Trends and Insights across Industry

In the previous chapter, we looked at how the rising tide of artificial intelligence (AI) is affecting companies across the spectrum, as well as the ways AI is affecting companies within their own operations. In this chapter, we will look at the various ways we’re seeing AI trending across industries, through the lens of prominent and respected research organizations, in an effort to inspire product managers and entrepreneurs out there to begin to formulate their strategies by elevating their products into AI products. In addition, we will go over the various considerations they must keep in mind when attempting to approach AI, including AI readiness and enablement.

Analyzing trends and understanding the growth areas for AI and machine learning (ML) adoption can open us up to powerful future opportunities. The nature of what we build and how we work is changing because of this massive shift in adoption. For most of this book, we’...

Highest growth areas – Forrester, Gartner, and McKinsey research

In this section, we will be taking a look at some of the growth areas of AI from some of the most prominent research and consulting groups. Understanding what the signals are saying gives us the motivation and foresight to be able to anticipate some of the greatest opportunities that lie ahead. This is particularly helpful in the context of revolutionizing a business or product toward AI because many product managers and technologists may be at odds about which specific areas of a product or service they might want to begin bolstering with AI capabilities.

Some of the top growth areas we will look at based on the conglomeration of research and trend analysis from Forrester, Gartner, and McKinsey in the following subsections are embedded AI, ethical AI, creative AI, and autonomous AI development. Embedded AI will look at AI that’s applied and integrated into the operations of organizations and foundations...

Trends in AI adoption – let the data speak for itself

In this section, we’ll be looking at some of the data that’s come out of Forrester, Gartner, and McKinsey about the growth areas previously outlined: embedded AI, ethical AI, creative AI, and autonomous AI development. Compiling the numbers into a section based on the data that’s come out of the research helps highlight the importance of these growth areas and gives us a bird’s-eye view of the state of AI adoption overall. We’ll begin the section with general trends about AI adoption overall and get into the numbers for each growth area next.

General trends

According to research done by Forrester on trends impacting the US, China, Japan, France, Germany, Italy, Spain, and the UK, “AI software spend will double from 2021 to 2025, reaching $64 billion.” What’s driving this adoption is the rise in AI-bolstered software products, particularly the rise of software companies...

Low-hanging fruit – quickest wins for AI enablement

By now, you’ve seen how involved applied ML is for an organization to embrace fully, and we’ve just spent most of this chapter looking through the various growth areas in AI for organizations that are already in business and are looking to capitalize on these growth areas. In Part 1 of this book, we discussed the various layers of infrastructure that need to be supported in an AI program. In Part 2 of this book, we discussed the AI native product. In this current section of the book, we’re discussing the transition of incorporating AI into a traditional software product.

This means we can now move on to set the stage for what this adoption looks like, but before we get started, we want to include a caveat. We really can’t talk about AI transformation unless we also set ourselves up for success to be able to begin the long and arduous process that is AI adoption. We have to make sure the conditions...

Summary

This chapter was all about trends and insights for AI adoption collected from some of the most reputable companies that speak about it. We looked at some of their insights and projections for the coming years and decades regarding AI adoption. Building an AI-native product is, in many ways, more straightforward than transitioning a product from traditional software development to an AI product.

In this chapter, we wanted to set the stage and discuss some of the high-growth areas for AI adoption because, for many companies, knowing where to begin is often the hardest part. Once you’re in the flow of things, you can better anticipate what comes next, but when you’re at the precipice of a major paradigm shift, there’s a lot of friction. Going over the growth areas, data, and common use cases and setting the stage for AI enablement was an intuitive choice to make sure product managers out there are aware of what adopting AI can mean for their product. It...

References

• Predictions 2022: Successfully Riding The Next Wave Of AI: https://www.forrester.com/blogs/predictions-for-2022-successfully-riding-the-next-wave-of-ai/
• What’s New in Artificial Intelligence from the 2022 Gartner Hype Cycle: https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2022-gartner-hype-cycle
• What’s New in the 2022 Gartner Hype Cycle for Emerging Technologies: https://www.gartner.com/en/articles/what-s-new-in-the-2022-gartner-hype-cycle-for-emerging-technologies
• McKinsey Technology Trends Outlook 2022 report August 2022: https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20top%20trends%20in%20tech%202022/mckinsey-tech-trends-outlook-2022-full-report.pdf
• Predictions 2023: AI Will Become An Indispensable, Trusted Enterprise Coworker: https://www.forrester.com/blogs/predictions-2023-ai/
• Gartner Identifies Four Trends Driving Near-Term Artificial Intelligence...

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