Each year, leading advisory and research firms publish predictions on emerging technologies and trends projected to impact organizations in the coming year. 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 ten recent reports and compared them to identify commonalities as well as unique perspectives. Join us on June 17 -18, 2026 as we discuss many of these technology trends at FEI’s 2026 Future of Technology in Finance Forum.
Following two years of rapid experimentation, 2026 marks the definitive shift from generative potential to agentic execution. While 2025 focused on the excitement of "chatting" with data, 2026 is defined by the integration of "Digital Coworkers" – autonomous agents that move beyond suggestion to action. However, despite widespread expectations of near‑term ROI, organizations continue to struggle with the transition from promising pilots to production‑ready deployments.
Many believe that 2025 marked the “Peak of Inflated Expectations” for AI use cases, making 2026 the year leaders must cut through disillusionment and re‑anchor on the core tenets of transformation to deliver real value in the age of AI. Increasingly, executives are recognizing that this next phase of AI maturity depends far less on the technology itself than on the organizational foundations built around it. As enterprises rush to deploy Digital Coworkers, they are confronting hard constraints: fragile data architectures, expanding cyber risk, rising AI operating costs, and a workforce unprepared to govern autonomous intelligence at scale. The organizations pulling ahead have acknowledged these realities and have shaped the technology trends for 2026; this article examines the four forces now separating scalable transformation from stalled ambition.
This article reflects the insights drawn from ten leading industry reports – fully cited and listed as references – and is independent, unsponsored, and not endorsed by any firm or organization that authored the source material.
The Rise of the "Digital Coworker"
2025 was the year Agentic AI rose to prominence, introducing systems capable of independent planning and decision-making. In our Emerging Technologies in 2025 article, we highlighted the foundational definitions and "art of the possible" viewpoints shared by thought leaders. Now in 2026, the conversation from many of these same thought leaders has progressed from theoretical potential to practical reality: these systems have evolved into "Digital Coworkers." According to Accenture’s 2026 Pulse of Change, 32% of C-Suite employees now regularly work alongside AI agents, a significant jump from the single digits seen just eighteen months ago.
The focus has shifted from " What is the best prompt for this result?" to "Which process can this agent own?" This transition is reflected in capital allocation:
- Continued Investment Surge: BCG reports that companies plan to double their AI spending in 2026, rising from 0.8% to about 1.7% of revenues. This is reinforced by the Forvis Mazars C-Suite Barometer, which found that 1 in 5 U.S. companies now allocates more than 20% of their total budget to AI, double the global average.
- Supercomputing Investment on the Horizon: Traditional infrastructure limits are driving a shift toward high-performance supercomputing for data-intensive AI workloads. In Gartner’s Top Strategic Technology Trends for 2026 Report, they predict by 2027, about 40% of enterprises will invest in AI supercomputing to speed up innovation and to tackle data-intensive workloads in areas like machine learning, simulation, and analytics
- The Agentic Reality Check: According to Deloitte Insights Tech Trends 2026, while 38% of organizations have invested in agentic pilots, only 11% of organizations have agents fully in production. The barrier is the need to redesign broken processes rather than simply automating them.
While the pressure to deploy "Digital Coworkers" is immense, CFOs must balance this urgency with an honest assessment of their internal process maturity and talent readiness before committing significant capital. Success hinges on strategically navigating the trade-offs between custom-built agents and those embedded in existing third-party platforms to ensure a tangible return on investment. Ultimately, the difference between a career-defining transformation and a costly pilot failure lies in the prudent allocation of funds: investing not just in the technology, but in the structural foundation required to turn promise into reality.
No Data Foundation, No Future
As organizations race to deploy AI-at-scale, a harder truth is emerging: without trusted, governed, and well‑integrated data, even the most advanced technologies fail to generate value. In our previous report, we noted that “Data Quality is a Prerequisite” should be a guiding principles for executives in 2025. Today, while we see that piloting of Agentic AI is widespread, data readiness is not.
- The Indexing Pivot: Organizations must move beyond rigid ETL (Extract, Transform, Load) architectures, which Deloitte Insights identifies as a primary barrier to AI searchability and reusability for nearly 50% of enterprises, toward an indexing methodology that allows agents to consume data with full business context.
- ROI starts at the Core: KPMG’s Global tech report 2026 highlights that in data‑intensive sectors such as financial services, the strongest returns continue to come from foundational technology platforms. More than half of respondents reported that over 40% of their realized digital‑technology value was generated from investments in data, cloud, and security.
- Not just AI-Ready – think Quantum Ready: IBM’s Institute for Business Value notes that as quantum computing approaches practical viability by the end of 2026, organizations with stronger data foundations will gain a measurable competitive edge. A loose “ecosystem” of fragmented data, limited integration, and weak foundations will constrain progress, reinforcing that quantum, like AI, rewards investment in data.
Deploying AI at scale, or quantum computing, is not possible without trusted, governed, and integrated data. AI does not forgive weak data – it amplifies it. AI systems automate judgment, but data quality determines whether that judgment is credible. Increasingly, the competitive divide will not be between companies that adopt AI and those that do not; it will be between those that invested early in data foundations and those now attempting to retrofit them under pressure.
Cybersecurity in the Age of AI: Offense and Defense Reimagined
Prior to 2026, the cybersecurity conversation centered on the initial shock of AI-generated phishing and deepfakes. Now the battleground has shifted toward "Agentic Defense," where autonomous security systems actively hunt threats in real-time.
- Adoption of Proactive Hunting: Leading organizations are utilizing AI to proactively identify and neutralize cyber threats before they can impact the business, swiftly analyze extensive data sets, identify patterns, and detect anomalies, improving threat detection and prevention. Reflecting this momentum, the McKinsey Technology Trends Outlook 2025, reports a notable rise in equity investments, job postings, and patents tied to Digital Trust and Cybersecurity.
- The Quantum Security Horizon: Quantum also presents new security threats, such as breaking through encryption, which are already on tech executives’ minds. KPMG Global Tech Report 2026 notes 41% of respondents say they are worried that they are falling behind in their preparation for the threats posed by quantum computing and their implementation of post-quantum cryptography.
While the efficiency gains of AI are expected to be lucrative, CFOs must recognize that AI is a "double-edged sword" that simultaneously lowers the barrier for sophisticated attackers. Ultimately, the resilience of the enterprise will be determined by whether its defensive AI can iterate faster than the offensive models seeking to penetrate it.
The AI Literacy Mandate
The transition to an agentic enterprise has shifted the "skills gap" from a technical hurdle to a leadership crisis. In 2026, the differentiator is no longer just access to AI, but the fluency to govern and scale it. True AI literacy transcends technical proficiency, requiring a fundamental mindset shift where employees view AI not as a threat to headcount, but as a strategic partner capable of enhancing human creativity and judgment, as highlighted in a prior CFIT article - Leading in the Age of AI.
- The Literacy Gap: While Accenture reports that 43% of employees cite a lack of clear training as their primary barrier to confidence, "Trailblazer" organizations are treating AI literacy as a core competency rather than a specialist concern reserved for those in technology organizations.
- Leading by Example: A significant year-over-year shift has occurred at the executive level. BCG's AI Radar 2026 found that "Trailblazing" CEOs now spend more than eight hours per week on personal AI upskilling. These leaders also allocate 60% of their AI budgets to reskilling – nearly triple the amount spent by their peers.
- Workforce Re-design: PwC’s 2026 AI Business Predictions highlight that as AI agents scale, organizations must rethink work itself—introducing skills like agent orchestration, incentives aligned to business outcomes, and new roles focused on oversight and strategic control. Equally critical is building a culture that actively supports change and adoption, as resistance to new ways of working now poses a greater risk than the technology.
- Talent as a Strategic Asset: As internal reskilling efforts scale, many organizations are simultaneously looking outward. EY's January 2026 CEO Outlook report finds that US CEO M&A intent has surged to 62% ahead of 2026, with AI talent, technology, and digital infrastructure as primary drivers. The rise of the AI-focused acqui-hire signals a sobering reality: for some organizations, it is faster to buy AI fluency than to build it – raising urgent questions about whether acquired capability can substitute for the deeper cultural and organizational transformation true AI literacy demands.
Ultimately, the agentic enterprise will not be defined by how many AI tools an organization deploys, but by how effectively leaders prepare their people to work alongside them. The organizations that win will be those that invest early in literacy, model adoption from the top, and redesign work with intention rather than urgency.
Guiding Principles for Finance Leaders
To navigate and lead through this next phase of AI maturity, finance executives must act on four imperatives:
- Move from Pilot Mindset to Production Discipline. The gap between organizations experimenting with agentic AI and those running it in production is not a technology problem – it is a process maturity problem. The difference between transformation and costly failure lies in an honest self-assessment of readiness before capital commitment.
- Data Foundations Are Now a Competitive Moat. The principle that "Data Quality is a Prerequisite" – a core tenet from our 2025 Guiding Principles — has only grown more urgent. The organizations building strong, governed, and well-integrated data foundations today will hold a measurable competitive edge as both AI and quantum computing reach scale.
- Treat Cybersecurity as Both Offense and Defense. Finance leaders must recognize that AI simultaneously empowers their defenses and lowers the barrier for sophisticated attackers. Resilience will be determined by whether the enterprise's defensive capabilities can iterate faster than the models seeking to penetrate it.
- AI Literacy Is a Leadership Responsibility, Not an IT Function. The most forward-leaning executives are investing heavily in their own AI fluency and allocating meaningful resources to reskilling their organizations. The agentic enterprise will not be defined by how many tools are deployed, but by how effectively leaders prepare their people to govern them.
References:
1 Accenture. Pulse of Change. January 15, 2026. accenture.com;
2 Boston Consulting Group. BCG AI Radar 2026: As AI Investments Surge, CEOs Take the Lead. January 2026. www.bcg.com;
3 Deloitte. Tech Trends 2026. December 10, 2025. deloitte.com;
4 EY. CEO Outlook Global Report. January 20, 2026. www.ey.com;
5 Forvis Mazars. Forvis Mazars C-suite Barometer 2026: US Insights. January 2026. forvismazars.us;
6 Gartner. Top Strategic Technology Trends for 2026. October 18, 2026. www.gartner.com;
7 IBM Institute for Business Value. 5 trends for 2026. January 2026. ibm.com;
8 KPMG. KPMG Global Tech Report 2026. January 2026. kpmg.com;
9 McKinsey & Company. McKinsey Technology Trends Outlook 2025. July 22, 2025. mckinsey.com; and
10 PwC. 2026 AI Business Predictions. January 2026. pwc.com.