Aug 03, 2020

Accenture: maximising the return on investment for AI

Accenture
AI
Technology
Innovation
Georgia Wilson
3 min
AI
Business Chief takes a look at research conducted by Accenture on how organisations are maximising their return on investment when it comes to AI...

In research conducted by Accenture the company reports that 84% of C-suite executives believe they must leverage artificial intelligence (AI) in order to achieve their objectives when it comes to growth, in addition to most believing that AI is an enabler for their strategic priorities.

While most executives also believe that in order to achieve a positive return on AI investments, it requires scaling across the organisation, however, 76% acknowledge that they struggle when it comes to scaling, with three out of four concerned that if they don’t scale AI in the next five years they risk going out of business.

How crucial Is scaling AI?

To determine the answer, Accenture conducted a global study of 1,500 C-Suite executives from 16 industries, focusing on the extent to which AI enables the business strategy, the top characteristics required to scale AI, and the financial results when done successfully.

Emerging from the study, Accenture identified three distinct groups of companies with the capability to successfully scale AI: proof of concept factory, strategically scaling and industrialised for growth.

How to succeed at scaling

Also within its research, Accenture highlighted the three critical success factors that differentiate the proof of concept stage from a strategic scaler.

1. Drive “intentional” AI

“Strategic scalers pilot and successfully scale more initiatives than their proof of concept counterparts—at a rate of nearly 2:1—and set longer timelines,” highlights Accenture. “They are 65% more likely to report a timeline of one to two years to move from pilot to scale. And even though they achieve more, Strategic Scalers spend less.”

To scale successfully companies require structure and governance in place. With this in mind Accenture reports that nearly 71% of strategic scales have a clear defined strategy and operating model for scaling AI in place, while only half of the companies in proof of concept report the same.

“Strategic scalers are also far more likely to have defined processes and owners with clear accountability and established leadership support with dedicated AI champions,” adds Accenture. 

2. Tune out data noise

“90% of data in the world was created in just the past 10 years,” with this in mind “175 zettabytes of data will be created by 2025. Yet after years of collecting, storing, analysing, and reconfiguring troves of information, most organisations struggle with the sheer volume of data and how to cleanse, manage, maintain, and consume it.”

Accenture highlights within its research that strategic scalers are capable of ‘tuning out the noise’ when it comes to data. 

“They recognise the importance of business-critical data, identifying financial, marketing, consumer, and master data as priority domains. And Strategic Scalers are more adept at structuring and managing data.“ In addition, “they use the right AI tools—things like cloud-based data lakes, data engineering/data science workbenches, and data and analytics search—to manage the data for their applications.”

3. Treat AI as a team sport

“The effort of scaling calls for embedding multi-disciplinary teams throughout the organisation—teams with clear sponsorship from the top ensuring alignment with the C-suite vision,” notes Accenture. For strategic scalers these teams are frequently headed by a Chief AI, Data or Analytics Officer.

It’s a lesson Strategic Scalers have learned well. In fact, a full 92% of them leverage multidisciplinary teams. Embedding them across the organisation is not only a powerful signal about the strategic intent of the scaling effort, it also enables faster culture and behavior changes.

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