Creating value with the right AI strategy

Artificial Intelligence (AI) is already blurring traditional industry boundaries, threatening legacy companies and giving agile new entrants the chance to make an impact fast. Clearly, AI has the power to disrupt any industry. Hence, 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 heading, 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.

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, helps qualify ideas quickly and effectively, so that 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. Accenture’s research shows that nearly 75% 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...And more data is not always better. In a world where data is proliferating and data creates more data, it can be tempting to gather more and more. Having a strong data strategy makes sure that 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, namely an agile, iterative approach to decision-making—as well as AI development—with data at the core.