4 days ago
How 10X AI Value Is Reshaping Tech Adoption
Paul Deraval, Cofounder & CEO of NinjaCat, is a software veteran with 20+ years driving innovation in martech, AI and agency growth.
For decades, the Rogers adoption curve has helped explain how innovations, from electricity to cloud computing, gain traction over time. Adoption typically followed a steady pattern from innovators to laggards, but today, AI is disrupting that pattern.
According to a March 2025 McKinsey report, over three-quarters of responding organizations reported using AI in at least one business function, up from 55% in 2023. The pace of adoption has accelerated faster than any prior enterprise technology.
Industries like media, which traditionally fell into the late majority, are now demonstrating early adopter behavior. Once-cautious players are accelerating adoption timelines due to economic necessity and strategic urgency.
Why did the traditional adoption curve collapse? The 10X effect.
What's changed? The simplest answer is the value multiple. In the past, new technologies might have offered a 2X return, enough for a pilot but not enough to force action. AI, by contrast, is delivering 10X or even 100X improvements in efficiency, time-to-insight or cost reduction.
That level of value is shifting how adoption timelines are understood, accelerating what used to unfold over years into changes that now happen within quarters. On the Rogers adoption curve, shifts from laggards to early adopters once took decades. Now, they happen in quarters. Organizations aren't necessarily becoming more risk-tolerant; economic pressure is compressing their shift within the adoption curve.
The Rogers curve wasn't built for step-change innovation.
Rogers' model was grounded in psychology. It is assumed that adoption would follow individuals' risk tolerance. New technology, lacking a proven track record, naturally attracted those willing to take a leap. However, AI is changing how risk itself is evaluated. For many leaders, urgency is now a stronger driver of technology decisions than traditional risk tolerance.
When tools can automate hours of manual work, optimize marketing spend in real time or streamline customer support, the conversation shifts from "should we?" to "can we afford not to?" In September 2024, a Gartner, Inc. report found that 44% of finance functions used intelligent automation and 39% use AI for anomaly detection. Sectors like legacy media, automotive retail and property management have also begun moving aggressively. For all, behavior isn't the core driver of change; the stakes are.
10X value is forcing strategic realignment.
Any IT leader knows that most technology decisions aim to cut costs, increase efficiency or reduce risk. Often, that means choosing tools with marginal returns. Upgrading a chat app or project tracker helps workflow but rarely moves the needle.
AI is different. It's driving exponential gains in creative production, analytics and customer service. An October 2023 Gartner survey found that 45% of responding enterprises were already scaling GenAI across functions. These gains are driving real-time change.
As performance gaps widen, laggards are being pulled forward. Competitive pressure is accelerating adoption as companies respond to tangible performance gaps. AI may not solve every challenge, but it acts as a force multiplier when applied to the right workflows. Smaller, more agile firms are now using it to challenge incumbents, rewriting the balance of power in entire sectors.
Unlike legacy systems that required expensive integrations, AI scales with data maturity and model access. As a result, even firms that typically watched and waited are finding it riskier to sit still. Rogers' model once gave laggards the benefit of learning from others. Now, delaying evaluation too long may result in missed opportunities to shape adoption on your terms.
Many companies are adjusting in real time. A May 2024 Forrester survey found that 67% of responding AI decision makers had planned to increase investment within a year. That momentum has made AI benchmarking an active and ongoing part of competitive strategy, further backing the idea that change across sectors of the Rogers curve is happening in a much more compressed fashion.
It's time to reevaluate your tech adoption strategy.
Fortune cited a March 2025 Dataiku report that found that nearly three-quarters of surveyed CEOs feared losing their jobs if AI investments didn't deliver results within two years. That level of concern signals to IT and business leaders that the adoption model must evolve.
This is a chance for IT leaders to guide their organizations by clarifying where AI fits and where it doesn't, so that adoption moves from reaction to readiness. A good starting point is examining how Frankenstacks formed. The Rogers curve wasn't designed as a diagnostic tool, but it can serve as one. Understanding where your organization typically falls can inform how you approach AI and future technologies.
If AI is expected to be a differentiator in the next 12 to 18 months, early adoption may be essential. Teams that have succeeded by adopting later and integrating well can continue to perform if they stay aware of faster-moving competitors. This is the time to assess your position and identify areas for improvement.
To evaluate your position, consider:
• Are competitors operationalizing AI to drive outcomes? How do we define operationalizing AI in our context? What do we consider a measurable outcome?
• How are industry leaders integrating AI into strategy? Are their approaches replicable or relevant for our structure? Could AI help us improve outcomes differently from competitors?
• Are employees using AI tools with structure, or is usage ad hoc? Have we formally assessed internal AI usage? Are teams eager or hesitant to adopt new tools?
We need to reframe the adoption decision.
AI is becoming embedded in core business functions. Leaders who dismiss it may miss out on advantages already being realized. Gartner found that 78% of responding executives believed the benefits of GenAI outweigh the risks, and a SiliconANGLE report stated that 97% of leading adopters were seeing measurable gains.
However, speed alone is not a strategy. Action without alignment creates risk. The Rogers adoption curve can serve as a useful lens for self-assessment. Understanding your organization's place on that curve, along with its readiness to support new capabilities, can guide more intentional decisions.
Business use cases for AI continue to grow. The opportunity is clear, but risk evaluation is still essential. In an environment where 10X outcomes are possible, the next move doesn't need to be immediate—but it does need to be deliberate.
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