Workplaces everywhere have onboarded AI, from general-purpose tools such as ChatGPT to enterprise resource planning integrations and specialized solutions. Now, they must confront changes that are coming from within the organization. How does AI change the tasks workers undertake, the talent companies need, and the ways teams interact?
Tech workers—being so close to AI-driven changes—are the first to be affected. The evolution of their work serves as a model for changes across job functions, opening a broad window into the future of work across occupations. Today, companies are growing into AI maturity, beginning with adoption of tools and evolving to agent-led orchestration (in which autonomous AI agents handle complex, end-to-end tasks such as coding, testing, analysis—with human oversight). As they do so, companies are grappling with how to develop a workforce strategy that taps the best of people and technology.
Our interviews with AI-native leaders, our client experience, and job market data demonstrate that major, AI-driven shifts in work are underway. Specifically:
- Work is being redefined. AI is taking over execution and freeing human teams to focus on strategy, design, and oversight.
- Roles are broadening and blending. Functional lines are disappearing.
- Teams and organizations are restructuring. Traditional pyramids are giving way to flatter, AI-augmented pods, redefining the need for junior, coordinator, and manager roles.
- Skills are shifting. Fluency in AI is becoming essential across roles, alongside systems thinking, problem framing, and sound judgment.
- Being open to AI distinguishes leaders. Early movers aren’t just adopting tools, they’re investing in workforce readiness, communication, and cultural alignment.
Still, organizations have an opportunity to shape the transformation, as we explore here.
No Time to Wait
For tech workers, our harbingers of change in the workplace, the future isn’t evolving gradually. Change is happening fast: in just the past year, leading tech organizations have moved quickly to adapt their talent models for an AI-first world.
The shift is driven not by one breakthrough but by a relentless wave of new tools and platforms, each pushing teams to rethink how work gets done and who does it. These shifts aren’t just about efficiency. They reflect a deeper transformation that is in motion: roles are being redefined, layers streamlined, and hiring strategies rewritten to prioritize AI fluency. In many cases, work is being redistributed, not eliminated, as teams find new ways to integrate AI into daily execution and value creation.
Here are some real-life examples of how various tech roles are changing as AI matures:
- Software engineers are focusing more on the “why” and “what,” rather than the “how.”
- User interface and user experience designers are becoming architects of AI-powered products, continuing to add the human touch.
- Product managers are evolving into strategists by automating administrative tasks and shifting how they plan, prioritize, and engage with teams.
- Data scientists are seeing a new pathway, from entry level to high-level and strategic questioning and oversight.
- Quality assurance workers are moving away from execution toward intelligent oversight of the agents that drive testing workflows.
A Framework for Understanding the Evolving Workforce
To help leaders navigate this transformation at scale, we developed the AI Talent Horizon Framework.
It’s built on two key dimensions (see the exhibit below):
- AI Maturity. It progresses from tool-based adoption by individuals, to workflow transformation, to full, agent-led orchestration. Most organizations, and even teams within them, operate across multiple stages at once, not in a linear path.
- Workforce Impact. This spans how tasks are executed, to what skills are needed, to how teams are structured, to how organizational culture must evolve to support new ways of working.
Most tech organizations today remain in the early stages of the AI maturity curve. Tool-based adoption, where individuals use AI to boost speed and reduce repetition, is still the norm. A smaller group is moving into workflow transformation, embedding AI into team processes and shifting tasks toward co-creation. The next horizon is agent-led orchestration, where AI takes on end-to-end execution and humans steer strategy and oversight.
In each stage of AI maturity, the shifts for tasks, talent, and teams are significant:
- Tasks move from manual execution to intelligent orchestration, with AI taking on routine tasks and humans focusing on design, decision making, and oversight.
- Talent evolves such that workers’ roles combine new sets of tasks—guiding AI, framing the right problems, validating outputs, and coordinating across systems. Success increasingly depends on judgment, systems thinking, and the ability to direct machines, not just do the work.
- Teams flatten, moving away from layered hierarchies toward hybrid teams that comprise humans and AI. Traditional pyramids give way to agile pods where senior talent and AI collaborate directly to deliver outcomes.
This isn’t a matter of whether teams will evolve. It’s how fast and how intentionally executives lead them to full AI maturity. The shift is essential to unlock enterprise-wide value from AI. If organizations don’t reimagine tasks, talent and roles, and team structures, they risk capping the ROI of even the most advanced tools. Sustained—even exponential—impact is possible, but it requires not just adoption of AI but also alignment of the humans who guide, govern, and amplify it.
Trends to Watch
Seven trends are reshaping how work gets done, who does it, and how teams are structured. These shifts are accelerating, and they point toward a very different future just five years from now.
Work is being redefined and increasingly done by AI. AI now handles code scaffolding, documentation, and test generation, freeing humans to focus on system design and oversight. In one interview, a leader told us that (human) engineers now manage broader domains while AI tools handle routine output. As maturity grows, full workflows will shift to AI execution, with humans guiding, reviewing, and governing—not executing.
Roles are blending from function-based to fluid. Boundaries between engineering, product, and design are breaking down. Engineers validate AI-generated specs, product managers prototype with AI, and designers step into product-level tasks. One leader we talked with said that product managers now cover four to six times greater scope, spanning prototyping, prompt writing, and light quality assurance. Employees who combine hybrid skill sets with AI fluency are fast becoming the norm.
Skills are shifting and new baselines are forming. AI fluency, systems thinking, and adaptability are now must-haves. One company no longer tests for basic coding, instead evaluating how well candidates use AI tools to solve problems. Still, coding depth matters, especially for debugging and AI oversight. Certain human strengths—ethics, empathy, contextual judgment—are rising in value as AI takes on more execution.
Teams are flattening as AI becomes embedded. Support-heavy roles like technical product managers, quality assurance engineers, and sales development representatives are shrinking as AI handles execution. Organizations are shifting to cross-functional pods powered by AI assistants. One tech leader said that the company had replaced coordination layers with shared tools and copilots, phasing out certain coordination-heavy roles, such as technical product managers and product marketing managers, as their responsibilities are redistributed and supported by AI. As some roles become obsolete, companies are also redeploying talent to other departments and teams.
Rising expectations are reshaping entry-level pipelines. As AI automates routine tasks, new hires are expected to contribute at a higher level from day one. The executives we interviewed noted a growing gap between what schools produce and what AI-enabled roles demand. While employers have long carried the burden of upskilling, the pressure is intensifying. Without a more systemic response, organizations may face a prolonged readiness gap at the entry level.
Location strategy is being rethought as human work shifts to higher-value activities. AI is reshaping location strategy by automating routine execution work, pushing human roles toward earlier, higher-value activities like design, problem solving, and innovation. Global capability centers, once focused on transactional tasks and later core development, are now evolving into innovation hubs leading AI pilots and delivery. As skill needs change, the function of each location will be redefined to match new strategic priorities.
The talent race is heating up, and the market is unforgiving. Demand for AI-native talent is surging. Salaries for top tech talent are rising fast, and late movers are paying premiums to catch up. Several organizations are poaching from rivals to close gaps quickly. In tomorrow’s hybrid teams, the edge goes to those who can lead AI.
Four Emerging Organization Archetypes and What They Mean for Leaders
Organizations must react to these AI-driven trends. And they are. Some are cautiously experimenting whereas others are rebuilding from the ground up. Four distinct talent archetypes are emerging, each tied to a unique combination of strategic ambition and talent philosophy. (See the exhibit below.)
There is no one-size-fits-all playbook. Each archetype demands distinct action today to build enduring advantage. The right moves depend on where an organization sits—whether it’s scaling GenAI within current structures, cautiously building new horizons, streamlining for speed, or reinventing itself. Organizations that move with clarity and speed will lead the next wave of AI-first transformation.
The Scaler. Scalers focus on throughput, embedding AI tools into existing workflows and expanding managers’ spans without altering team structures. Some are already seeing measurable velocity gains and have begun reducing the product manager head count.What leaders need to do now: Focus on execution at scale. Embed AI tools across functions—from engineering to quality assurance to product management—and codify clear usage guidelines. Retrain individual contributors to oversee output generated from AI, rather than try to have humans generate that output from scratch. Begin to flatten management layers, launch AI enablement hubs, and begin rewriting workflows to center on orchestration rather than task completion.
The Horizon Builder. Horizon builders invest heavily in AI while preserving traditional job ladders. They retrain from within and evolve through internal mobility, not reorganization.What leaders need to do now: Take a test-and-expand approach. Pilot AI in safe zones (such as ticket triage and content operations), then scale successes. Begin bridging legacy roles to AI-enhanced ones through structured rotations, internal mobility, and shadowing. Upskill teams with foundational fluency and collaborative AI usage skills. Entry-level pipelines should evolve, not disappear: elevate expectations, and support early talent with intentional development tracks.
The Streamliner. Streamliners collapse roles, phase out coordination layers, and build lean pods. Product managers also design, and engineers self-validate their output with AI.What leaders need to do now: Prioritize efficiency and focus. Redesign roles around hybrid skill sets. Clarify new responsibilities. Reduce redundant layers, collapse handoffs, and build small, senior-led pods that fully integrate AI into daily delivery. Invest in learning programs tied to blended roles, and position HR as a strategic partner in rewiring the organization for speed.
The Reinventor. Reinventors are rebuilding from the ground up, introducing new roles like large-language-model (LLM) product managers and agent orchestrators, redesigning ladders, and making AI front and center in delivery.What leaders need to do now: Move boldly. Redesign entire job families around AI-human teaming: think LLM product managers, agent quality assurance, and Prompt Ops. Begin to flatten the pyramid, create new job ladders that reflect AI orchestration, and establish pods that include AI. Secure hard-to-find, AI-native talent early, and upskill the rest fast. HR must be embedded at the front line of transformation, shaping new paths and policies in real time.
Where to Start
AI isn’t just reshaping tools. It’s redefining how businesses build, organize, and compete. And the archetypes we describe aren’t just shifts in operating models. They are the foundations of long-term competitive advantage rooted in a deep embrace of AI. Adoption today isn’t about dabbling. It’s about building the roles, systems, and behaviors that will define an organization’s edge in 2030 and beyond. Remember: what feels advanced today will be table stakes by 2030—if not before. To stay ahead, organizations must know where they stand now and act accordingly.
So, identify the archetype your organization most closely reflects. Use it as a lens to prioritize action, from role redesign to team rewiring to workforce planning. Then, move with intention. The next wave of advantage won’t come from technology and task automation alone, but from how decisively leaders reimagine the talent and teams that power it.
We thank our BCG colleagues who helped to shape this work: Megan Mirabella, Auli Shen, Joe Khoury, Sambhav Jain, and Aditya Sharma. Special thanks to the leaders at AI-native and large tech organizations (including Vitaly Gordon of Faros AI and Surojit Chatterjee of Ema) who generously shared their insights through interviews and to the extended project team for their research, analysis, and thought partnership.
