Enterprise's Slow Path from Automation to Disruption
Lesson 4. The “Absorb” phase of enterprise adoption of new technologies and the reality of deployment timelines.
Enterprises are slow to adapt to change and new technologies. They are risk-averse, inefficient, and overcrowded with bureaucracy. In this lesson, we break down the three-part process enterprises take to adopt and deploy new technology,
Find all the lessons in the Economics of AI here and the previous lesson below.
Lesson 3: The Competitive Landscape of Generative AI
Lesson 3 breaks down the commoditization of AI models, the lack of competitive advantages between AI companies, low user engagement, and the strategies companies are using to develop long-term, durable advantages and moats.
Enterprise Adoption: The Slow Path from Automation to Disruption
Enterprises deploy transformative new technologies in a predictable, three-stage pattern: Absorb, Innovate, Disrupt
Historically, enterprises deploy transformative new technologies in a predictable, three-stage pattern: Absorb, Innovate, Disrupt. The initial “Absorb” phase involves automating obvious, existing tasks. The “Innovate” phase sees the creation of entirely new products and services. Finally, the “Disrupt” phase redefines entire markets. Currently, the vast majority of enterprise activity in generative AI is concentrated in the first phase.
The “Absorb” Phase: Automating the Obvious
Enterprises are primarily applying generative AI to absorb it into existing workflows where the return on investment is clear and immediate. The most common use cases are:
Coding: Automating software development and testing.
Marketing: Generating ad copy, images, and campaign ideas.
Customer Support: Powering chatbots and summarizing customer interactions. This phase is being heavily driven by consultants, with firms like Accenture reporting over $1.5 billion in new generative AI bookings per quarter as they help large companies identify and implement these initial automation opportunities.
The Reality of Deployment Timelines
Major technology deployments take time.
While the hype cycle is moving at lightning speed, the reality of enterprise technology adoption is far slower and more methodical. Several data points provide critical context:
Cloud Precedent: Cloud computing is now considered an “old and boring” technology, yet it still only accounts for approximately one-third of all enterprise workflows. Major technology deployments take time.
Evans, Benedict. (2025). AI Eats the World [Slide deck]. Current AI Deployment: Recent surveys show that while about a third of large companies have at least one generative AI product in deployment, another quarter state they have no plans to do anything “just yet.”
Evans, Benedict. (2025). AI Eats the World [Slide deck]. 
Evans, Benedict. (2025). AI Eats the World [Slide deck]. Pilot Challenges: An MIT report found that “95% of generative AI pilots at companies are failing.” However, these failures are not typically due to the AI technology itself but rather to the classic challenges of any major IT implementation: security concerns, data integration with legacy systems, privacy, and legal issues.
Evans, Benedict. (2025). AI Eats the World [Slide deck].
The Power of “Just Automation”
Simple automation, deployed at scale, can be profoundly disruptive.
Dismissing the “Absorb” phase as mere incrementalism is a strategic error. Simple automation, deployed at scale, can be profoundly disruptive. The introduction of the barcode in the U.S. grocery industry in 1974 is a powerful historical analogue. This seemingly simple automation technology enabled retailers to manage five to ten times more unique products (SKUs), which in turn fundamentally restructured the entire consumer packaged goods industry. This historical precedent demonstrates that today’s “simple” automation is not the end goal, but rather the foundational layer upon which true market disruption will be built.
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