Adopt AI, or Be Left Behind

The AI wave isn’t coming, it is already cresting.

AI is the most rapidly-adopted technology that has ever been created. ChatGPT achieved 100 million users in two months; by comparison Instagram took 2.5 years to achieve this milestone, the internet took 7 years, and the telephone took 75 years. Business adoption of AI is just as fervent; in Q1 2023 earnings calls the mention of “AI” nearly doubled relative to the long-term average. The AI wave isn’t coming, it is here and it is cresting quickly. Enterprises across verticals, and in every function, need to learn to surf the wave by adopting AI aggressively, or they risk being left behind and will get tumbled by the wave.

With the advent of any new technology, there is traditionally a multi-decade adoption period before it becomes “mainstream”. The adoption period is typically determined by a combination of factors, including consumer and business readiness, distribution capabilities including infrastructure and device availability, regulatory constraints, and a cost threshold where democratization of the innovation can be achieved. With AI, users are already excitedly conversing with it every day, the infrastructure exists to drive mass proliferation, the cost has reached a critical democratization threshold, and the economic and productivity potential for all sizes of business has been quantified by several firms to be in the trillions of dollars.

AI Will Be Adopted by Businesses in Parallel Stages of Automation and Augmentation

First, AI will unleash early productivity gains by eliminating the “drudgery” of administrative tasks through automation (think: writing emails, scheduling meetings, filing expenses, booking travel).

Based on the latest Goldman Sachs study, 46% of administrative tasks can be automated with AI. There are already a wealth of plug-ins, apps and models emerging that are beginning to augment and/or reduce admin tasks; but asynchronously taking these tasks off your hands will be the biggest leap. Imagine the economic gains achieved by having nearly half of your employees’ time back to focus on their core roles.

In parallel, there will be a rapidly-emerging set of third-party AI tools that will augment and enhance productivity within core roles. These tools will range from marketing content creation, to coding, to financial analysis – the breadth of skills and roles that will be enhanced is vast. The velocity and quality of each employee’s work will leap forward dramatically, creating an accelerated competitive gap for companies that are leading the curve of AI adoption. Take the example of online retail curation; today curation is largely undertaken by the customer through a vertical search process – whether through an aggregator like Amazon or Google or directly on the retailer’s website like Zappos.

Imagine an AI-assisted curation process by an agent without filters or search menus; the customer can simply converse with the assistant in their native language. There are already advances in this direction with ChatGPT and Google’s Bard, where you can prompt the chatbot with specific parameters (eg. “find me the most highly rated tennis racket for a beginner”) and it will return a series of products. While useful, the quality of the results is based on the quality of the prompts – each user will have a different aptitude for prompts. In the near future, conversational interaction with the AI will naturally narrow the prompts (eg. “would you like me to educate you on selecting between graphite, aluminum, titanium, composite materials as part of the selection?”) to find that perfect racket for your needs at higher and higher confidence intervals. From a marketing perspective, this means higher relevance, higher conversions and ultimately more sales in a more friction-less and educational shopping experience.

The ‘Leaders’ in Adoption Will Be Winners, the Laggards Will Be Left Behind

We’re in the early innings of AI adoption, but the velocity and acceleration of the wave can’t be underestimated. While it is hard to predict productivity booms, historically they’ve begun approximately 20-25 years after the invention of a new technology – typically when crossing the 50% adoption threshold by businesses as seen by the invention of the electric motor in 1890 and the invention of the personal computer in 1981. McKinsey & Company recently published a revised estimate, stating that the 50% adoption threshold of AI by businesses could occur as early as 2030 in an accelerated scenario. Goldman Sachs calculates that the productivity growth for a company leading in AI adoption is 10x that of a company that is slow to adopt AI (3% vs. 0.3% annually).

The great news is that AI can benefit almost any industry, and any size of company.

For example, McKinsey estimates that AI could generate productivity-enhancing value for retail / CPG by 1.2% to 2.0%of annual revenues (an additional $400-660 billion), 2.8% to 4.7%for banking (an additional $200-340 billion) and 2.6% to 4% annually in the pharmaceutical and medical-product industries (an additional $60-110 billion annually).  Similarly, a recent Accenture study highlights that across 20 varied industries, over 40% of working hours are expected to be meaningfully impacted by Generative AI. Even industries like natural resources, chemicals and industrial were anticipated to have a 25-35% impact on working hours. Marketing is on an accelerated path for AI adoption; a recent AI Marketing Benchmark study found that 60% of marketers are already using AI in their marketing operations, and 44% have already adopted AI for marketing content production.

How Do Companies Avoid Being Left Behind?

There is no silver bullet for AI, but there is a golden lining. To learn to surf the wave, companies need to make three primary investments:

Invest in technology

Whether it is investing in-house, or investing in third-party solutions; making AI investments a meaningful percentage of capital expenditures is vital for the next 10+ years. The recent acceleration of AI mentions on earnings calls are undoubtedly a prelude to increased and more AI-focused capital expenditures. According to Accenture, 98% of the global executives surveyed agreed that foundational models will play a critical role in their organizations’ strategies in the next 3-5 years. Investments need to be parallel for administrative task reduction (i.e., giving time back to focus on more productive tasks), and for core role enhancement technology to make the most of the newfound time.

Invest in talent

While engineering talent is a necessity, so too are the non-engineering talent investments as AI augments workflows across teams and roles. Of the 22 job categories that Accenture analyzed, five of them will have more than 50% of their daily work impacted by AI, and 16 of them will have at least 30% of their daily work impacted by AI. Having a non-engineering workforce trained on how to leverage AI will be critical to success.

Invest in iteration

Companies of all sizes need to adopt a startup mentality when it comes to “test and iterate.” AI is evolving too quickly to create a non-fungible long-term plan. Instead, companies should build a long-term strategy, and evolve the plan as AI capabilities evolve. Investing in a culture of innovation and iteration will open the door to evaluating numerous AI solutions at each stage of the value chain, and encourage each employee to become part of the AI adoption process. With the velocity of AI tool creation, exploration and curation will need to become company values.

Summary

The rapid acceleration of AI has created a once-in-a-generation opportunity for companies, and the workforce at large. Like the inventions of the electric motor and the personal computer, AI is going to fundamentally change the way we work and elevate the levels of productivity in which workers and companies operate. Companies that embrace AI, invest early and build a culture of iteration stand to benefit immensely. Those that are trepidatious, are hesitant to invest and take a “wait and see” approach risk a rapid deceleration in competitiveness and opportunity cost that will compound year after year – put simply, they’ll be left behind.

[To share your insights with us, please write to sghosh@martechseries.com]

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