Growing Up: 5 Reasons Why Many Companies Are Still in the “Adolescence of AI”

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Here’s what businesses can learn from the small group of organizations already using artificial intelligence (AI) for their competitive advantage.

If the biggest companies in the world were people, most would be in their teens when it comes to using artificial intelligence (AI).

According new search Accenture AI Maturity Survey, 63% of the 1,200 companies were identified as “experimenters,” or companies stuck in the experimentation phase of their AI life. They have yet to harness the full potential of technology to innovate and transform their business, and they risk leaving money on the table.

This is money that the most mature AI organizations are already making. While “AI adults” (dubbed Achievers in research) are just a small group – representing 12% of companies – they reap big rewards: by outperforming their peers on AI, they increase growth of their income by 50% on average. How? Because they master key capabilities in the right mix by mastering the technology itself – including data, AI and cloud – as well as their organizational strategy, responsible use of AI, sponsorship of the C suite, talent and culture.

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Unlike people, companies do not necessarily grow and reach adulthood in a relatively fixed period. Instead, they hold their development in their own hands. It is therefore crucial to understand what is preventing adolescent AI users from reaching maturity. They generally share the following five characteristics:

1. Their C-suite didn’t buy AI’s ability to drive growth

Only 56% of experimenters have a CEO and senior sponsorship, compared to 83% of achievers, indicating that AI maturity starts with leadership buy-in. Additionally, Achievers are four times more likely than Experimenters to set up platforms that encourage the sharing of ideas and easily ask questions internally. In one example of innovation driven by leadership, a global digital platform is harnessing AI and generative design to create self-contained buildings that fit together like the pieces of a LEGO set.

2. They don’t invest in their team members

Experimenters are hampered by a shortage of skilled AI workers. Moreover, they have not yet invested in training that helps their employees acquire AI knowledge. While more than three-quarters of Achievers (78%) have mandatory AI training for their engineers up to C-suite executives, the same can be said for only 51% of Experimenters.

To be successful with AI, experimenters must retrain current team members in the technology. For example, a large oil and gas company in Southeast Asia built a gaming platform to develop the digital literacy of its employees. He then created a cloud-based performance reviewer that assessed a decade of employee data to make recommendations for filling various digital roles. This has reduced the time it takes to fill vacancies and has helped close the digital skills gap.

3. Their use of AI is not integrated across the business

While 75% of all companies analyzed have integrated AI into their business strategies and cloud plans, they lack a foundational AI core. To achieve AI maturity, they need to embed AI across the enterprise while knowing when to leverage external resources.

Achievers are 32% more likely than Experimenters to develop custom machine learning applications or work with a partner to extract value from their data. For example, a major US credit card company created an innovative AI ecosystem by partnering with a technical university to create a dedicated analytics lab. The lab helped him stay on top of scientific and technical breakthroughs.

4. They design AI without considering its implications

Scaling AI does indeed rely on building responsibly from the start. With an increase in AI regulation, organizations that can demonstrate reliable, high-quality technology systems that are “regulation-ready” will have a significant market advantage. In fact, Achievers are already 53% more likely than their peers to develop and deploy AI responsibly.

Otherwise, companies risk destroying trust with customers, employees, businesses and society. To combat this, a European-based pharmaceutical company has created accountability mechanisms and risk management controls to ensure its AI-powered operations and services are aligned with its core values.

5. They mistakenly believe that AI has already reached a plateau

Companies that do not aggressively increase their AI spending risk being left behind. To successfully drive business value with AI, leaders know it’s just the beginning, which is why in the last year alone, 46% of CEOs mentioned the technology in their earnings calls .

By 2024, we predict that nearly half of enterprises (49%) will spend at least 30% of their technology budgets on AI, up from 19% in 2021. These organizations know that the quality of their investments matters just as much than quantity, and they are dedicated to simultaneously expanding the reach of AI while better integrating its solutions.

AI stands for lifelong learning

Environments shape people, especially in adolescence. It is not so different with the companies and industries in which they are embedded. Tech companies with little legacy technology have a natural advantage over AI. Most insurance companies, on the other hand, are both hampered by this legacy and faced with a much higher degree of regulation. Unsurprisingly, these are the sectors where AI maturity is highest and lowest respectively. Still, most industries have their Achievers, and overall, all are expected to mature further. By 2024, the overall share of Achievers will increase from the current rate of 12% to 27%.

But even these “adults” will have to keep learning as technology transforms every part of a business, sometimes leading to total business reinvention. There is plenty of room for growth around AI for everyone.

Sanjeev Vohra leads Accenture’s data and AI department Applied intelligence and is a member of the Accenture Global Management Committee.

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