Weekly Business Insights from Top Ten Business Magazines | Week 306 | Shaping Section | 2

Extractive summaries and key takeaways from the articles curated from TOP TEN BUSINESS MAGAZINES to promote informed business decision-making | Week 306 | July 21-27 , 2023

Applied AI: Six growth considerations for private markets

By Ilia Bakhtourine et al. | McKinsey & Company | July 18, 2023

Listen to the Extractive Summary of the Article

Innovation and excitement are surging in applied AI. Recent displays of capabilities in areas such as generative AI have further boosted the technology’s profile.  Of course, the industry is still young, with plenty of opportunities for growth as organizations increase their adoption of AI and their spending on technology.  McKinsey’s analysis suggests that the value at stake from AI can reach $15 trillion.  This attention is partly based on the industry’s high growth. But at the same time, McKinsey’s analysis of software and applied-AI companies shows that AI companies are less efficient at generating revenue compared with their software-as-a-service (SaaS) counterparts. Three key factors appear to contribute to this difference: higher costs of marketing and sales, less efficient spending at scale, and costly professional services.

Six considerations can help stakeholders think about applied-AI companies as the industry develops.

1. A clear ROI story.  Applied-AI investments would ideally have a clear market niche with adjacencies. Experts in the industry suggest that a company with a niche in the $15 trillion total addressable market might have a serviceable available market worth $1 billion to $3 billion and focus on a market segment that combines factors such as geography, industry, the end customer, and business function.
2. Better customer segmentation models.  While the value of applied AI is generally accepted, not all buyers are convinced that applied-AI solutions are critical. Go-to-market strategies should therefore use a segmentation approach that emphasizes buyer segments in which sustaining an operating model without innovative technologies is increasingly difficult. That is, applied-AI solutions may be most valuable to buyers in industries that are highly competitive and in which technology can provide a critical advantage. 
3. A plan for multi persona marketing.  Decision-making related to adopting an AI solution is often shared among managers, business users, data scientists, and IT professionals. Since the personas and roles will likely differ, AI solutions’ value proposition might be distinct for each persona to best address their pain points, needs, and goals. These considerations may require the coordinated use of diverse channels on the way to a purchasing consensus.
4. Captive workflows to defend against competition.  Of course, just as with SaaS, lock-in and customer stickiness will likely come from a strong go-to-market approach, control over the postsales process, and a deep understanding of vertical application (in which a solution is designed for the specific needs of a market, industry, or company).
5. New efficiency levers.  AI companies are seeing advantages in developing ways to optimize spending in product development. R&D partnerships and improved tooling in data and model development in product engineering, also known as MLOps, can help. Any efficiencies can be built into the product, which could help the company create revenue more efficiently.

3 key takeaways from the article

1. Innovation and excitement are surging in applied AI. Recent displays of capabilities in areas such as generative AI have further boosted the technology’s profile.  Of course, the industry is still young, with plenty of opportunities for growth as organizations increase their adoption of AI and their spending on the technology. 
2. McKinsey analysis suggests that the value at stake from AI can reach $15 trillion.  This attention is partly based on the industry’s high growth. But at the same time, McKinsey’s analysis of software and applied-AI companies shows that AI companies are less efficient at generating revenue compared with their software-as-a-service (SaaS) counterparts.
3. Six considerations can help stakeholders think about applied-AI companies as the industry develops:  a clear ROI story, better customer segmentation models, a plan for multipersona marketing, captive workflows to defend against competition, and work on new efficiency levers.

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Topics:  Technology, Artificial Intelligence, Software-as-a-Service