The CFO Guide to Tech Trends 2026

The future of finance: A CFO guide to AI-driven tech trends

Tech Trends 2026 focuses on making impact with artificial intelligence (AI)—matching the shift from adoption to innovation as reported in Deloitte’s State of AI in the Enterprise.¹ Learn five ways AI will disrupt and change how work gets done—and open opportunities for the finance function. Our Finance Trends 2026 research shows most finance departments piloting AI use cases, with 63% actively using AI solutions.²

Today’s finance leaders are stewarding stability while architecting transformation. More than half of our Finance Trends respondents play a leading role in influencing strategy across the organization—and those same respondents also seem further along in their AI journeys within finance. As these trends reinforce, the next chapter for businesses, including finance, isn’t likely to be comfortable or predictable—and it’s shaping itself faster than ever. We see a future of finance in which the function’s role has evolved to…

• Finance for finance: Driving efficiency and control in operations within the four walls of finance through advanced AI and technology, enabling lower costs and increased quality and speed.
• Finance for the enterprise: Using predictive modeling and AI/machine learning algorithms; improving resource allocation and risk management; and enabling growth, higher margins, and better cash flow to support business decisions.
• Finance for the market: Boosting trust, investor confidence, and competitive edge by clearly communicating value-creation strategies and positive signals; supporting higher total shareholder return (TSR), liquidity, and credibility; and lowering risk.

Next, learn how CFOs can strategize for each trend, embrace new possibilities, and elevate their function’s value.

The rise of AI-enabled robotics

AI-powered robots aren’t just on factory floors; AI-enabled drones and autonomous vehicles are increasingly common in smart warehousing and supply chain operations, and robots are even supporting restaurants. As organizations overcome scaling barriers, AI-enabled robots will likely become mainstream. In years to come, we may even see humanoid robots.

Enterprise applications like warehousing and logistics remain the deployment testbed: BMW is testing humanoid robots to handle tasks that industrial robots can’t do. A larger opportunity is in consumer markets, such as elderly and disability care, cleaning and maintenance, meal preparation, and laundry. The Bank of America Institute projects the material costs of a humanoid robot will fall from $35,000 in 2025 to between $13,000 and $17,000 by 2035.³

Why CFOs should care about AI-driven robots

Finance for the enterprise: Two big reasons—cost and return on investment (ROI).

Implementing physical AI may change an organization’s products and services, affecting its revenue, cost of goods sold, and operations. This can lead to movements in manufacturing, warehousing, quality control, inspection, and more. The CFO may influence dialogue around operational and financial key performance indicators (KPIs) so those costs and benefits are reflected well on the balance sheets—creating competitive advantage. They’ll need to bolster ROI measurement on cost of operations from moving to a hybrid human/AI workforce.

For finance talent, CFOs may consider creating career pathways to build business acumen in physical AI, with an understanding of the issues at stake, questions to ask, and unlimited potential of this technology.

Preparing for a workforce based on agentic AI

Enterprises love agentic AI’s potential for autonomous operation and intelligent execution. Gartner predicts that agentic AI will make 15% of everyday work decisions and augment 33% of enterprise software applications by 2028.⁴ But three core infrastructure obstacles may keep organizations from realizing agentic AI’s potential: legacy system integration, data architecture constraints, and governance and control frameworks.

Today, most enterprises aren’t able to capitalize on these agents yet. But leading organizations are unlocking it through strategic process redesign, architectural modernization, and new governance frameworks. Advanced organizations with a workforce that merges agentic AI and human capabilities may gain competitive advantage in most industries.

Why CFOs should care about agentic AI in the workforce

Finance for finance: Intelligent tech and an adaptive workforce empower a future-forward finance team.

CFOs should use both to drive both commercial and operational transformation considering: How does this change the organization’s goods or services and work processes, and how can we drive experimentation?

AI innovations will be built on core and edge technologies, enabling agility and even a new class of data to take bigger bets and make faster, more confident portfolio decisions. Agentic teams could augment human ones to execute finance work, and core human competencies of critical thinking, curiosity, and ethics should be balanced with those new technologies. But as these agents are deployed in different ways, CFOs may need a reality check. As costs change, what will be the most useful and cost-efficient deployments? In some cases, human labor may be more cost-effective than agents. Service costs and delivery models may shift as some tasks may require less agentic input than others.

Finance can help ensure efficient deployment using resource tagging, real-time monitoring, and automated resource management, plus strong governance frameworks to manage costs and expenditures.

AI infrastructure: Optimizing compute as inference costs rise

AI has moved from proof of concept to production-scale deployment, and enterprises are finding that their infrastructures are not built for AI’s growing demands. For one, AI consumption and spending have surged: Inference usage has dramatically outpaced cost reduction.

As organizations grapple with those costs, some are looking at on-premises deployment, which can be more economical than cloud services for the high-volume AI workloads.

Why CFOs should care about AI inference economics

Finance for the enterprise: An organization can succeed by navigating AI economics with precision while also fostering a learn fast, fail-safe culture to drive exponential outcomes.

Enterprises are facing more data-center-driven costs and new infrastructure demands. AI workloads are power- and cooling-intensive, and graphics processing unit capacity is constrained, pushing organizations to evaluate colocation or build out their own facilities. Regulatory requirements, geopolitical concerns, and data sovereignty are driving some workforce and infrastructure choices. Large language model (LLM) tools can become cost-prohibitive at enterprise scale, and some organizations are billed tens of millions of dollars for AI use. In particular, agentic AI, with continuous inference, can spike token costs. CFOs can convert tokens into enterprise value—but only if they manage tokenomics volatility.

Given these challenges, an environment that supports fast learning and development is key: How can CFOs simplify 60-day build-and-test cycles while ensuring clear traceability to token use with tight accountability and usage controls?

Architecting an AI-native tech organization

AI is rewiring how an IT organization operates and spends. Seventy-eight percent of tech leaders anticipate integrating AI agents into their architecture workflows over the next five years—without signs of slowing.⁵ While there is no one way to structure a tech function—and to pay for it—an AI-powered tech organization can be leaner, smarter, and faster.

AI is changing how teams are built and led, and agents and people are being integrated into teams faster than most companies are ready for. Nearly 70% of tech leaders plan to grow their teams as an augmentation and specialization strategy in response to Generative AI.⁶ With the support of AI, tech functions are gaining influence: 66% of large enterprises look at their tech centers for revenue instead of just service.⁷ No longer just operational concerns, AI and tech are central to a business’s growth, innovation, and competitive edge. 

Why CFOs should care about partnering with their IT orgs

Finance for the market: These AI-powered tech organizations’ partnerships with the C-suite hold lessons for finance leaders.

The CFO can partner with and learn from their CIO, understanding how to work with IT, integrating where possible, and recognizing how they can be allies—even bringing mature IT concepts into finance. A CFO could even wonder: What is the operating model in a product-based environment in which IT, HR, and finance serve as seamlessly integrated partners in product-centric outcomes? A future-state finance function has optimized AI investments to automate tasks, and its humans have become optimization strategists who focus on process analytics, AI model management, and data integrity. CFOs can also use AI to get closer to the commercial process and be part of those embedded workflows.

Finance organizations should invest in continuous learning and reskilling so teams can adapt to new technologies and evolving business outcomes.

The AI dilemma: Securing and leveraging AI for cyber defense

AI’s promise for organizations comes with security risks, and some of the more pressing originate from inside. The risks manifest across four domains: data, AI models, applications, and infrastructure. However, AI can be used to mitigate the risks it can create. AI-powered cybersecurity solutions can help organizations operate at machine speed and adapt to evolving threats in real time, as well as identify patterns that humans miss, speed up threat response, and anticipate attacks.

Those capabilities are changing how organizations manage cyber risks. But at the same time, most of the practices that secure AI are not new. They are simply updated to address new risks, such as enforcing strong software development life cycle approaches and enforcing basic security measures. 

Why CFOs should care about AI cybersecurity

Finance for the enterprise: While finance already stewards enterprise risk management, it can steward the enterprise’s assets as well.

That means the CFO needs to invest thoughtfully in cyber defense alongside the organization’s core AI investments and balance internal and external commitments. A partnership with the CISO, who understands security controls, could help the CFO, who understands security governance and controls, better understand risks (and vice versa)—which, in turn, can help determine more efficient capital allocation for cyber safeguards, as well as new governance and controls. Finance leaders will want to increase governance of shadow AI deployments and reporting and feedback loops.

Finance could leverage the capabilities of its “citizen developers,” which will require governance as well. CFOs should have a learning mindset—because just as cyberattacks will keep evolving, so will their role in how to manage their effects.

Moving forward

As the CFO role evolves, it’s up to finance leaders to anticipate and prepare for what’s next.

Organizations face unprecedented technological shifts, and finance leaders can help shape their future by leveraging technology, elevating finance talent, and becoming architects of strategy and partners of business innovation.

By looking at Tech Trends 2026 through our lens of finance for finance, finance for the enterprise, and finance for the market, CFOs can explore how these trends may impact them now and down the road—and how they can leverage what’s next to secure an organization’s strategy, competitive edge, and resilient future.