While hiring freezes at Big Tech firms might be hurting certain AI investments, it’s clear that there remains a strong appetite throughout the enterprise for AI technologies — whether developed in-house or outsourced to third parties.
According to a McKinsey survey from early December, AI adoption at companies has more than doubled since 2017, with 63% of businesses expecting spending on AI to increase over the next three years. In February, IDC forecast that companies would increase their spend on AI solutions by 19.6% in 2022, reaching $432.8 billion by the end of the year and over $500 billion in 2023.
Generative AI is driving much of the recent corporate interest, with text-to-image tools such as OpenAI’s DALL-E 2 and Stable Diffusion seeing swift uptake despite the risks. Adobe just this month announced that it would open its stock image service, Adobe Stock, to creations made with the help of generative AI programs, following in the footsteps of Shutterstock (but not rival Getty Images). Meanwhile, Microsoft partnered with OpenAI to provide enterprise-tailored access to DALL-E 2 to customers like Mattel, which is using DALL-E 2 to come up with ideas for new Hot Wheels model cars.
Sequoia, the venture capital firm, said in a September blog post that it thought that generative AI could create “trillions of dollars of economic value.” That might sound optimistic, but there’s some evidence to suggest that AI has crossed the threshold from research project to serious revenue generator.
In the McKinsey report, one-quarter of businesses said that at least 5% of their EBIT was “attributable to AI” in 2021. EBIT, or earnings before interest and taxes, is profit inclusive of all incomes and expenses except interest and income taxes.
Of course, AI is an exceptionally broad field, and not all categories are equally profitable. Taking a look at the McKinsey report, the vast majority of organizations have adopted AI to “optimize service operations” and “create new AI-based products,” as well as analyze customer service interactions, segment customers and introduce AI-based enhancements to existing products. Lower on the list of AI use cases are acquiring customers and generating leads, automating contact center capabilities, risk modeling and analysis, and predictive service and intervention.
“Today, the biggest reported revenue impacts are found in marketing and sales, product and service development, and service operations, and respondents report the highest cost benefits from AI in supply chain management,” the report’s co-authors wrote. “The bottom-line value realized from AI remains strong and largely consistent.”
The impact on startups
The enterprise’s increasing bet on AI has obvious implications for startups selling AI products and services. For one, it’s lured VCs to pour tens of billions of dollars into ventures that they believe have the potential to transform whole industries.
According to Statista, funding of AI startups reached $26.7 billion across the first two fiscal quarters of 2022. While a dip from the $32.4 billion that AI startups secured across Q1 and Q2 2021, it’s a steep increase from the $13.5 billion raised in the first half of 2020.
Two leading VCs, Nathan Benaich of Air Street Capital and Ian Hogarth of Plural, came to a similar conclusion in their annual “State of AI” report released earlier this year. They predicted AI startups would raise 36% less money in 2022 compared to 2021 but would still surpass the 2020 level.
It’s instructive to look at which AI startups attracted the most capital this year. Outside of self-driving companies Cruise, Wayve and WeRide and robotics firm MegaRobo, the top-performing firms in terms of money raised were software-based businesses, according to Crunchbase.
Contentsquare, which sells a service that provides AI-driven recommendations for web content, closed a $600 million round in July. Uniphore, which sells software for “conversational analytics” (think call center metrics) and conversational assistants, landed $400 million in February. Meanwhile, Highspot, whose AI-powered platform provides sales reps and marketers with real-time and data-driven recommendations, nabbed $248 million in January.
Note that Contentsquare and Uniphore occupy categories of AI that McKinsey spotlighted as key investment areas for enterprises this year — e.g., AI that analyzes customer service interactions and optimizes service operations.
In an interview with Emerging Tech Brew in August, Brendan Burke, a senior analyst for emerging technology at PitchBook, said that VC investment would continue to favor AI applications with “near-term” commercial use cases, such as data preparation, database management and natural language processing (think systems like OpenAI’s text-generating GPT-3), rather than AI apps with impressive demos but elusive product-market fits.
“The bull market in 2021 favored some longer-term sectors, such as autonomous vehicles, that led the vertical in exit accounts, or at least mega-exit count … in 2021,” Burke told the publication. “But that mix is shifting in 2022 to focus more on smaller exits for more fundamental and near-term technologies.”
Economic headwinds
To be clear, the AI startup ecosystem hasn’t proven immune to the current macroeconomic headwinds. According to PitchBook data, global AI funding dipped 44% year over year from $33.6 billion to $18.8 billion in Q2 2022. On a quarterly basis, funding for AI and machine learning was down more than 26% between Q2 and Q1.
I recently wrote about how tighter VC capital is forcing AI startups to face the music. But to reiterate, the receding financing tide isn’t dragging all startups with it. Burke told TechCrunch for that piece that vendors building analytics on top of leading cloud database systems and industry-specific systems of record, in particular, can expect to see more rapid adoption into the next year.
Survey data seems to support this. In a 2023 industry forecast from Info-Tech Research Group, an IT analyst firm, 44% of the companies responding said they plan to invest in AI systems by next year, regardless of any broader economic slowdowns. Deloitte’s State of the AI report from September suggests midsized companies, not just enterprises, could be responsible for the growth — 80% of midsize companies responding to Deloitte’s poll said they intend to increase their annual AI investments, a jump from 25% in 2018.
Forrester advocates for that strategy. In its planning advice for corporate tech budgets for 2023, the firm recommended that companies “keep spending” on AI capabilities, specifically applications that “improve customer experience and reduce costs,” like robotic process automation. (While I’ve explored why the RPA market might be in trouble from a vendor perspective, many analysts believe the tech will likely have long legs given its broad applicability and versatility.)
“The goal for both of these technologies is to make data-driven decisions at the corporate level easier, with more accessible, digestible data analyses,” Alexandra Kelley wrote in an analysis of the Info-Tech report for NextGov. “AI is likely to intersect with this task, as automated algorithms are being used to organize and compile data visualizations. More menial and redundant tasks and security measures have also been targeted by businesses looking to automate with the help of AI.”
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