP’s AI Center of Excellence.

The framing is excellent because strategy focuses on value creation, and addressing this question’s specific points requires a complete examination of data and AI strategy’s value proposition. Most people struggle to see the point of adding this new exercise to the process. Technical strategists should have an answer.

AnAccenture survey found thatcompanies with holistic data and AI strategies are seeing 50% more growth than their peers. There is a body of evidence to support having the conversation about technical strategy, but these data points alone are not enough to be convincing.

I believe the business must justify everything in terms of value, even strategy. Inmy strategy course, I spend part of the first lesson detailing the value proposition behind data and AI strategy.

The business needs a strategy for anything that:

In a data-driven business, data is critical for business operations and creates value for the business. Leaders who set the strategic goal of becoming data-driven need a data strategy.

The same holds when the business delivers value to customers with technology. When products and features are technology enabled or dependent, those technologies are strategic by nature.

Do data, analytics, and AI create new opportunities for the business? When the business chooses to evaluate technology as part of the opportunity discovery process, a strategy should be built for the technology behind any opportunity leadership selects.

Do customers expect service levels or products that require data, analytics, or AI to support? When custom expectations shift or competitors train customers to expect more, risk is created for the business. Automation, productivity, and efficiency advantages can create more competitive threats. Strategy should be built to mitigate risks created by the technology.

Integration is an alternative to technical strategy. That approach adds technology into core strategy and implements an integrated business strategy. There is an assumption baked into this approach.

Does data become part of the business and operating models if the business hires a data team? No, there’s work to do outside of hiring.

If the business also buys self-service tools and data infrastructure, does data become part of the business and operating models? No, purchasing technology isn’t enough, either.

If the business adds data literacy training to this execution roadmap, will data become part of the business and operating models? The answer is still no.

This is the typical execution roadmap. Once all three are complete, the business runs early initiatives and pilot projects. 60% of companies do not take the next step successfully. When technologies lack a strategy, they do not become part of the business and operating models.

Predictably, the focus is on tactics, even though the technology is part of core strategy. Why? The technology isn’t integrated into the existing business and operating models. For technology to succeed, the business and operating models must transform. Changes are required across the business; those will not happen without a technical strategy.

Software is integrated into the business and operating models as part of digital transformation. Integrating cloud is a different process with different objectives. Cloud creates efficiency and scale for software. Both technologies create value in different ways.

They also amplify each other. There’s no point in using the cloud if the business doesn’t have software to run on it. Transformation is sequential, and pieces must be in place from the last wave of technology before the company can move forward with the next.

Legacy software must transform to run in the cloud. Most programs weren’t built with the cloud in mind. Shouldn’t they have been?

Here’s one reason technical strategy is critical. A cloud strategy should have informed decisions about software. Even before adopting cloud technologies, the business should have been asking questions about it. What happens to this application if we need to scale? What are the costs and benefits of building the app to run on the cloud?

The technology group asked and answered these questions. However, they did not have a cloud strategy to inform their decisions. Software architects and engineers made those decisions for technical and project-focused reasons. The approach was optimized for technology, project costs, and timeline, but not business value.

As a result, many businesses must rearchitect their purpose-built apps and update their purchased software to work in the cloud. Transformation is inefficient without a technical strategy. Data, analytics, AI, IoT, platforms, and many waves to come are well understood. Technical strategy enables the business to make decisions today that amplify the value of technology waves that will come next.

Data and AI strategies support decision-making across the business. Technology teams use these strategies to include business value in their decision-making process. The rest of the company uses these strategies to make technical decisions to support long-term objectives. What kind of decisions?

Transformation implies a transition that should be clearly defined so the business can make informed decisions. Data and AI strategies are critical pieces of that definition and prioritization.

I teach the technology model framework to cover the integration process and connect technical and business strategies. Strategy must drive technology to keep technical initiatives aligned with value creation.

The business model is a statement of value creation. Each time the business monetizes a technology, some of the business model is turned over to the technology model. The operating model explains how the business creates and delivers that value to customers. Every time the business operationalizes a technology, some of the operating model is turned over to the technology model.

These are the governing principles of transformation strategy. The business and operating models are transformed to integrate technology. That happens at the strategy planning level. Technical strategies extend that process to define why the business uses each technology for revenue generation, increased productivity, and cost savings.

The technology model is the business’s strategic response to technology-enabled opportunities, emerging threats, and changing customer needs. Technical strategy is forward-looking and prescriptive. The technology organization can make decisions at the project level that are informed by future needs and transformations.

Business units across the firm can see changes to their value streams and workflows in advance. Change isn’t sudden or dictated to them by technology. Business units leverage technical strategy to manage continuous transformation and change.

Prioritization is value-driven vs. technology-driven. Evaluating the highest value transformation is possible with the framework of transitioning parts of the business and operating models into the technology model. What should the highest priority be?

There are two sides to the answer. Value creation based on the business and operating models. Feasibility and cost based on the technology model.

Data and AI strategy create alignment and a focus on value. They add a decision-making and prioritization framework. They solve the challenges of moving from early pilot projects to scaling use cases and integrating the technology across the business.

The value proposition is significant, as studies like Accenture’s demonstrate. However, the causes and dynamics of value creation are more complex. I use the technology model framework to help clients see the connection.