3 success factors for scaling AI

The stakes have never been higher for AI adoption. CIOs and other C-level executives are well aware of the urgent need to shift from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.

A new report from Accenture finds that 84% of C-suite executives believe they must scale AI to achieve their growth objectives, and three quarters believe the failure to scale AI could put them out of business in five years. Yet 76% acknowledge they struggle when it comes to scaling AI across their organizations.

How do they make this shift?

The research points to three key success factors for scaling AI: A C-suite-led commitment to intentional AI deployment, a strong data foundation, and multi-disciplinary AI teams embedded throughout the organization.

Drive intentional AI

AI initiatives that are not firmly grounded in business strategy and lack a governance construct to aid oversight and management are slower to progress. It’s about doing the basics brilliantly: Having a clearly defined strategy and operating model, having a flexible business processes with defined owners for measuring value, and having clearly defined accountability and appropriate levels of funding.

Tune out data noise

Nearly all companies surveyed (95%) agree on the importance of data as the foundation to scaling AI. Yet after years of collecting, storing, analyzing, and reconfiguring troves of information, most organizations struggle with the sheer volume of data and how to cleanse, manage, maintain, and consume it.

Companies need to focus on business-critical data and how to structure and manage it. The companies that wield a larger, more accurate data set and are simultaneously able to integrate internal and external data sets as a standard practice will be more successful. What’s more, it’s important to use the right AI tools to manage the data for their applications. Things like cloud-based data lakes, data engineering/data science workbenches, and data and analytics search capabilities are important here. It’s about being more intentional and focused on ensuring the right, relevant data assets are in place to underpin their AI efforts.  Companies should not use data as an excuse to slow down or limit their plans.

Treat AI as a team sport

While previously it may have been thought that AI deployment should be led solely by the IT department, the research shows that to successfully scale, companies should embed multi-disciplinary teams throughout the organization. These teams should be comprised of data scientists; data modelers; machine learning, data and AI engineers; visualization experts; data quality, training and communications, and other specialists.

While CIOs can still play a key role in ensuring these teams have clear sponsorship from the top and are aligned with the C-suite vision, it’s important to move away from relying on a lone champion to drive AI efforts. Embedding AI experts across the organization enables faster culture and behavior changes. By ensuring employees have a full understanding of both what AI is and how it applies to their day-to-day role, they will be more willing to readily adopt AI as it is scaled.

Companies across industries are using AI to change the fabric of what they do and how they do it. Successfully scaling AI allows companies to achieve a range of benefits from improved customer experiences to increased workforce productivity. Making AI the expectation rather than the exception is the way to ensure it is a cornerstone of your business growth.