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8 insights into AI deployment

8 insights into AI deployment

8 insights into AI deployment

René Koets | Partner,

As the pace of AI-related technology development accelerates, it is becoming a key driver of innovation for new products, services and business models. Our survey of 30 of the world’s largest companies identifies what the best AI deployers are doing, and where others may be falling behind.

Survey identifies eight ways to better deploy AI

To better understand how companies are deploying AI, we interviewed senior leaders at 30 of the world’s largest companies. Our results are not only for the largest or most advanced players, however. Our eight key insights are brainfood for any organization – whether you need to speed up your AI adoption or simply fill in selected gaps.

1. The field is moving rapidly from experimental to applied technology

AI has gone from ‘something to look out for’ to being widely deployed. Yet, wide scope and scale is rare, despite being a goal for many companies for the next three years. For instance, one-quarter of those surveyed have deployed Robotic Process Automation (RPA) at scale, but 83 percent expect to do so in the next three years. Similarly, 17 percent use cognitive computing and machine learning at scale, while around 50 percent expect to do so.

My takeaway: Technology is ready to add value if we are ready to deploy it.

2. Automation, AI, analytics and low-code platforms are converging

Companies find that technologies are more effective when deployed together rather than in isolation. Current trends enable this, such as low-code platforms allowing multiple technologies to be integrated and used; in-house talent working across technologies; and leadership ensuring a coordinated approach.

My takeaway: Technologies should be viewed as complementary, being mixed and matched to achieve specific business goals.

3. Enterprise demand is growing

Interviewees are investing heavily in AI in order to move from functional-level deployment into broader areas of their businesses. Investing in talent is key. Most said their investments in AI-related talent and infrastructure will increase by 50-100 percent in the next three years. Five already employ an average of 375 full-time employees on AI and automation and, we estimate, spend around USD75 million each on AI talent. What’s more, they expect staffing levels to continue to grow, with each having 500-600 FTEs working on AI in the next three years.

My takeaway: AI investments will increase significantly over the next three years.

4. New organizational capabilities are critical

Building AI-related competitive advantage involves much more than technology. It must combine talent and capabilities with processes. As well as top-down approaches, we found some companies where business lines or functions lead the initiatives. Around two-thirds of interviewees said they have centers of excellence for AI deployment.

My takeaway: Success is about more than just getting the technology right. It’s also about governance.

5. Internal governance is key

Formal governance policies, processes and controls are needed around AI technologies, service delivery models and third-party providers. These should include standard procedures around monitoring and managing risks, performance and value; ensuring trust and transparency across the AI lifecycle; clear roles, responsibilities and accountability; and appropriate training.

My takeaway: Effective governance is crucial – governance and scale go together.

6. AI needs to be controlled

Only 25 to 30 percent of the large companies are investing heavily in developing control frameworks and methods to enhance trust and transparency. Yet, this is vital if responsible AI is to successfully evolve and not become simultaneously more powerful and opaque.

My takeaway: Digital tooling and frameworks allow real-time, continuous monitoring of AI’s performance, risk and compliance.

7. AI-as-a-Service is on the rise

As well as building capabilities in-house, companies can tap into a growing ‘as-a-service’ market. This does not replace the need for a comprehensive AI strategy and internal capabilities, however. Companies must decide which capabilities are needed, how to balance build vs buy, and when ‘as-a-service’ can accelerate AI deployment.

My takeaway: Have an AI strategy that reflects ‘as-a-service’ models, modularity and flexibility.

8. AI could shift the competitive landscape

Even the largest companies vary greatly in how they factor AI into competitive positioning. Companies investing in AI report an average of 15 percent productivity gains on relevant projects. And companies with more mature AI capabilities devote almost ten times the resources to AI than early stage companies. But only a few dedicate the necessary resources to use AI for full-range competitive advantage.

My takeaway: Make AI part of your overall business strategic planning.

Conclusion

AI will continue to play a key role in developing new business, financial and operating models. Investments being made today have the potential to create new winners and losers. The winners will be those that use AI for a full range of benefits, from back and middle office productivity to front office product innovations and customer engagement.

With this in mind, have you identified how AI can transform your business and how the right technology and capital should be deployed? And do you have a data strategy to achieve your vision?

Find further insights in AI transforming the enterprise.

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