How to create an effective AI strategy

One of the most frequently cited leading practices for AI transformation is the need for a bold, enterprise wide strategy that is set and championed by an organization’s highest leadership.

Our State of AI in the Enterprise, 4th Edition survey results confirmed this: Transformers are more than three times as likely to have an enterprisewide strategy in place and well over twice as likely as Starters to report their leaders communicate a vision for AI. However, only 40% of our total survey respondents completely agreed that their company has one in place. Meanwhile, even though a significant majority (66%) of respondents view AI as critical to success, only 38% believe their use of AI differentiates them from competitors.

Lost in AI use cases: Leaders can forget to put their business strategy first

To many leaders, it comes as a surprise to learn that the investment needed to develop AI solutions cannot realize a return through the deployment of single, disconnected use cases, or even a handful.This is why it’s so important to have an AI strategy that is connected and coordinated across the enterprise, in tight alignment with the overarching business strategy. All too often, however, business leaders get the planning process out of order, focusing too much on use cases or abdicating leadership of the AI strategy to IT or data sciences. This can be a slippery slope, diminishing the organization’s ability to use AI to create new ways of competing for customers, launching products, accelerating time-to-market, securing supply chains, and beyond.

The strongest AI strategies tend to begin without ever mentioning AI. Instead, they should begin with the organization’s north star: the core business strategy. From there, the process requires tight collaboration with engaged leaders across all business divisions and the focus of workers at all levels. Ultimately, AI strategy should function as the fuel to the business strategy, aligning to the same key performance indicators (KPIs) that have been crafted to incentivize and grow competitive advantage.

In a now famous example from the early 2010s, Jeff Bezos mandated that every leader across Amazon plan for how they would use AI and machine learning (ML) to help the company compete and win. This imperative drove unparalleled innovation and was cited as the catalyst for the Amazon’s rise to become an AI leader today.2 Many of the strongest AI strategies start in this same way: by pushing clear objectives down to business leadership, so they can identify gaps and opportunities within their divisions and work backward from there to apply AI as a solution.

These local plans should then be brought back to the top, so that mutual goals and initiatives can be aligned and unified with the core business strategy. This step is often critical: It’s only when AI has been integrated and proliferated throughout the enterprise that it can deliver the combination of efficiency and value-creating outcomes needed to fuel ongoing returns.

Balance your goals: Over indexing on efficiency can lead to missed opportunities

It’s through the combination of both efficiency and value-creation targets that organizations typically achieve the most success. “When digitally transforming a company, you want greater degrees of efficiency,” remarks Rajeev Ronanki, SVP and Chief Digital Officer at Anthem. “But there is a second order of business: What new business opportunities, what capabilities does AI open up that allow for servicing adjacent or maybe entirely new areas?”

Our survey results reinforced this, demonstrating that lower-achieving organizations (Starters and Underachievers) tended to focus more on efficiency or “cost out” goals, while high-achieving organizations (Transformers and Pathseekers) were more likely to emphasize growth-oriented goals, such as: improving customer satisfaction, creating new products and offers, and entering new markets. In other words, high-achieving organizations are more likely to maintain an eye toward the art of the possible and a growth mindset, which allow them to take advantage of opportunities often missed by those who over index on efficiency or supporting business as usual.

“You have to go both for impact and build the foundations in parallel, and that is the most challenging part,” advises Najat Khan, PhD, Chief Data Science Officer and Global Head of Strategy & Operations for Janssen Research & Development. AI-fueled organizations can create durable competitive advantage when the CEO and C-suite collectively harness data, advanced analytics, and AI to shape strategic possibilities for both the near and long term in support of their corporate strategy.

“You have to pick the right questions, and have what I call a diversified portfolio of questions to drive impact, ensuring that you can demonstrate early value to build momentum for achieving longer-term, sustainable impact.”

Najat Khan, PhD, Chief Data Science Officer and Global Head of Strategy & Operations for Janssen Research & Development.

Communicate the vision: Publicly signaling transformation can build market value

Chief executives of high-achieving organizations typically serve as the AI communicator-in-chief. According to our survey data, those organizations that communicate a clear vision are 1.5 times as likely to achieve desired outcomes compared to those who do not. The most effective leaders tend to use their platform not only to communicate and champion their plans; they also clarify the implications and trade-offs required along the way.

This is often essential for maintaining focus and ensuring that decisions made at all levels of the organization remain aligned to the vision. Leaders should also remember that value can be created by influencing perceptions of the market and investors. Communicating the company’s vision publicly can amplify success, signaling to capital markets and the competitive talent market that an organization is investing in a bold and exciting future. If it’s not important enough to merit such a forceful signal toward change, it’s highly likely that the gravitational pull toward the status quo could dampen outcomes for even the strongest strategy.

Remain dynamic: Perpetually iterate your AI strategy

Finally, developing an enterprisewide AI strategy that’s set up to fuel a differentiating core business strategy is not a one-and-done exercise. Organizations should develop dynamic ways of assessing their strategy to ensure it remains responsive to ever-changing market and technology developments. As the organization’s core business strategy and AI capabilities mature over time, leaders should continually sharpen their goals, moving beyond staying competitive to
increasingly using AI and ML as competitive differentiators.

 

Original post: https://www2.deloitte.com/us/en/pages/technology/articles/effective-ai-strategy.html

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