Driving the Necessity of Scaling AI Strategically

by April 15, 2020 0 comments


When placed at the edge of quick-paced digital transformation, most C-suite executives realize that they need to adapt AI capabilities across their organizations in order to stay relevant in the market. However, most of them fail to get any further level with proof of concept. There are high chances that either the c-suite leaders tend to focus on irrelevant details or put the effort into building a model rather than analyzing and solving the problem first. This calls for the better scaling of AI or else they would go out of business in upcoming years.

To understand the depth of scaling AI, Accenture conducted a landmark global study last year involving 1,500 C-suite executives from organizations across 16 industries. The study focused on determining the extent to which AI enables the business strategy, the top characteristics required to scale AI, and the financial results when done successfully. The research revealed three critical success factors that separate the Strategic Scalers from organizations in the Proof of Concept stage.As per the study strategic scalers drive “intentional” AI, tune out data noise and treat AI as a team sport.

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. 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. At first glance, it may seem paradoxical. But the data indicate that these leaders are more intentional, with a more realistic expectation in terms of time to scale—and what it takes to do so responsibly.

To successfully scale, companies need structure and governance in place. And the Strategic Scalers have both. Nearly three-quarters of them (71%) say they have a clearly-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. Initiatives not firmly grounded in business strategy and lacking governance construct to oversee and manage are slower to progress. Turf wars break out over who “owns” AI. And, regardless of the AI platforms used, or the knowhow recruited, misaligned efforts fall flat.

Strategic Scalers tune out “the noise” surrounding data. They recognize 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. The research shows they are much more likely to wield a larger, more accurate data set (61% versus 38% of respondents in Proof of Concept). And 67% of Strategic Scalers integrate both internal and external data sets as a standard practice compared to 56% of their Proof of Concept counterparts.

The effort of scaling calls for embedding multi-disciplinary teams throughout the organization—teams with clear sponsorship from the top ensuring alignment with the C-suite vision. For Strategic Scalers, these teams are most often headed by the Chief AI, Data or Analytics Officer. They’re comprised of data scientists; data modelers; machine learning, data, and AI engineers; visualization experts; data quality, training, and communications, and other specialists.

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