AI ‘Exponentials’ Will Rule the World

Constellation Research’s latest projection of the global artificial intelligence market puts it $1. 7 trillion by 2031, with a compound annual growth rate of 34.3 percent, compared with $275 billion in 2024. Amid this staggering growth, what role will humans play in the age of AI, as society grapples with a world where machines can replicate most of what humanity can do?

The age of AI means a technology revolution with an exponential scale unmatched by any other to date. Highly effective tiny teams will be nimbler than the billion-dollar legacy behemoths. As a result of AI, organizations now have the power to build at the speed of thought via decision automation. The difference between successful AI natives and the even more successful AI “exponentials” will be in how effective they are at achieving decision velocity for customer experiences.

Legacy organizations caught between two S-curves must quickly determine whether they should start building their CX from scratch and migrate to AI, or attempt to modernize their technical debt within their existing organizational structures and CX vendors.

The organizations embracing agentic AI will make the key architectural shift required to have an attacker’s advantage. Leaders can expect future pricing models to move away from per-seat pricing to outcomes-based models.

Inside the 10 Attributes of AI Maturity for CX

Whatever gains expected from digital transformation will be blown to shreds by AI exponentials at a logarithmic scale not seen since the advent of the internet. Although at first this may sound like more AI hyperbole, the early indications for organizations that begin their journey as AI natives have a tremendous advantage over the AI-enabled, which have to reduce their legacy-technology, cultural, and financial debt.

CX leaders should seek to progress up the AI maturity scale, which begins with AI Luddites and ends with AI exponentials. Here are the 10 defining characteristics:

1. Level of AI investment. Organizations must overcome technical debt and then invest at double or triple their current rates in AI fundamentals, such as data strategy, decision automation, and large language models.
2. Stage of AI maturity. The stages of AI maturity range from augmentation to acceleration, automation, agentic, and autonomous.
3. Value of data. How organizations tend to their data reflects their maturity in AI. Those organizations that take their data for granted will lag behind in how they source, curate, nurture, and renew their data.
4. Monetization models. Traditional businesses monetize the sale of products and services. Greater levels of digitization enable the monetization of experiences, outcomes, and revenue share.
5. Means of production. This attribute assigns the default assumption for how work gets done and ranges from full human involvement to full digital labor.
6. Agentic usage. Organizations range from no agents to multiple agents and multiple platforms. The goal is to create an agentic ecosystem.
7. Machine scale. This attribute addresses the percentage of human-led vs. machine-led output.
8. Profit per employee. As AI investments increase, expect digital labor to create massive leverage as profit per employee grows.
9. Growth expectations. AI ushers in an era of exponential efficiency and growth. Expectations move from double-digit percentage growth to 10x and even up to 100x growth.
10. Partnerships. Partnerships involving technology providers and enablers mature to include new data signals and data collectives that share data. The most advanced AI exponentials build “Data Inc.” business models that monetize data in complex ecosystems.

Considering the 10 attributes of AI maturity, five distinct levels emerge. At the very top of the maturity model are the AI exponentials, which start out as AI natives (second from the top) but through automation and decision velocity advance to achieve 100x to 1,000x returns. Although AI Luddites (at the bottom) may make their way to more enablement, the biggest opportunity is moving from AI-aware to AI-enabled as those companies strive to compete with the AI natives.

In three years, Constellation expects the first $1 billion services company to be staffed by 1,000 full-time equivalents (FTEs). What’s more, the first single-person $1 billion annual recurring revenue (ARR) company will arrive within five years. That may sound far-fetched, but companies that drive millions of ARR with few employees will dominate the new landscape. All future CX designs must focus on when and where to insert a human, not where to automate, and customers will expect companies to move to machine scale. Those that fail to adapt will cease to exist. 

R “Ray” Wang is the author of Everybody Wants to Rule the World: Surviving and Thriving in a World of Digital Giants (HarperCollins Leadership) and founder of Constellation Research.

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