How AI Pays Off When Businesses Go All In – MIT Sloan Study

AI tools, investment in complementary technology, and infrastructure supporting their efforts must be considered for firms to see growth through AI.

Approximately 92% of larger businesses have realized returns on their investments in AI and are subsequently dedicating additional funds to the technology. However, what is necessary for smaller firms and earlier-stage startups to reach this same success?

Choi, a postdoctoral scholar at MIT Sloan, asks whether these machines can learn and make decisions independently. This is a crucial inquiry considering how modern technology is swiftly developing.

Choi says:

“AI utilization is tied to startups’ products and services. It’s more directly relevant,”

According to a recent paper by Choi et al., firms have to be willing to make significant investments in AI if they wish to reap any benefits, as the minimal adoption of this technology fails to yield an increase in revenue.

Investments in AI begin to yield returns when businesses increase their utilization of AI to at least 25%. With a quarter of all available AI tools being implemented, this suggested level of use triggers an increase in growth rates.

Yong Suk Lee, Taekyun Kim, and Wonjoon Kim were all involved in producing a paper they co-authored.

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The researchers examined the AI usage of 160 startups and small businesses located in South Korea. They asked questions about using natural language processing, computer vision, and machine learning technologies.

Roughly half of the investigated firms were technology-oriented, such as those in software, pharma, and mobile computing, and an equal amount had implemented AI to some extent.

The survey was specifically given to companies established before 2015 when AI adoption became more accepted in South Korea.

After Google DeepMind’s AlphaGo program beat Go master Lee Sedol in March 2016, there was a surge of interest regarding AI in the country. This event is noted as a footnote in the paper.

The AI adoption in the surveyed firms demonstrated a J-shaped pattern when correlated to revenue growth: initially, a gradual increase was followed by dramatic spurts after an intensity threshold of 25% was reached.

For firms with AI intensity below 25%, annual revenue growth was negligible; conversely, firms with AI intensity above 25% saw a dramatic increase of nearly 24% in their annual revenue growth.

Choi went on to say:

“There’s a disruptive power for AI. With lower utilization, it’s harder to make a profit,”

“When you’re in those early stages of AI adoption, you may need some time to obtain the payoff to using AI.”

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Firms embracing AI intensively can be influenced by size and CEO entrepreneurial experience. Companies that are relatively small and whose CEOs have existing industry knowledge are more likely to accept Artificial Intelligence on a broad scale.

AI adoption is less common in larger organizations and spinoffs of other companies. Nonetheless, research labs formed as spinoffs are exceptions to this phenomenon.

The adoption of technology which works in conjunction, including big data and cloud computing, is seen to have an immense influence. This two-fold approach facilitates the enabling of advancements in the development process.

Data collection and management are integral to better Artificial Intelligence (AI) outcomes. At the same time, the computational power necessary to carry out complex calculations also plays an essential role. Both of these elements enable organizations to achieve growth with their AI investments.

This finding was unsurprising to Choi and his co-authors, who have been aware that investing in one technology has driven the adoption of other technologies for decades.

New operating systems paved the way for improved software, faster modems enabled the emergence of computer networks, and IT infrastructure supported the development of online selling.

Choi continues to say:

“Complementary technology makes it easy to adopt new technology such as AI,”

“To adopt and utilize AI effectively, and to get the payoff at earlier stages in your investment, you need the technology and the skills that go with it.”

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The paper’s main takeaway is that access to AI technology alone is not enough to make successful adoption. The infrastructure necessary for its support must also be implemented, making complementary technology a crucial element. To Choi, this illustrates the importance of setting up an environment to facilitate AI implementation.

Choi went on to say:

“When you make that easily available, you can accelerate AI adoption,”

A company must consider how tightly AI is integrated with its main product or service regarding research and development strategies. This can heavily shape the direction of their R&D investments.

R&D directed inwards creates “absorptive capacity” – a capability used to take in and apply AI expertise – that puts the company in a more advantageous place when implementing and utilizing AI.

Firms looking to keep their algorithms and data as personal property may find having their proprietary processing infrastructure beneficial. This can protect them, ensuring that no third party can access or process their algorithms or data sets.

AI can be used as a complement to a firm’s core focus, according to Choi. In this case, external resources should be sought. Alternatively, firms should invest in AI-specific knowledge and expertise if AI is the centerpiece of a company’s operations.

OpenAI’s ChatGPT, a large language model, is an excellent illustration of the current trend toward widespread use and refinement. These are accessible models that are constantly evolving to become more advanced.

“It’s important to ask, ‘Is there a point solution for the AI work I’m trying to do?’”

“If your area of work is more systematic, then you don’t necessarily need an internally focused R&D strategy. You can license something that’s already available.”

As AI evolves and advances, businesses embracing this technology will be well-positioned to thrive. However, it is essential that companies approach AI with a thoughtful and strategic mindset, understanding the potential benefits and challenges and taking steps to ensure that the technology is used in a way that aligns with their values and objectives.

Source: MIT Sloan

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