As a banking executive in 2025, I have been swept into a whirlwind of exploration about artificial intelligence, the most transformative force in a generation, which is reshaping our industry at breakneck speed. Over recent months I have engaged in candid one-on-one conversations with fellow banking leaders, attended dynamic forums led by Deloitte and Grant Thornton, and delved into hands-on discussions about implementing AI. Banks embracing AI are reaping remarkable efficiency gains, uncovering deeper operational insights, and elevating customer experiences, promises that felt distant just a year ago. Here is my personal story of discovery, interwoven with real-world examples, technical insights, challenges, and a vision for what lies ahead in 2026. Even though I was an early adopter personally, the progress has far exceeded my expectations.
A deep dive into AI's current role
My journey kicked off with private discussions with industry peers who vividly illustrated AI's central role in banking today. They highlighted that AI has moved beyond a visionary concept to a core operational driver, with roughly half of financial institutions already using it in 2023 and estimates suggesting nearly three-quarters will by 2026. What captivated me was the emergence of agentic AI, intelligent systems that operate independently, adapting to fresh data without constant human guidance. These AI agents act as orchestrators, pulling together data, processes, and AI models. For instance, in autonomous vehicles an AI agent processes sensor inputs, applies navigation rules, and uses deep learning to make real-time driving choices. This adaptability is revolutionizing banking, from customer interactions to back-office workflows, risk management, trading, and research.
Agentic AI differs from traditional AI by its ability to autonomously pursue goals, make decisions, and take actions in complex, dynamic environments without constant human intervention. While traditional AI typically focuses on specific tasks like pattern recognition, data analysis, or predictive modeling within predefined parameters, agentic AI leverages advanced reasoning, planning, and adaptability to achieve broader objectives. It can interpret high-level goals, break them into actionable steps, and interact with external systems to execute plans, often learning and adjusting in real time. Traditional AI might analyze customer data to recommend products; agentic AI could independently manage an entire customer service workflow, negotiating, scheduling, and resolving issues. That autonomy, enabled by large language models, reinforcement learning, and contextual awareness, makes agentic AI more proactive and versatile.
Another eye-opener was AI's influence on software development. Tools like GitHub Copilot and Tabnine auto-generate code, troubleshoot errors, and propose optimizations, with industry estimates suggesting up to 40 percent reductions in development timelines. At a Deloitte forum I witnessed how generative AI, agentic AI, cloud computing, and natural language processing empower banks to analyze unstructured data, personalize offerings, and automate intricate tasks at scale. Grant Thornton's sessions deepened this perspective, underscoring AI's power to boost efficiency and innovation while cautioning about risks if not carefully managed.
Opportunities awaiting in 2026
Industry leaders painted a picture of hyper-personalized customer experiences driven by agentic AI. Picture a client receiving customized financial advice, instant portfolio adjustments, and seamless cross-selling offers that feel natural rather than forced, building loyalty and boosting revenue. Operational efficiency also loomed large: automating routine tasks like compliance reviews and data processing could free teams for higher-value strategic work. Risk management stood out as well, with AI systems monitoring market shifts and credit risks in real time. Financial reporting could become a strength, with AI replacing cumbersome manual processes with dynamic dashboards. Regulatory compliance, often a burden, could be streamlined, cutting costs and improving precision.
These possibilities rely on agentic AI's capacity to function as a virtual workforce, independently tackling complex tasks. But a critical insight emerged: none of this works without robust, high-quality data.
Success stories that inspired
A Grant Thornton breakfast with a major investment bank's chief information officer was a revelation. One leader described AI-powered trading and risk management platforms that analyze market data in real time to predict trends and support trade execution with precision. The system also processes news, earnings reports, and market signals, automates trading decisions, and populates risk management dashboards. The firm has automated nearly 40 percent of tasks traditionally handled by analysts in investment banking, trading, and research, including data collection, financial modeling, client presentations, and due diligence.
Their automation of securities filings was equally striking. AI drafts prospectuses and regulatory documents, cutting preparation time and dropping per-filing costs from $10,000 to under $500. Tools similar to JPMorgan's Contract Intelligence platform scan filings for risks and errors, reducing legal costs and saving millions annually by reducing reliance on external legal support.
Deloitte's Zora AI, highlighted at their forum, was another standout. Built on NVIDIA's platform, it automates expense tracking, procurement, and financial reporting. Deloitte's finance team uses it to oversee payroll and marketing expenses, reporting a 25 percent cost reduction and a 40 percent productivity increase. Its real-time monitoring of project costs and progress, flagging delays or overruns instantly, could change how banks manage technology and operations projects.
Bank of America's Erica brought agentic AI to life. Erica analyzes spending patterns, savings goals, and credit usage to create a personalized budget, alerting customers to overspending and recommending investments based on risk tolerance. Bank of America reports Erica has managed over 2 billion interactions since 2018, including 676 million in 2024, with more than 98 percent of clients getting answers within 44 seconds.
Data and the art of the prompt
A recurring lesson was the critical role of robust data. Clean, accurate, well-organized data is AI's foundation. Poor data that is incomplete, biased, or fragmented can lead to flawed loan decisions or trading errors. My own experience at Bank of America, building a large-scale data warehouse and sponsoring data science work in the 1990s, echoed this, as data gaps often derailed insights. Executive management must prioritize and resource data governance, including data remediation.
The concept of the prompt was another revelation, reminiscent of my high school experience crafting BASIC commands. A prompt is a step forward from coding: it is the natural-language instruction, like asking Erica to review a customer's transactions and suggest a savings plan. Its clarity and precision are vital, because vague prompts produce off-target results. Modern AI relies on tools like vector databases, which act as digital librarians, and retrieval frameworks that connect prompts to data. These mirror the logic of BASIC, structuring inputs to unlock meaningful outputs.
Challenges like data silos, where legacy systems fragment data, complicate real-time analytics. Latency in processing large datasets hinders performance, and inconsistent data erodes trust. A range of platform vendors now address these problems, unifying silos, enforcing governance, and supporting prompt design at scale. These are critical components of any bank's AI strategy.
Cost savings that matter
Legal document review can see up to 80 percent cost reductions with tools that extract terms and flag risks instantly. For a bank reviewing 10,000 documents yearly, this could save millions. Operations automation offers 20 to 30 percent savings, potentially saving a mid-sized bank $5 to $10 million by automating half its back-office tasks. Financial reporting promises 15 to 25 percent savings, which could mean $2 to $5 million annually for a large bank. Beyond savings, real-time insights that were unimaginable a few years ago are transforming decision-making.
Navigating the risks
Biased data can lead to unfair outcomes, including discriminatory lending decisions. Banks are countering this with bias detection, transparent data sources, and formal ethical frameworks. Cybersecurity threats such as AI-driven fraud and deepfakes require security-by-design and continuous monitoring.
AI hallucination, where models produce convincing but false outputs, poses one of the most serious risks, undermining trust in critical applications or executing flawed trades. Mitigation includes human-in-the-loop validation, prompt engineering that instructs the model to avoid speculation, fact-checking against trusted databases, and requiring detailed reference citations. Regular model audits help identify and correct hallucination patterns. Regulatory and audit challenges arise when AI decisions lack transparency, which is why requiring citations is critical.
Another risk is unmanaged adoption. A KPMG survey found 44 percent of workers admitted to potentially unauthorized or inappropriate uses of AI, and 46 percent admitted uploading sensitive company information and intellectual property to public AI tools. Employees are taking the AI transition into their own hands, and companies delaying adoption risk unmanaged adoption and everything that comes with it: data insecurity, inadequate controls, and more.
The biggest risk for most institutions is more direct. Evolving too slowly means surrendering the upside entirely to those who move first.
What is coming to financial institutions
Over the next three years AI will enable institutions to deliver hyper-personalized services that strengthen client relationships. By analyzing transaction data, behavioral patterns, and market trends, banks will offer tailored products such as customized loan terms and automated mortgage applications in real time. Generative-AI-refined assistants could handle complex client inquiries with the finesse of a seasoned banker.
AI is already beginning to change underwriting by incorporating non-traditional data such as payment histories to build more accurate credit models. This could expand lending reach while improving default predictions by 20 to 30 percent. For risk management, AI-driven stress testing and portfolio optimization will enable precise scenario modeling, critical for regulatory compliance.
The impact in the investment community will be sizeable. Predictive trading and portfolio management will produce applications that replace fund managers, a transformation already underway. These algorithms analyze market sentiment and news in real time, optimizing portfolios with institutional-grade precision, and for retail clients they can democratize access to sophisticated strategies.
Blockchain and digital assets will act as catalysts, amplifying efficiency, transparency, and innovation. Expect migration to stablecoin payment systems for instant payments, tokenized client profiles feeding AI models with real-time consented data, smart contracts automating compliance tasks and document management, and tokenized securities transactions accelerating settlement.
Where to begin
Begin with an AI overview for the board and C-suite, followed by a deeper program across the organization. Leadership should target specific outcomes: customer experience, fraud prevention, risk management, or process automation. Form teams to evaluate data quality, accessibility, and security, because well-organized data is the foundation of effective AI. Involve people from across departments to align goals and address regulatory compliance, data protection, and ethics.
Start with small pilot projects led by those closest to the problem. Experiment, assess impact, and refine before expanding. Build capability by recruiting AI and data science expertise or upskilling existing staff. Track performance continuously, optimize workflows, and scale what works. Stay alert to regulatory change and ensure initiatives adhere to industry standards.
Workforce implementation
Employees often hesitate, wary of tools that feel disconnected from daily work or fearful of displacement. Companies must create an environment where AI is a partner, not a threat. Encouraging people to experiment with AI in their personal lives can spark enthusiasm. I recently used AI to plan a family spring break: with careful prompting I narrowed lodging options, booked a sailing charter, and found restaurants, all with remarkable ease. Personal wins like that inspire people to champion AI at work.
Designate AI champions at every level, enthusiasts who guide and inspire peers. Role-specific training matters, with hands-on workshops and accessible resources, and champions providing ongoing support. Trust is equally critical: explain transparently how AI functions, address bias and privacy concerns, and ensure secure access to tools. Start with small, high-impact pilots to build confidence before scaling. Leadership must model AI use and align it with business goals. Regular feedback loops and clear success metrics, such as time saved or improved outcomes, keep momentum alive.
Furthermore, agentic workflow belongs in workforce strategy. It will involve new management roles responsible for integrating digital workers, then monitoring and governing them. With both digital and human workers on the job, institutions can plan for greater agility and shift resources more quickly to meet changing demand.
The future workforce
Workforce disruption, with estimates of 300 million jobs at risk globally, demands reskilling. Even more important is ensuring college graduates are ready. This is immediate for me: my daughter begins her master's in accounting at SMU this summer. I recently had lunch with Helmuth Ludwig, a Professor of Practice at SMU's Cox School of Business, who infuses his strategy and entrepreneurship courses with a focus on AI, guiding students to explore practical applications in innovation, decision-making, and business transformation. Drawing on his experience as Siemens' former chief information officer, he challenges students to weigh AI's strategic promise against real-world hurdles like cost, ethics, and scalability. SMU's commitment, underscored by an $11.5 million NVIDIA partnership integrating AI and STEM across disciplines, prepares students to work in a technology-driven field.
All of this matters even more to me as the father of a 12-year-old. For him, the focus has to be on skills, mindsets, and habits that let him thrive in a world where AI is a partner rather than a mystery. For all my children, this century will demand a lifetime of learning and continual adaptation.
A vision for the future
This journey has reshaped my perspective as a banking leader and as a father. AI is not just a tool, it is a catalyst for reimagining the industry. Every company that makes things or provides services, whether banking, lawnmowers, or construction machinery, will shift from manual to autonomous or semi-autonomous systems in the next few years. In banking we can deliver hyper-personalized experiences, optimize operations, strengthen risk management, transform reporting, and streamline compliance, all while reducing cost. But robust data and precise prompts are the foundation.
The workforce impact will be significant. New jobs will be created, some jobs will be lost, and every job will change. AI will streamline routine tasks, reducing operational staffing needs by 20 percent or more and enabling broader management spans. This demands new skills in AI management, data engineering, and prompt design, which we must build through reskilling. Students should prioritize logic, data science, technical writing, statistics, and the humanities, with disciplines like philosophy gaining new relevance for critical thinking and prompt design. Our employees, and our children, will need to partner with AI, with more capacity freed for strategic work that fuels innovation.
Reflecting on this journey, from intimate leader sessions to vibrant forums and practical discussions, I am invigorated by the possibilities. By balancing innovation with responsibility, we can steer banks toward a future where AI drives sustainable growth, empowers people, and redefines what is possible.
