Growth driven by artificial intelligence (AI) is unfolding at the level of the individual contributor (IC), a marked shift from where it traditionally took hold. Prior to the widespread accessibility of AI, meaningful gains in productivity were concentrated at the leadership and systems level, driven by executive strategy, large-scale technology rollouts, and centralized transformation initiatives. Today, that momentum has moved closer to the ground, where access to AI tools is reshaping how work gets done and how value is created in real time by individuals themselves. Richard Achée, the Head of Partnerships at Edgescale, and Founder and CEO of Found42, describes this shift as the emergence of a new standard for performance.
“We’re seeing the rise of the 10x IC,” Achée says. “Not just engineers, across sales, marketing, even legal. AI is giving people leverage they’ve never had before.” This evolution builds on the long-standing idea of the “10x engineer,” a term used to describe a developer believed to produce ten times the output of their peers, expanding it into a broader, cross-functional reality. AI has lowered the barrier to execution while raising the ceiling for impact, creating a new class of contributors who can operate with speed, precision, and scale.
From Specialized Skill to Scaled Capability
The mythology of the 10x engineer once centered on raw technical talent. Today, that level of output is becoming more accessible to a variety of roles, though not evenly distributed. While tools are widely available, the ability to use them effectively still separates high performers from the rest. AI provides a powerful starting point, but it does not replace judgment, context, or domain expertise.
What has changed is the baseline. Individual contributors now begin with a higher level of capability, allowing them to move faster and explore more possibilities than before. This is particularly visible in functions that historically faced bandwidth constraints.
Experimentation as a Core Operating Model
One of the most immediate impacts of AI is the acceleration of experimentation. Tasks that once required significant time and coordination can now be executed and iterated on rapidly. “People can experiment on a much bigger scale now,” Achée explains. “You’re not just testing one idea, you’re testing dozens of variations, highly targeted messaging, different angles.”
In sales and marketing, this translates into faster campaign iteration and more refined targeting. In legal teams, it unlocks responsiveness and throughput that were previously unattainable. “Legal was understaffed before AI came along,” Achée says. “Now suddenly, they can move faster, be more efficient, and handle more volume.”
This shift creates what he describes as a new base layer of expertise. AI generates an initial output that can be refined and improved, leading to compounding gains over time. As individuals share prompts, workflows, and results, these gains begin to scale across teams. “People are sharing what works,” Achée says. “That’s how this scales internally.”
Culture as the Real Constraint
Despite rapid adoption at the individual level, organizational progress often lags behind and the primary barrier continues to be cultural readiness. “It’s not the technology,” Achée says. “It’s the culture.” AI adoption follows a familiar pattern, with early adopters experimenting first, followed by a broader group that waits for proof, and a final segment that resists change. What makes this moment distinct is how uneven adoption can be within the same organization.
“The tools are already at consumer adoption levels,” Achée says. “But inside big companies, it can take years to fully adopt.” This gap has led to the rise of what he calls “shadow AI,” where employees independently integrate tools into their workflows even without formal approval. The result is a disconnect between individual capability and institutional alignment.
“The question is whether people have the freedom to explore,” he says. “Can they experiment, take ownership, become an AI champion?” Smaller, early-stage companies often move faster due to fewer constraints and a greater willingness to iterate. In these environments, adoption is driven by necessity as much as opportunity.
Training That Drives Real Outcomes
Effective adoption depends on how organizations approach training. Generic, one-size-fits-all programs tend to fall short because they fail to connect AI capabilities to day-to-day work. “Generic training doesn’t work,” Achée says. “It has to be role-specific.”
A more effective approach focuses on tangible outputs. By identifying a small number of core deliverables for each role and building structured prompts around them, organizations can make AI immediately relevant. “Start with five key deliverables,” he advises. “Then build five really well-structured prompts around those.”
Training should also reflect real-world scenarios. Simulated case studies allow individuals to practice applying AI in contexts that mirror their responsibilities, reinforcing both confidence and competence. “That’s how people actually start using it,” Achée adds. “When they see it directly improving their work.”
From Individual Leverage to System-Level Impact
The next phase of AI adoption is already taking shape. While current gains are largely driven by individuals, the focus is shifting toward coordinated workflows and team-level integration. “In 2026, this moves from individual usage to team and workflow orchestration,” Achée says.
This transition redefines what it means to be a high-performing contributor. Success will depend not only on using AI effectively, but also on understanding how individual outputs connect within broader systems. A critical component of this evolution is recognizing where AI falls short. Rather than avoiding errors, organizations must study them to build stronger processes and safeguards.
“Organizations need to spend more time on failure modes,” Achée explains. “Where do these tools fail? Why do they fail? That’s where the learning is.” For individual contributors, this represents an opportunity to deepen expertise. Mastery is less about crafting perfect prompts and more about providing the right context. “Think of AI like an intern,” Achée says. “It’s smart, but it needs direction.”
A New Baseline for Performance
The broader implication is a redefinition of performance itself. The capabilities that once distinguished top performers are becoming the expected standard. “The 10x IC is becoming the new baseline,” Achée says. As AI continues to evolve, the gap between those who integrate it effectively and those who do not will widen. What was once considered exceptional is quickly becoming foundational. For organizations, the challenge is how to align culture, training, and systems to support it. For individuals, the opportunity lies in embracing AI not as a tool, but as a multiplier of impact.
Follow Richard Achée on LinkedIn or visit his website for more insights.
