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3 foundations of successful AI leadership

  • Posted on February 28, 2019
  • Estimated reading time 4 minutes
ai leadership

If you’re not already using artificial intelligence in your organization, I won’t ask you why not, because you don’t have time to answer. You need to be engaged with AI—really engaged, not just dipping in a toe with a limited pilot—right now.

I’m being a bit dramatic, of course—but not by much. New research from Forbes Insights confirms what we see at Avanade: that the vast majority of companies are already well on their way with AI and that these companies outperform their peers. Based on a survey of 305 executives, Forbes Insights finds that almost all (92%) recognize and evangelize the value of a comprehensive AI strategy. Most of these companies are building the muscle to shape successful AI programs and culture, especially around data and analytics.

Forbes Insights also finds that companies embracing analytics and AI have significantly more business growth than those who lag in adoption. How much more growth? Four-fifths of the AI leaders grew more than 10% in the past year, compared to just 36% of the late adopters. And just over half of the AI leaders (51%) grew more than 20%, compared to just 13% of the late adopters.

We’ve seen similar results reported out of the MIT Center for Information Systems Research, which finds that digital leaders—an approximate proxy for AI leaders—are three times more profitable than others. Side note: they also provide 38% better customer experience than the laggards.

The good news for the AI laggards is that, while AI adoption has never been more crucial to a company’s future, AI adoption has also never been easier. Another recent study, by HFS Research, bears this out, finding that “Microsoft is emerging as the most ‘enterprise friendly’ AI ecosystem.” The result: companies can adopt AI faster and more cost effectively than ever before.

Forbes Insights calls out “a persevering and visionary leadership” as being essential to AI success. No surprise there, but it needs to be emphasized that AI requires a solid strategy and a steady hand on the wheel. That’s because achieving enterprise AI and embedding AI into the DNA of the organization crosses quarterly and even annual boundaries. It’s not as simple as adopting an intelligent application, service or solution.

The researchers helpfully identify what they see as the three foundations on which AI leadership should be based:

  1. Democratize data and AI and weave them into the fabric of the whole organization. Maybe it sounds intuitive, but organizations haven’t traditionally acted as if it was. Traditionally, knowledge is power, and that’s meant that its flow has often been restricted, sometimes for good reasons (e.g., security), sometimes for not-so-good reasons (e.g., prestige). Enterprise-friendly AI means it’s now technically possible to put data and AI everywhere they can be used, but many organizations need to build a comprehensive AI strategy that permeates the organization to the culture and change management layer. The Forbes Insights researchers are telling us that following through on this possibility should be a pillar of AI leadership.
  2. Strike the right balance of power in machine-human collaboration. AI is often talked about as a way to replace humans with bots to lower costs. Bots are certainly a big part of how AI will change the workplace, but bots are not going to replace humans. Instead, as jobs get reimagined, AI will augment human workers. It will handle the mundane activities, such as forms processing, so people can deal with the important parts of their jobs, the parts that only they can do. As the organization becomes more successful, total employment may well rise—and each worker will be far more productive than was possible before.
  3. Create a sound ethical framework for the AI-driven enterprise. As AI gains more insights into the behavior of customers, employees and others, the need for ethical controls over what insights are generated and how they’re used becomes ever more important. AI systems need to be ethical. For most executives in the Forbes Insights survey, that means making data and analytics transparent and comprehensible to lay people. Near majorities also cite reducing bias, using blockchain for security and transparency and giving consumers and employees control to change and correct their data.

 


There’s much more to succeeding with AI, of course. But these three steps—democratize data, balance humans and machines, and build an ethical framework—are crucial. Use them as stars to steer by, and your AI efforts are far more likely to transform your business for the better.

Read the Forbes Insight research and discover how executives are accelerating their data and AI efforts.

Vipin Nair Twitter Orange Icon@vipinvnair78

I think evangelizing the concept; capability of AI across the industry is also a critical step towards AI adoption. This will result in a well thought out and continuous funnel of AI use cases from all the LOBs across the enterprise. Prioritizing these, with a clear focus on operationalizing the AI implementation, to realize tangible RoI, is key to accelerating the AI adoption. In an Enterprise AI scenario, an AI use case need not always cater to the external consumer. They may very well cater to other internal Lines of Business (LOBs) within an enterprise.

A step in that direction might be to foster business models that incentivize data sharing and AI enrichment. An example of incentivizing democratization of data across business functions, might be to monetize and cross charge data consumption, as if it were just another resource or service that the LOB is offering to the rest of the enterprise. A LOB that offers AI service on the top of its data can charge for the additional enrichment. One may argue that such a strategy is a CDO's worst nightmare as it builds up barriers across an organization's Data marts and Data lakes. But maybe those barriers do exist even today, it’s just not explicitly costed for...

I also feel one of the key reasons for the slow rate of adoption is the disillusionment that sets in because of half-baked use cases and poorly defined success criteria. A key responsibility of an AI evangelist should be to help business sponsors identify and define use cases with realizable RoI. In the long-term interest of AI Adoption, they should steer the sponsors away from what I call "AI Mirages" :)

March 3, 2019

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