When people hear “augmentation,” they often think replacement. More automation, fewer jobs, less need for humans. The reality from both research and practice is different. IT and AI augmentation are about pairing people with technology so that teams do more, learn faster, and grow without simply adding headcount. That’s why it’s positive for growth: you get higher output and better use of existing talent, and the evidence from leading studies backs it up. This post ties that evidence to what we see in the field: exchange of experience, shared learnings, and why augmentation works when it’s done as partnership, not substitution.
What the Research Says: Human-Machine Partnerships
McKinsey Global Institute frames the shift clearly. In their report Agents, robots and us: Skill partnerships in the age of AI (McKinsey Global Institute, 2025), authors including Michael Chui, Anu Madgavkar, and Sven Smit argue that the future of work is a partnership between people, AI-powered agents, and robots. Today’s technologies could theoretically automate a large share of current work hours, but the report stresses that this reflects workflow transformation, not simple job loss. Skills evolve rather than disappear: most of the skills employers value today show up in both automatable and non-automatable work. By 2030, the report suggests, trillions in economic value could be unlocked through human-AI collaboration, and enterprises with strong IT and technology adoption can see meaningfully higher revenue growth and margins. The message: growth comes from integrating technology with people, not from replacing them.
Harvard Business School adds rigorous experimental support. In Navigating the Jagged Technological Frontier (Harvard Business School Working Paper, with Karim Lakhani and colleagues, in collaboration with Boston Consulting Group), 758 BCG consultants were given access to GPT-4 for real consulting tasks. The study introduced the idea of a “jagged technological frontier”: AI excels on some tasks and fails on others of similar difficulty, so the payoff depends on where you use it. Within the frontier, consultants using AI completed 12.2% more tasks, finished 25.1% faster, and delivered over 40% higher quality. Lower performers improved the most (about 43%), while stronger performers still gained (about 17%). The researchers also observed two patterns of use: “Centaurs,” who split work between themselves and AI, and “Cyborgs,” who wove AI into every step. Both approaches worked; the point was deliberate use and learning, not passive automation. That’s exactly the kind of exchange of experience that makes augmentation stick: teams figure out what works for their context and share it.
MIT and Microsoft focused on software development. In The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers (SSRN / MIT, with Tobias Salz, Mert Demirer, and co-authors), large randomized trials at Microsoft, Accenture, and a Fortune 100 company with thousands of developers showed that those using an AI coding assistant (e.g. GitHub Copilot) completed about 26% more tasks on average. A striking finding: less experienced developers tended to adopt the tool more and gain more in productivity. Augmentation here acts as a leveller: it helps newer or less senior people close the gap and contribute more, which is good for team growth and for scaling without endless hiring. Again, the benefit comes from combining human judgment with the tool, not from the tool alone.
Together, these studies say the same thing: augmentation, done as human-technology partnership, increases output, quality, and the effectiveness of existing teams. That’s why it’s positive for growth.
Exchange of Experience: Why Practice and Research Align
Research gives the numbers; practice gives the nuance. In outstaffing and product teams we see the same themes: the best outcomes come when people treat AI and IT tools as partners, share what works, and adapt workflows over time.
Learning from each other. Teams that share prompts, patterns, and “where AI helped vs. where it didn’t” improve much faster. That mirrors the Harvard finding that Centaurs and Cyborgs both succeed when they consciously choose how to use AI. Exchange of experience (internally and across clients) turns augmentation from a one-off tool into a repeatable advantage.
Levelling the playing field. Junior and mid-level engineers who adopt augmentation effectively often see the biggest jumps in throughput and quality. That matches the MIT/Microsoft result: less experienced developers gain more. For a growing company, that means you can scale impact without scaling headcount in a linear way.
Clarity on the frontier. The “jagged frontier” idea from Harvard is practical: some tasks (boilerplate, exploration, repetition) are inside the frontier; others (architecture, trade-offs, product sense) stay firmly with humans. Teams that explicitly map “human vs. augmented” per type of work avoid both underuse and overuse of AI and grow more predictably.
Partnership, not replacement. McKinsey’s emphasis on skill partnerships fits what we see: the goal is to combine human judgment with machine speed and scale. When that’s the culture, people invest in learning and the organization grows; when the narrative is “AI will replace us,” engagement and adoption drop.
Why This Is Good for Growth
Growth here means: more output per person, faster learning, and the ability to scale without proportionally scaling fixed cost.
Higher productivity and quality. The Harvard and MIT results are clear: well-used augmentation increases tasks completed, speed, and often quality. That directly supports revenue and delivery goals without a matching increase in team size.
Better use of existing talent. By giving people the right tools and a shared understanding of how to use them, you raise the ceiling for everyone. Junior talent becomes more productive; senior talent focuses on higher-leverage work. That’s positive for both capacity and morale.
Faster iteration and learning. When routine and exploratory work are partially augmented, teams can run more experiments, ship more often, and learn from the market quicker. That’s growth in the sense of adaptability and time to value.
Alignment with research. McKinsey, Harvard, and MIT are not saying “replace everyone with AI.” They’re saying: design work as human-machine partnerships, invest in skills and exchange of experience, and map the frontier so you use technology where it helps. That’s exactly the mindset that makes IT and AI augmentation a net positive for growth.
If you want to go deeper, the reports and papers above are a good start: McKinsey’s Agents, robots and us for the big picture, Harvard’s Navigating the Jagged Technological Frontier for knowledge work, and the MIT/Microsoft developer study for engineering. Then add your own exchange of experience: what works in your team, what doesn’t, and how you’re drawing the line between human and augmented work. That combination of evidence and practice is what makes augmentation a growth lever, not a threat.
Sources
- McKinsey Global Institute. Agents, robots and us: Skill partnerships in the age of AI. 2025.
https://www.mckinsey.com/mgi/our-research/agents-robots-and-us-skill-partnerships-in-the-age-of-ai - Dell’Acqua, F., McFowland III, E., Mollick, E., Lifshitz-Assaf, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., and Lakhani, K. R. Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper, No. 24-013, September 2023.
https://www.hbs.edu/faculty/Pages/item.aspx?num=64700 - Cui, Z., Demirer, M., Jaffe, S., Musolff, A., Peng, L., and Salz, T. The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers. SSRN, 2024–2025.
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4945566