Workforce Reskilling Is The Competitive Edge In The Agentic AI Era

Forbes

6 days ago

Agentic AI
AdobeStock_1157006091
From Generative to Agentic — A New Chapter in Enterprise AI
Generative AI brought along tools like chatbots and auto-generated content, but the next frontier is Agentic AI – systems that can plan, decide, and act across processes. Agentic AI is designed to handle ambiguity and equipped to make autonomous decisions, and companies are excited about its potential, with the Agentic AI market expected to be worth $196B by 2034, reaching an impressive CAGR of nearly 44%.
However, scaling agentic solutions requires a multi-disciplinary delivery model—combining deep industry, data, technology, and process expertise with specialized talent. Furthermore, agent orchestration is the emerging backbone of enterprise architecture: a way to coordinate multiple specialized AI agents as an intelligent network. In practice, this means bridging data, processes, and customer engagement through AI-driven workflows instead of point-to-point integrations. Enterprises will increasingly run AI-infused processes with agents handling data flows and humans supervising outcomes.
'Agentic AI demands more than smart algorithms—it demands smart organizations,' says Sanjeev Vohra, Chief Technology and Innovation Officer at Genpact. 'Enterprises need to reimagine how their people and processes interact with technology. That starts with deeply reskilling the workforce for a future where AI is embedded in every decision.'
The Workforce Wake-Up Call — Why Talent, Not Tech, Will Decide Agentic AI Success
Despite the hype, the real bottleneck is talent. According to a recent Prosper Insights & Analytics survey, 43.5% of executives already use Gen AI tools, but only 26.5% of employees say the same. This data highlights an 'unforeseen talent gap' between executives and employees – suggesting that there is not necessarily a lack of interest in AI, but a shortage of skilled people to implement it.
Prosper - Heard of Generative AI
Prosper Insights & Analytics
Globally, employees are eager to learn. In Genpact's research, nearly 80% of workers said they want new AI-related skills and 59% said they'd be more comfortable with AI if they understood it better. Yet few companies have scaled training. The research also revealed that only around one in three employees are offered AI training, and just 21% have participated.
Enterprises are already taking action by segmenting their workforce into AI builders and consumers. Builders (data scientists, engineers, domain experts) create and refine AI tools, while the broader workforce is made 'AI-fluent' – trained to use AI outputs and embed them in decision-making. This dual investment in specialized talent and broad AI literacy is now viewed as essential for thriving in the agentic era.
Beyond Automation — Rethinking Roles, Skills, and Human-Machine Collaboration
AI will transform legacy roles, with some fading, but many new ones are emerging. We're already seeing this shift in action, with 'prompt engineers' who craft inputs for AI models, 'AI translators' who turn machine outputs into strategic advice, and 'agent overseers' who manage fleets of AI tools. The survivors will have blended skillsets combining domain expertise, technical savvy, and human judgment.
Even as AI handles more tasks, human collaboration remains crucial as humans will be responsible for oversight, creative decisions, and ethical judgment while offloading repetitive or data-intensive steps to agents. As a result, organizations must encourage 'learning by doing' and engage experts to create micro-projects so employees can practice new skills on real problems.
Embedding Responsible Innovation — Ethics, Upskilling, and Culture at the Core
As Agentic AI grows, so does the need for controls and conscience. According to a recent Prosper Insights & Analytics survey, employees have several concerns with the use of AI, specifically in terms of them agreeing it requires human oversight (33.6), more transparency on the data it uses (29.6%), and that can cause job loss (27.9%).
Prosper-Concerns About Recent Developments in AI
Prosper Insights & Analytics
Every AI deployment must include robust oversight, including operating within controlled parameters and following responsible AI guidelines. This means using role-based access controls, audit logs, and clear human-in-the-loop checkpoints from day one. Establishing guardrails early builds trust and avoids the mistakes of 'blind' automation.
'The future belongs to companies that scale curiosity, not just code—by building human-centered upskilling programs and by fostering a culture of shared learning and responsible innovation, where experts share knowledge across networks. In this way, organizations instill a sense of collective responsibility. And the result is a virtuous cycle: a more skilled and diverse workforce that innovates both rapidly and responsibly,' Vohra says.
A Playbook for Change — How to Start Building an Agentic-Ready Workforce Today
To build a workforce that can thrive in the Agentic AI era, organizations must first map their future organization and reimagine roles by identifying which jobs will evolve, such as prompt engineers or AI ethicists, as well as which may no longer be needed. Determining which employees are AI builders or users will enable an organization to launch targeted training that reflects any needed shifts.
With a clear view of the future roles it will need, organizations must then pair investments in core AI talent with efforts to achieve widespread AI fluency. This can be done by incorporating continuous learning into daily workflows and encouraging hands-on learning through micro-projects, hackathons, internal training, and peer mentorship. Celebrating these efforts during regular reviews and building cross-functional pods that combine business, IT, and data talent can reinforce a collaborative culture where learning, experimentation, and AI adoption compound to address all facets of a problem.
Lastly, organizations cannot build an agentic-ready workforce unless they are prioritizing responsible AI. To address concerns, organizations must ensure clear oversight, offer human-in-the-loop systems, and implement guardrails to manage risks from day one of their AI implementation journey. Innovation can never outpace oversight, and that may require organizations to start with small proofs-of-concept to ensure oversight and gauge talent and infrastructure readiness.
Reskilling is no longer optional—it's a survival imperative. Enterprises that embrace these shifts and build blended skillsets (domain + tech + human judgment) will gain the decisive edge in the Agentic AI era.