Your business strategy is your AI strategy
To scale AI successfully, get your ducks in a row early. That means 1) understanding what business value means to you, 2) translating that definition into a business strategy and 3) focusing in on AI solutions that explicitly deliver on the most critical elements of that strategy. Simple, right? If you have already defined value in your own unique context, you can harness AI to multiply that value—not just grow it marginally—charting a course that genuinely aligns with your business’s strategic priorities and delivers unprecedented returns. Strategic Scalers understand this imperative, with more than 70 percent linking their AI ambitions explicitly to their overall business strategy.
Decide what to focus on—and focus.
Look to the highest-level priorities
It seems like more and more applications of AI are emerging each day. So, how do you determine which applications are going to deliver value, whatever that means for your specific context?
Finding true value starts with defining what really matters to a business and aligning the AI agenda to the highest-level strategic plans. Ask yourself: What are the boardroom’s short- and long-term priorities? How can AI help achieve the objectives of the C-suite such as organic growth, expansion into a new industry or development of new products?
Define value for today—with a vision for tomorrow
While you need to look at the short-term return AI can create for your business, you also need to look at value—and therefore your high-level priorities—through a broader lens. Where is your organization headed in the ‘human-plus-machine’ era? What is the future of your industry? Will that change how you define value three to five years from now?
AI has the power to disrupt well beyond individual businesses. It is already blurring traditional industry boundaries, threatening legacy companies and giving agile new entrants the chance to make an impact, fast. Make sure you’re paying attention to what’s disrupting your industry already, how your world and the world at large are changing, and adjust your strategy, act boldly and invest to buy your way into the action. You may find yourself making different choices when you bring the macro into play.
Take a portfolio view of your AI projects
To be successful on your AI journey, think about your AI projects as a portfolio of things you’re trying to achieve. This means thinking holistically about where you’re headed and navigating the iterative nature of AI initiatives while remaining aligned to strategy and value. Scaling value relies on a formally defined AI roadmap which can help you deliver faster with more rigor and get to production more quickly.
The first step of the life cycle is to create an "idea pipeline"—and populate it with potential AI concepts that are yet to be tested for feasibility and value for your business. Shape, develop and investigate those ideas iteratively—but quickly—before a "go/no go" decision. The ideas you generate may vary in terms of their potential to succeed, so having a holistic view of the collective success of your AI projects will be vital.
Therefore, assimilating AI into your business brings a new type of project execution risk with only a portion of your ideas and experiments expected to go to production. But the good news is that following an AI roadmap, like the one here, helps qualify ideas quickly and effectively—so ideas that fail, fail fast and can be shelved with minimal investment before moving on to something else.
Underpin your AI strategy with a data strategy
Every AI transformation journey starts with data. Our research shows that nearly 75 percent of AI Strategic Scalers agree that a core data foundation is an important success factor for scaling AI. More specifically, they understand the importance of having a data strategy—a design and intent that underpin what data is being captured, in what way, and for what purpose. The data strategy drives value as much as AI does.
And more data is not always better. In a world where data is proliferating and data begets more data, it can be tempting to gather more and more. Having a strong data strategy ensures you’re curating the right data to deliver the desired outcome and then capturing its insights to fuel an AI strategy that delivers that outcome at speed and scale.
Once the data strategy is set, data can be mined to generate insights that help refine both the organization’s strategy and the AI systems themselves. To really get the most out of this constant stream of data-driven insights, you’ll need to explicitly integrate "feedback loops" into business decisions in an orchestrated way—for instance, to fine tune your business strategy and/or make necessary adjustments to your AI initiatives at the same time. This requires a new way of working: an agile, iterative approach to decision making—as well as AI development—with data at the core.
1 Accenture, “Disruption need not be an enigma,” February 26, 2018.