Roles of the future in an AI-first organization

Insights

• AI is giving rise to new job families that blend human judgment, creativity, and emotional intelligence with machine efficiency.
• This shift demands reskilling, new performance metrics, and reimagined career paths to embed human-AI collaboration effectively.
• Organizations that invest in responsible AI, continuous learning, and workforce redesign will gain a decisive advantage in the future of work.

As artificial intelligence (AI) evolves, organizations are witnessing a fundamental shift in how work gets organized to deliver value. But unlike past revolutions, AI is not merely a technological upgrade: It is a mindset shift that will require organizations to reshape operating models, leadership practices, and organizational culture.

While it is undeniable that some roles with a transactional nature could be phased out because of AI, as with most technological advancements, AI will also create opportunities and new jobs. That said, embedding the new roles into their existing systems will require organizations to tackle key challenges associated with the roles and reimagine the fundamentals of workforce design.

The next wave of roles

The traditional narrative around AI and work has been frustratingly binary: Jobs will either be automated away or remain untouched. This oversimplification misses the nuanced reality emerging from recent research. Stanford’s Human Agency Scale (H1–H5) framework reveals that most workers don’t want all-or-nothing solutions. Instead, they prefer what researchers call H3 partnerships — that is, equal collaboration between human intelligence and artificial systems. The study emphasizes that the future of work is human potential amplified by AI and human-AI collaboration, not replacement.

The emergence of agentic AI is catalyzing the development of five distinct job families that could dominate the future workplace.

1. Human-essential orchestrators (H5 agency)

These roles require maximum human involvement and minimal AI assistance. They center on uniquely human capabilities: Complex relationship management, ethical decision-making, creative strategy, and cultural leadership. Think CEO roles, highly skilled craftspeople, therapists, and negotiators who must maintain full agency over outcomes.

The growth trajectory here is clear: As AI handles more routine work, demand increases for roles that can provide meaning, purpose, and human connection. These positions will likely command premium compensation as they become increasingly rare and valuable.

2. Human-led amplifiers (H4 agency)

In these roles, humans maintain primary control while leveraging AI as a sophisticated toolkit. The professional remains the decision-maker and quality arbiter, but AI enhances their capabilities. Examples include financial advisors using AI for market analysis while maintaining responsibility for client relationships, or architects using AI for structural calculations while retaining creative control over design. The key differentiator is that human judgment ultimately governs outcomes, with AI serving as an intelligent amplifier rather than a replacement.

3. Collaborative partners (H3 agency)

The fastest-growing job family represents equal partnerships between human and AI. Here, both parties contribute essential capabilities that the other cannot replicate. Humans bring contextual understanding, ethical reasoning, and adaptive problem-solving, while AI contributes rapid data processing, pattern recognition, and tireless execution.

This family includes roles like AI-augmented doctors who collaborate with diagnostic systems, content creators working with generative AI, and process engineers designing human-AI workflows. Success requires developing new literacies around AI collaboration, prompt engineering, and result interpretation.

4. AI-supervised specialists (H2 agency)

These positions involve high automation with minimal human oversight. Humans primarily monitor AI performance, handle edge cases, and ensure quality control. While the volume of direct human involvement is low, the expertise required is often high —these specialists must understand both the domain and the AI systems deeply enough to recognize when intervention is needed.

Examples include autonomous vehicle operators, AI-powered trading supervisors, and automated manufacturing coordinators. The challenge is maintaining human skills and engagement when direct involvement is limited but critical expertise remains essential.

5. Process architects and AI trainers (emerging H1-H5 hybrid)

An entirely new job family is emerging around designing, implementing, and optimizing human-AI collaboration systems. These roles span the entire Human Agency Scale (HAS) because they operate across all levels, creating the frameworks that enable effective human-AI partnerships.

This family includes AI system designers, human-AI interaction specialists, algorithmic auditors, and ethicists. They’re responsible for ensuring that AI systems enhance rather than diminish human agency, addressing bias and fairness concerns, and designing workflows to optimize efficiency and human satisfaction.

The shift toward human-centric job families is triggering a skills renaissance. While technical competencies remain important, the premium is increasingly on uniquely human capabilities that complement rather than compete with AI. Creativity in both artistic or literary and scientific pursuits will continue to be dominated by humans, and productivity gains from AI will ensure humans have more time on their hands to put their creativity to use.

As processes become more standardized, organizations will transform toward advisory roles, and therefore, interpersonal skills and emotional intelligence will become premium skills. The ability to build relationships, communicate complex ideas, and navigate human dynamics becomes a key differentiator. The World Economic Forum’s Future of Jobs Report confirms this trend, showing that care economy roles and education positions are among the fastest-growing sectors. However, embedding changes in roles in a setup that has always had traditional roles isn’t an easy feat.

Challenges of embedding new roles

The impact of AI on jobs is complex. Most organizations still operate within traditional role frameworks, with linear career paths, fixed job descriptions, and performance measures designed for predictable, task-oriented work. These structures do not easily accommodate AI-driven, cross-functional roles that demand adaptability and fluid skill sets. Getting employees to adapt to their new roles is a challenge.

The concentration of value in high-agency roles risks creating new forms of inequality. Workers whose skills are easily automated could find themselves relegated to lower-wage, lower-agency positions despite the critical nature of their work, and might feel uncertain about their future relevance. There’s also the risk of agency erosion: As AI systems become more capable, humans could gradually lose skills and confidence in areas where they once excelled. This creates a dangerous dependency loop where increasing reliance on AI leads to decreased human capability, which drives further reliance on AI. The introduction of agentic AI is creating new types of professional relationships that many workers find challenging to navigate. There can be an issue of trust between the employee and the AI they are using.

To embed new roles effectively, organizations will need to rethink the fundamentals of management, along with workforce transformation and design. It will require them to redefine career paths, performance metrics, and learning approaches for employees, along with managing the attitudes of employees through the right culture-related initiatives.

Top management, including the C-suite, will need to adapt to new ways of working as time spent on operational aspects reduces and decision-making shifts from gut feeling to fact-based, with the ability to perform scenario analysis and simulations. With more individual contributors emerging in enterprises, team building and motivation will need a fresh look. The role of the HR function will need to evolve to manage a workforce with human and AI, which can be challenging.

Professionals will need to understand not just their specific domain, but how human-AI systems interact, where bottlenecks occur, and how to optimize collaborative workflows. As AI systems make more decisions that affect people’s lives, humans will have the added responsibility to provide moral reasoning, cultural context, and values-based judgment that technology cannot replicate.

How to chart the course

Infosys research has shown that equipping employees to understand AI policies and embrace change can boost AI success rates by up to 18 percentage points. Through carefully planned actions, organizations can attempt to solve the complications associated with AI-related role changes, and embed them effectively.

• Invest in AI literacy across all levels, from staff and leadership to boardrooms. Organizations must embed learning in the new AI-infused operating model, with platforms like Coursera and LinkedIn Learning becoming central to workforce development, and offer modular, AI-curated learning paths to prepare employees for new roles.
• Redesign job architectures into fluid, skill-based pathways that allow employees to explore new AI-related roles. They can do this by introducing bridge roles that help workers gradually transition from traditional positions into emerging AI job families.
• As enterprises must now measure human and AI collaboration, AI-augmented productivity, and outcomes, they should develop new KPIs that capture the value of human-AI collaboration, such as innovation impact, adaptability, and cross-functional problem-solving, rather than focusing only on task completion.
• Establish continuous learning ecosystems that combine technical reskilling (AI literacy, data fluency, prompt engineering) with human-centric skills (creativity, ethical judgment, complex problem-solving). Invest in on-the-job learning with AI tools, so employees can practice and experiment in real workflows. Workers should be given opportunities to experiment with AI tools, understand their capabilities and limitations, and develop judgment about when and how to collaborate effectively. Infosys Consulting’s AI&A group launched several AI Champions programs, where AI creators and early adopters mentor colleagues and showcase practical applications through workshops and webinars.
• Organizations must train employees in responsible AI to build trust in AI and ethical use of AI. They must proactively invest in upskilling and reskilling programs that help workers develop capabilities that complement rather than compete with AI. Building strong AI governance involves setting up a dedicated AI Center of Excellence, defining clear policies and procedures, and establishing metrics that track AI use and adoption while providing feedback for continuous improvement that’s characteristic of a product-centric value delivery (PCVD) mindset. Building trust in AI starts with transparency about its strengths and limits. Organizations should communicate regularly on AI initiatives, showing how they connect to business goals and employee roles. Involving employees in the design, training, and evolution of AI solutions fosters shared ownership and a culture of understanding.
• An organization’s competitive advantage in terms of its size, scale, and history (years of existence) will continue to diminish as new lean or AI-native organizations evolve with nimbleness and agility, making it hard to compete in terms of product families as well as cost structures. Forming a core AI strategy team to monitor this and discuss the way ahead can help the C-suite and boards prepare themselves for this paradigm shift and learn to remain competitive and relevant with changing times.
• The tenure of individuals in a particular role will reduce due to AI, and while the number of entry-level graduates in all organizations will decrease from current intake, there will still be entry-level graduates undergoing job rotation in an AI-embedded world. Leaders must ensure that the entry-level graduates’ roles will not only be driven by domain expertise but also by AI fluency. The ability to adapt, learn, and grow in this new environment will be key to taking on bigger responsibilities and moving from transactional roles to decision-making ones.
• Universities too have an expanded responsibility to prepare talent for AI-driven industries. “Universities must build initial work experience into their degree programs to avoid the disappearing entry job trap. The intention must be that our graduates immediately add value and can tackle the complex projects beyond what AI can deliver,” says Frederik Anseel, dean of UNSW Business School. “AI will change everything. Perhaps this is the moment to move away from the idea that a career begins with an entry-level job to master the basics, and that we prepare highly educated people for that first step. The ambitious goal must be to give highly educated people the capabilities to develop and position themselves so that they are immediately an asset, and longer term, help shape tomorrow’s labor market themselves.”
• Organizations can tie up with universities to create a crop of young talent with the requisite skills to enter an AI-first workplace and consider hiring them.
• Organizations can partner with consulting companies for change management strategy advice and execution. Through its Live Enterprise platform, Infosys has led AI transformation for clients across industries and helped them integrate agentic AI to amplify human potential. By fostering human-AI symbiosis, it has achieved cases where AI has empowered employees rather than displacing them. Facing the challenge of using generative AI without constraining innovation, a German utilities company turned to Infosys for support. The collaboration led to the creation of a generative AI Center of Excellence and a governance model designed to balance oversight with creativity. This initiative not only improved processes, training, and operations but also drove a cultural shift — removing barriers and accelerating adoption across the organization.
  
  Another example is where Infosys BPM used generative AI to sift through vast HR policy documents for a large enterprise with 56,000 employees across 16 countries. The AI helped deliver accurate, context-based, optimal solutions while adhering to complex policy intricacies and exceptions. Governance involved ensuring policy compliance and ethical AI use in a balanced way to improve operational efficiency and effectiveness. Infosys also partnered with a large European airline to embed an AI-powered solution in airline IT operations involving optimizing flight navigation, scheduling and crew operations. In addition to innovation, the governance model facilitated transparent accountability and ethical AI while driving process improvements in airline operations.

AI is reshaping the very foundations of work, demanding more than technological adoption — a cultural and organizational shift. Organizations that reimagine workforce design with agility and foresight will lead in the AI-powered future of work.

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