Rethink work and get your people ready

AI's disruptive nature means your old ways of working will need to change. Those who can successfully integrate AI into their culture and processes will be able to multiply value for businesses, employees and customers alike. Our AI: Built To Scale research confirms the correlation: AI Strategic Scalers are more likely than those in the Proof of Concept stage to embed AI ownership and accountability into teams and ensure employees fully understand AI and how it relates to their roles.

Start configuring the business of the future—now

There are some practical steps you can take to start configuring business processes and the workforce to support AI at scale:

Move from "workforce" planning to "work" planning
Break down traditional job roles, and look at which tasks and activities will be automated, which will require human-machine collaboration, and how this might impact how people and teams intersect and interact.

Look seriously at new skilling
Get a clear view of the knowledge and skills you'll need to generate real value from human-machine collaboration. Look at your leadership, learning and recruitment programs, and invest in new ways to teach new things. For example, we put 60 percent of the money we save from investments in AI into our training programs.

Look at the big picture
What entirely new jobs—such as the "AI trainer"—can AI create in the organization? Are we prepared for those in the context of new markets, products and customer experiences?

As we lean into human + machine collaboration, many human tasks will be augmented by AI. For example, AI can provide enhanced views of real-time data to help support decision making—without the decision making itself necessarily being offloaded to AI. It's important to be clear about the right boundary, or process, for the organization when it comes to the split between the human and the machine—including how that boundary may shift as the organization's AI maturity continues to change. Successful scaling relies on understanding how the organizational chart will change with the upskilling and reskilling of people to be "data native" and with new ways of delineating jobs and tasks.

Your workforce may be more ready than you think to adjust. Our research on the Future Workforce says so. Now it's up to you to take action.

AI may be good for workers:

62%

of workers believe that AI will have a positive impact on their jobs, and 67% say it’s important to develop skills to work with intelligent machines.1

Establish the right talent mix

It’s no surprise that you need new kinds of talent to create AI products and services that deliver value. But beware of thinking data scientists are the only ones who matter when it comes to creating a route to "go-live." You also need data integration experts, business analysts, data engineers, and software engineers among others—and enough of them, in the right configuration.

In addition to the technical skills, it's important your team is interdisciplinary, bringing industry, business, design and governance expertise in the right ways and early on. These areas of knowledge might be easy to overlook, but they play a crucial role in creating successful AI applications.

You need the right mix of talent to move from POC to production

Look at your organizational set-up

Along with establishing who gets the work done, it's important to revisit the "how." Think about the kind of physical set-up that will help you achieve your business goals and integrate AI most effectively. For instance, do you need geographically dispersed business units and AI tools or a more centralized structure?

Our research suggests that a centralized organizational model may be the most effective, with Strategic Scalers saying they now use this approach.

Another variant is "hub-and-spoke," a model that includes both a centralized cross-functional AI group (i.e., the "hub," sometimes called a Center of Excellence) and separate autonomous AI teams (i.e., the "spokes") that sit within business units. Finally, a "distributed" model also exists. Highly autonomous AI teams are housed within each business unit or function, with a delivery focus specific to that business unit or function.

Be guided by your business aspirations, and define a way of working that best supports those goals and your level of AI maturity.

Mind the gap

There can be a gap between the CEO's understanding of AI—what it can do and how—and what the people actually implementing the AI believe. The CEO's perspective will naturally be influenced by the topline strategic intent of the company, what her peers are telling her, and what her long-term aspirations are for the organization. The AI leads doing the work might not always be aligned with the realities and focused goals of the C-suite—but they need to be! In fact, our research indicates that leadership's limited understanding of AI's potential can be one of the top challenges companies face when scaling AI. Strategic Scalers "mind the gap"—they reduce the distance between the goals and understanding of the C-suite and the practitioners when it comes to how AI can and should be applied to change the world, and their world.

Time to implement? Look outside your organization

We are now firmly in the "era of implementation" with an explosion of investment in AI capabilities coming from well beyond Silicon Valley.2 These days, there are myriad tools which are proven, low-cost and academically rigorous. And there are varied and flexible ways to get your hands on AI: open source code, application programming interfaces (APIs), and small and medium-sized enterprise (SME) vendors to name just a few. As AI becomes mainstream, solution price points will also continue to drop.

Now you can reuse, partner or buy to implement and scale AI capabilities before you even need to consider building new proprietary technologies in-house. Take advantage of what's out there for success at speed and scale.

So how do you decide when to reuse, buy, partner or build? This is a full topic in and of itself (we published an entire article on it), but the simple answer is almost always reuse, buy or partner to take advantage of the investment other companies have already made—and get started quickly.

1Accenture, "The big disconnect: AI, leaders and the workforce," July 12, 2018.

2 Dr. Kai-fu Lee, AI Superpowers: China, Sillicon Valley and the New World Order. Houghton Mifflin Harcourt, 2018.

Dr. Athina Kanioura

Chief Analytics Officer and Global Lead – Applied Intelligence


Fernando Lucini

Managing Director – Artificial Intelligence lead, Accenture UKI

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