The Agentic AI Enterprise Edition
2025 Insights. 2026 Strategic Imperatives
Why managing AI risk presents new challenges
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The difficult of using AI to improve risk management
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How to bring AI into managing risk
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Pros and cons of using AI to manage risks
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Benefits and opportunities for risk managers applying AI
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As we head into 2026, every industry will continue to be affected in different ways by every form of convergence.
We stand at a remarkable inflection point. The technology that analysts have predicted for years is now production-ready. The business case that seemed theoretical is now proven through client implementations. The regulatory frameworks that were nascent are now defined. 2025 was the year we demonstrated what's possible across multiple dimensions. 2026 will be the year we prove it at scale across the convergent landscape.
ArcOne AI is uniquely positioned for this moment. Not because we possess the largest AI models, the most blockchain nodes, or the biggest cloud infrastructure, but because we combine sophisticated AI capabilities with genuine understanding of how revenue intelligence works in banking, energy and utilities, architected for both current operations and the tokenized, embedded, programmable future. We know that dynamic pricing isn't just changing numbers in a spreadsheet. It's balancing customer relationships, competitive dynamics, regulatory constraints, profitability targets, and now, real-time digital asset markets with instant settlement. We understand that exception management isn't just fixing billing errors. It's protecting revenue, maintaining compliance, preserving customer trust, and increasingly, orchestrating smart contract settlements and payment flows. We understand customer engagement intelligence is understanding the customer's changing needs across their interaction journey across months and years.
This domain expertise, combined with our multi-agent architecture, governance frameworks, convergence-ready infrastructure, and commitment to client success, positions us to be the partner that enterprises choose when revenue intelligence moves from aspiration to execution.
— Akshay Sabhikhi, CEO, ArcOne AI
The Agentic AI Revolution - Market Landscape
The Watershed Year for Enterprise AI
2025 will be remembered as the year artificial intelligence transitioned from a promising technology to an operational imperative especially across financial services. While 2024 saw widespread adoption with generative AI, 2025 marked the decisive shift toward agentic AI with systems capable of decision-making, workflow orchestration, and adaptive learning across enterprise operations. The numbers tell a compelling story of accelerated adoption.
IBM's 2025 Banking Outlook revealed that 78% of banks now use AI in at least one business function. More striking is the shift in approach: while only 8% of banks were systematically developing generative AI in 2024, 78% adopted tactical approaches throughout 2025, with many now redefining their strategic approach toward agentic AI deployment.
This transformation didn't happen in isolation. Financial institutions collectively invested approximately $35 billion globally in AI during 2023, with projections indicating this will nearly triple. The urgency is palpable: 60% of banking CEOs surveyed acknowledge they must accept calculated risks to harness automation advantages and remain competitive.
Agentic AI: Beyond Generative Capabilities
Agentic AI represents a quantum leap beyond the reactive content generation that characterized early generative AI implementations. These systems possess the ability to interpret objectives, decompose them into subtasks, interact dynamically with both human users and enterprise systems, execute actions autonomously, and adapt in real-time based on changing conditions. For the human in the loop, this will speed time to understanding and to subsequent action. Other tasks will evolve and require minimal human intervention.
The distinction is critical. Where generative AI enhanced productivity through content creation and pattern recognition and AI agents could handle specific tasks, Agentic AI fundamentally rewires how work gets done. It's the difference between an assistant that drafts emails and an autonomous system that manages entire customer service workflows, from initial inquiry through resolution and follow-up. Read more about Exception Management Intelligence for Utilities here.
For banking, energy and utilities, the sectors where ArcOne AI operates, this capability transforms revenue management from a reactive function into a predictive, strategic operation. Agentic systems can continuously scan market conditions, adjust pricing strategies in real-time, identify revenue optimization opportunities, detect revenue leakage and orchestrate complex workflows that previously required extensive human coordination.
Market Forces Driving Adoption
Several converging pressures accelerated AI adoption in 2025. First, margin compression across both banking and utilities created urgency for operational efficiency. Banks faced the dual challenge of potential deposit flight as interest rates shifted and increasing competition from digital-native competitors. The rapid expansion of AI data centers is driving a significant and unprecedented growth in electricity demand, transforming energy and utilities from traditionally stable, slow-growth entities into a dynamic sector facing major infrastructure challenges and investment opportunities.
Second, regulatory complexity intensified. Evolving guidelines in jurisdictions worldwide is forcing organizations to build governance frameworks and transparency mechanisms. Rather than viewing this as burden, forward-thinking institutions recognized that robust AI governance will become a competitive differentiator.
Third, customer expectations evolved rapidly. Over 16% of global bank clients now express comfort with branchless, fully digital banking relationships, according to IBM 's research. This represents a fundamental shift from mass-market digital offerings toward sophisticated, embedded finance and personalized advisory services.
Industry Analyst Predictions - The Agentic AI Trajectory
AI Agents at Peak Expectations
Gartner 's 2025 Hype Cycle for Artificial Intelligence positioned AI agents and AI-ready data as the two fastest-advancing technologies, both sitting at the Peak of Inflated Expectations. This placement reflects both tremendous potential and significant near-term challenges.
The firm's predictions carry weight: By 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024. Additionally, 33% of enterprise software applications will include agentic AI by 2028, compared to less than 1% in 2024. For banking specifically. Gartner's November 2025 report highlighted that AI agents, autonomous operations, and programmable money will transform customer experiences, enabling banks to deliver significantly enhanced service delivery.
However, Gartner also issued cautionary guidance. The firm predicts over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. This bifurcation will separate organizations that approach agentic AI strategically with clear ROI metrics, robust governance, and phased implementation, from those pursuing technology for technology's sake.
Banking Operations Reimagined
McKinsey & Company 's research, particularly their December 2025 publications on agentic AI in banking, outlined transformative potential across the revenue lifecycle. Their survey of 1,993 respondents across 105 countries revealed that 88% of organizations now use AI in at least one business function, though 62% remain in experimentation or piloting stages rather than enterprise-wide scaling.
The firm identified three critical domains for agentic AI impact in banking operations. For frontline sales, agentic systems can lift relationship manager productivity by 3-15% in revenue per RM and reduce cost-toserve by 20-40% when deployed end-to-end in single domains like prospecting. In corporate credit processes, AI agents can reduce credit review timelines from days to near real-time while maintaining compliance rigor. For operational transformation, McKinsey highlighted ten key domains offering significant reimagination opportunities through multi-agent systems.
The Autonomous Finance Evolution
Forrester 's commissioned study of 559 global technology and business strategy leaders revealed that 88% of financial services leaders agree their organizations need to innovate faster to compete effectively. The firm identifies three transformative areas where agentic AI is reshaping financial services into what they term "autonomous finance."
First, enhanced customer service capabilities extend beyond simple query handling to comprehensive support for account management, loan application processing, and dispute resolution. Second, intelligent operations and automation optimize complex workflows that previously required human expertise, from robotic process automation enhancement to real-time compliance monitoring. Third, hyper-personalized financial guidance democratizes sophisticated advisory services, with 70% of respondents anticipating using agentic AI to deliver tailored customer experiences previously available only to high-net-worth individuals.
Trust, Governance, and Transformation
Deloitte 's extensive 2025 research program identified trust as the paramount barrier to agentic AI adoption. Their poll of over 3,300 finance and accounting professionals revealed that 80.5% believe AI-powered tools will become standard within five years, yet only 13.5% currently use agentic AI. Trust emerged as the leading adoption barrier (21.3%), followed by integration challenges (20.1%) and talent gaps (13.5%).
For commerce and payments, Deloitte projects the digital commerce market, increasingly powered by agentic workflows, could reach $17.5 trillion by 2030, making it a pivotal force in payments and consumer engagement. This represents autonomous systems that make purchase decisions, negotiate terms, and execute transactions on behalf of users or organizations ushering a fundamental reshaping of commercial relationships.
Core Modernization and AI Integration
FIS reported that industry surveys indicate more than three out of four banks have actively launched or are piloting generative AI and agentic solutions, a marked increase from just a year ago. FIS CEO Stephanie Ferris stated during the company's November 2025 earnings call: "We anticipated that AI would transform financial services, but the pace and depth of adoption have exceeded our expectations." FIS's Digital One platform experienced user growth exceeding 30% as banks increased spending on digital integration and open banking capabilities.
Research as stated above reveals a consistent narrative: 2025 marked agentic AI's transition from emerging technology to strategic imperative. Organizations that approach implementation strategically with clear value propositions, robust governance, phased deployment, and partnerships with specialized providers will capture disproportionate advantages in the coming years.
The Convergence Arcs: The Multi-Trillion Dollar Transformation
Stablecoin Inflection Point
If 2025 was a foundational year for agentic AI, it simultaneously represented the regulatory breakthrough for stablecoins and digital assets. The passage of the GENIUS Act (Guiding and Establishing National Innovation for U.S. Stablecoins) in June 2025 ended years of regulatory ambiguity and established the United States as the first major economy with comprehensive stablecoin legislation. This framework goes into effect in December 2026, creating an 18-month window that has triggered unprecedented activity across the banking sector. Stablecoin circulation, which stood at $208 billion in Q1 2025, is projected by Bernstein Research to reach $2.8 trillion by 2028.
American Banker 's December 2025 survey revealed that 63% of bankers view stablecoin legislation as moderately or very likely in 2026, with 51% predicting positive impact for financial institutions.
Tokenization : The $13.5 Trillion Opportunity
Stablecoins represent merely the foundational layer of a broader tokenization revolution. The asset tokenization market, valued at approximately $2.08 trillion in 2025, is projected to reach $13.55 trillion by 2030. Real-world asset tokenization alone has achieved $24 billion in 2025, representing 308% growth over three years. In Europe, tokenized bond issuances exceeded €1.7 billion in 2024, demonstrating that institutional adoption is accelerating beyond pilots to production deployments. Deloitte's 2025 research indicates the number of major banks issuing tokenized assets on blockchain will double in the coming year. Coinbase reports that 76% of companies plan to add tokenized assets in 2026, with some targeting 5% or more of their entire portfolio allocation.
Embedded Finance : The $7 Trillion Convergence
Embedded finance which is the integration of financial services directly into non-financial platforms and customer journeys represents perhaps the most profound shift in how banking products reach end consumers. The global embedded finance market, valued at $104.8 billion in 2024, is projected to expand at 23.3% CAGR through 2034. Bain & Company estimates embedded financial services could represent a $7 trillion market opportunity by 2026, much of it enabled by open banking capabilities. This shift has profound implications for revenue management. Pricing strategies must accommodate revenue-sharing arrangements with platform partners. Product design must consider the constraints and opportunities of embedded delivery. Customer analytics must synthesize behavioral data from partner platforms with internal transactional data. The revenue intelligence systems that excel in this environment will be those architected for distributed customer touchpoints and multi-party commercial relationships.
Open Banking and Open Finance : Data as Strategic Asset
The global open banking market, projected to reach $43.15 billion by 2026 at 24.4% CAGR, represents far more than regulatory compliance. It's a fundamental reimagining of data ownership and portability in financial services. But 2026 represents evolution beyond basic account aggregation toward full-spectrum financial data access. Pensions, insurance policies, mortgages, payroll information, tax data, even cryptocurrency wallets: all flowing through unified API layers. This creates the data foundation necessary for sophisticated AI applications, personalized financial guidance, and real-time credit decisioning.
For revenue management, customer value becomes more visible when comprehensive financial data reveals total wallet share, but competition intensifies when switching costs approach zero. Pricing optimization must account for customers' ability to instantaneously compare offers across institutions. Product development must deliver differentiated value that customers cannot easily replicate by combining offerings from multiple providers through open banking orchestration.
Regulatory Convergence and Compliance as Competitive Advantage
The foundations laid in 2025 through legislation like the GENIUS Act, MiCA, and various open banking frameworks create the certainty institutional capital requires to commit at scale. Institutions approaching compliance strategically rather than reactively will differentiate themselves. Ethical AI governance frameworks become critical when customers and regulators increasingly scrutinize algorithmic decision-making. Operational resilience capabilities enable innovative partnerships that competitors with fragile infrastructure cannot pursue. Real-time regulatory reporting built into platform architecture enables faster product launches versus institutions retrofitting compliance into systems designed without regulatory considerations.
Looking Forward - 2026 Strategic Imperatives
If 2025 was the year agentic AI moved from concept to pilot, 2026 will be the year of scaled production deployment. The experimentation phase has concluded. Organizations now face a stark choice: execute with speed and strategic focus, or risk falling irretrievably behind competitors who do. Organizations pursuing AI without clear business value propositions, robust governance frameworks, or partnerships with domain experts will burn resources and lose market position. Meanwhile, those that approach deployment strategically will compound advantages quarter after quarter.
For banking, energy and utilities specifically, the window for strategic positioning is narrowing. In banking, the institutions that master intelligent offer management, dynamic pricing, and AI-augmented relationship management will capture disproportionate market share as customer expectations for personalization and responsiveness intensify. In energy and utilities, as the business model expands to include market growth and challenges, those that resolve revenue leakage intelligently while positioning the company for growth and business continuity.
Six Strategic Predictions For Revenue Intelligence in 2026
Agentic Revenue Management Becomes Table Stakes in Commercial and Retail
- Banking: By Q4 2026, we predict that 50% of top-quartile commercial banks will have deployed agentic AI for at least one complete revenue management workflow, typically in areas like deal pricing, relationship profitability analysis, or renewal optimization. This represents acceleration from the current 15-20% adoption rate.
- Energy and Utilities Transforms from Cost Center to Strategic Growth Engine: This sector energy will experience a paradigm shift. Unpredictable weather, population growth and the rapid expansion of AI data centers are driving unprecedented growth in demand and transforming this industry from stable, slow-growth entities into a dynamic sector facing major infrastructure challenges. As energy and utilities grapple with the capital requirements to power the AI revolution while facing continued pressure on rate increases and regulatory scrutiny, the "back office" will become the "front line." Revenue assurance, grid modernization, data value, customer experience and exception management will increasingly be recognized as strategic levers to fund infrastructure modernization
- Human-AI Collaboration Models Mature Beyond Simple Oversight: The "human-in-the-loop" paradigm that dominated 2025 discussions will evolve toward more sophisticated collaboration models where human judgment and AI capabilities are architected to complement each other's strengths rather than treating humans primarily as guardrails. We'll see emergence of what we term "collaborative intelligence" with workflows designed from the ground up for human-AI teams rather than AI systems with human oversight grafted on. In revenue management, this means relationship managers focusing on strategic relationship development and complex negotiation while AI agents handle data synthesis, scenario modeling, compliance verification, and documentation with seamless handoffs when situations require human judgment. This evolution addresses the talent challenge emphasized by analysts. Rather than AI replacing humans or humans constraining AI, properly designed systems amplify human expertise while automating routine cognitive work. Organizations that achieve this balance will out-compete those stuck in adversarial human-versus-AI thinking.
- AI Governance Becomes Competitive Differentiator, Not Compliance Burden: The EU AI Act mandates hitting in August 2026 will force a reckoning, but forward-thinking organizations won't view governance frameworks merely as regulatory compliance. Instead, AI that provides explainability, bias detection, audit trails, and risk management will become a market differentiator that customers and partners actively value.
In banking particularly, the institution that can demonstrate not just AI capabilities but also rigorous governance will win enterprise relationships where AI decisions impact financial outcomes. CFOs selecting banking partners will explicitly evaluate AI frameworks as part of vendor risk management. Similarly, regulators will increasingly scrutinize not whether institutions use AI, but how responsibly they deploy and govern it. As customers increasingly distinguish between "AI-washing" and genuine responsible AI deployment, this architectural difference will drive purchasing decisions. - Domain Specialization is Key Differentiator in Enterprise Revenue Management: The trend toward smaller, specialized AI models will manifest decisively in enterprise software. For revenue management specifically, domain-specialized AI that understands banking products, regulatory requirements, customer lifecycle patterns, and pricing strategies will dramatically outperform general-purpose AI attempting these tasks.
This specialization enables AI agents to possess contextual understanding that generic AI systems cannot match: they comprehend deposit product hierarchies, understand rate structures, recognize regulatory compliance patterns, and anticipate customer behavior within these specific contexts. As organizations move towards production deployment, they will increasingly recognize that specialized intelligence, pre-trained models, and domain-specific workflows will command premium value versus horizontal platforms requiring extensive customization.
- Year of consolidation continues with increasing M&A activity: Activity here is predicted to see a strong rebound and potential record-breaking year in 2026, driven by stabilizing interest rates, abundant private capital, and the strategic pursuit of AI capabilities and scale. Companies are moving toward a "buy and build" approach, focusing on smaller, complementary acquisitions to optimize their portfolios and expand within core strengths rather than pursuing high-risk, one-off transformative deals. Agentic AI's transformative impact on the M&A process itself will continue to evolve especially as it revolves around data value, migration and integration across disparate cores.
In Closing
The future of revenue intelligence isn't just automated. It's adaptive, predictive, continuously learning, and architected for digital-native financial infrastructure. It augments human expertise rather than replacing it. It operates within governance frameworks that engender trust. It delivers measurable business outcomes that compound over time across an expanding array of revenue instruments and customer touch points.
Insights From The Arc: The Agentic AI Edition | Editor: Shaku Selvakumar
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