Emerging Technologies in 2025: GenAI, AI Agents, and Evolving Workforce

FEI’s Committee on Finance and Information Technology (CFIT) suggests the following prompt for AI summarization of this article: Summarize this article for finance and IT executives. Highlight the interconnected role of Generative AI, Agentic AI, and Workforce Transformation. Emphasize the foundational need for clean, governed data, the shift from AI assistance to autonomy, and the importance of upskilling and human-AI collaboration. Clearly outline the guiding principles: assure data quality, align AI with human judgment, and empower the workforce for sustained transformation.
 
Each year, leading advisory and research firms publish predictions on emerging technologies. To spotlight the most finance-relevant insights, members of FEI’s Committee on Finance and Information Technology (CFIT) Emerging Technologies Subcommittee conducted a review of 11 recent thought leadership reports. In 2025, three trends emerged as both transformative and deeply interrelated: Generative AI (GenAI), Agentic AI, and Workforce Transformation.
 
These aren’t isolated innovations, they represent a systemic shift powered by data. The core insight is simple: to realize the value of AI, organizations must begin with a clean, secure, governed Data Foundation. From there, AI models generate insights and actions, which in turn reshape roles, processes, and workforce capabilities. Understanding the intersections between these domains is crucial for finance and technology leaders navigating rapid change.
 
GenAI: From Novelty to Strategic Infrastructure
Generative AI is entering a new phase. No longer a fringe experiment, it is embedded into core business workflows, particularly in forecasting, reporting, customer service, and compliance.
 
KPMG emphasizes that “confidence in the reliability, quality and safety of those AI models…comes back to data quality.”[1] Data integration and trust are now foundational enablers of GenAI success.
 
McKinsey reinforces this point, stating that GenAI has “the potential to generate an annual value of $2.6 trillion to $4.4 trillion” but cautions that “only 24 percent are focusing on nurturing a data-centric culture and ensuring data interoperability in the near term,” a gap that presents a “substantial barrier to fully harnessing technology’s potential.”[2]
 
Accenture urges firms to treat GenAI as an operational layer, not a bolt-on: “[e]very company must be prepared to forge a new technological footprint, founded on AI.”[3] That requires rethinking workflows and systems so GenAI becomes an embedded, decision-driving capability.
 
Agentic AI: From Assistance to Autonomy
While GenAI responds to user prompts, Agentic AI takes initiative and acts independently. These systems can plan, make decisions, and execute tasks based on defined goals, marking a shift from assistance to autonomy.
 
Gartner defines Agentic AI as AI that “autonomously plan[s] and take[s] actions to meet user-defined goals” and forecasts that “by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024.”[4]
 
Accenture explains that these agents will “create a layer of abstraction across technology,”[5] allowing AI to handle not just answers, but orchestration: task delegation, workflow management, and decision-making.
 
PwC adds that “AI agents...could easily double your knowledge workforce and those in roles like sales and field support,” transforming workflows across the enterprise. These agents “can autonomously perform many tasks, such as handling routine customer inquiries, producing ‘first drafts’ of software code or turning human-provided design ideas into prototypes.”[6] They won’t just support employees; they’ll independently execute entire processes.
 
This shift amplifies the stakes for data integrity. As Gartner explains, “[t]he key challenges facing organizations that are building early AI agents lie in the need to establish high levels of trust and confidence.” These agents “must be constrained by robust guardrails that ensure their actions are aligned to the provider’s intentions and properly reflect user intent.”[7] Therefore, making trust, risk, and security management essential to scaling AI responsibly.
 
Workforce Transformation: Human-AI Collaboration at Scale
As AI grows more capable, the workforce must evolve in tandem. Upskilling, role redesign, and better human-AI collaboration are now imperative for driving business value.
 
Accenture cautions that “using generative AI for conventional automation would yield one-time benefits while turning workers’ enthusiasm into disenchantment.”[8] The goal isn’t to replace employees, it’s to augment them, empowering creativity, analysis, and decision-making.
 
KPMG finds that while “organizations believe their workforce has an appetite for cutting-edge tech…there is also fear that some individuals feel left behind.”[9] Hackett Group reinforces this challenge, identifying access to skilled talent as one of the top three enterprise concerns for 2025. They highlight talent as a critical risk and emphasize the need to “prioritize talent acquisition, upskilling and retention,” especially for “emerging technologies like Gen AI.” To scale successfully, organizations must also overcome “change management issues,” “lack of implementation skills and experience,” and “staff-training and education concerns.”[10]
 
In response, and as emphasized across several reports, organizations must foster a culture of agility and invest in digital skills, adaptive learning, and flexible operating models, making upskilling in AI fluency, prompt engineering, and data stewardship a core business priority.
 
The Systemic Shift: Connecting Data, AI, and Talent
The CFIT Emerging Technologies Subcommittee proposes a framework, illustrated in the diagram below, that captures this ecosystem of change:

• Data Foundation: Trusted data from enterprise systems, cloud, IoT, and external sources. It must be clean, secure, integrated, and governed.
• AI Enablement: Generative and Agentic AI models trained on that data to drive predictions, decisions, and execution. These models rely on accurate inputs to deliver reliable outcomes.
• Workforce Transformation: Roles, responsibilities, and skills evolve in response, creating new models for collaboration and productivity.
• New Data Generated: This transformation, in turn, generates new data, on performance, interactions, and system usage, which feeds back into the Data Foundation, reinforcing and refining the loop. 

 
Guiding Principles for Executives
To navigate and lead through this transformation, executives must act on three imperatives:

• Data Quality is a Prerequisite. As Gartner notes, “AI governance platforms” and frameworks like their “AI Trust, Risk, and Security Management (AI TRiSM) framework” are no longer optional, they’re core to ensuring responsible scale.[11] KPMG concurs that a lack of confidence in data and models is one of the top barriers to scaling AI.
• AI Must Align with Human Judgment. As mentioned above, Gartner warns that autonomous systems must be “constrained by robust guardrails that ensure their actions are aligned to the provider’s intentions.”[12] Responsible AI requires continuous human oversight.
• Empower the Workforce. As Accenture notes, “[s]parking the new learning loop between people and AI will be key to creating meaningful change and continuing to drive the diffusion of this powerful technology across the organization.”[13] That means upskilling, rethinking performance models, and creating safe space for experimentation.

 
Conclusion
The top technology trends of 2025, Generative AI, Agentic AI, and Workforce Transformation, are not just concurrent developments. They are interwoven forces transforming enterprise architecture and leadership priorities.
 
Success will depend on the ability to design, govern, and lead at this intersection, where clean data, intelligent systems, and empowered people work in concert to create sustainable competitive advantage.