DEV Community: Priya Sharma

Core Banking Modernization: How Banks Can Finally Move Off Legacy Systems in 2026

Priya Sharma — Wed, 07 Oct 2026 11:39:44 +0000

The Legacy Problem Isn't Going Away, But the Solutions Are Getting Better

Here’s a number that should worry every bank CTO: according to Accenture’s 2024 Banking Technology Vision report, 72% of banks still run their core operations on systems built before 2000. Some of these COBOL-based platforms are older than the people who maintain them.

For years, banks have discussed core banking modernization in the same way people talk about going to the gym in January. Everyone acknowledges its importance, but few ever take action. The perceived risks are too high, the costs seem too steep, and the timelines feel too lengthy.

But 2026 looks different. Cloud-native platforms have matured. Migration playbooks have actually been tested in the real world. And digital-first challengers are putting pressure on every bank that hasn’t moved yet. Put together, modernizing the core has stopped being optional.
We’ve pulled together what’s changed, what’s actually working, and how banks of any size can modernize without betting the whole bank on one big launch.

Why Legacy Core Banking Systems Are Breaking Down Now

Legacy core banking systems weren’t always a problem. For decades, they did exactly what they were built to do: process transactions reliably, day after day, without drama. What changed is everything around them. Customers expect new channels. Regulators demand more speed. Competitors move faster. The system didn’t fail. The world sped up.

The Cost of Standing Still

Maintaining a 30-year-old core banking system is expensive. According to Celent’s 2024 report titled "IT Spending in Banking," large banks allocate 65% to 75% of their technology budgets just to keep these aging systems operational, rather than investing in new ones. This expenditure primarily goes toward basic maintenance, rather than developing the digital experiences that customers expect today.

Regulatory Pressure Is Intensifying

Regulators around the world are tightening the rules on real-time reporting, data tracking and operational resilience. The European Union’s Digital Operational Resilience Act (DORA), fully in effect since January 2025, requires banks to prove they manage technology risk properly. Legacy systems make that much harder. They’re hard to monitor, and their connections to other systems break easily.

Customer Expectations Have Permanently Shifted

Customers who use Revolut, Nubank or Chime don’t compare their bank to other banks. They compare it to Netflix and Amazon. When an old core banking system takes 48 hours to open an account, and a neobank does it in four minutes, the gap isn’t about technology anymore. It’s about survival.

What's Different About Core Banking Modernization in 2026

Earlier waves of digital banking change often failed because banks treated modernization as one giant project. Replace everything at once. Pick a launch date. Hope nothing breaks.
That approach caused some famous disasters. In 2018, TSB Bank’s migration in the UK locked 1.9 million customers out of their accounts. It cost the bank more than £330 million to fix the damage, according to the UK Parliament’s Treasury Committee.
The industry has learned from that. Here’s what’s actually different now.

Progressive Migration Replaces Big-Bang Deployments

The most successful modernization projects of the last two years didn’t try to move everything at once. They moved one product at a time. Savings accounts first. Then loans. Then current accounts. The old and new systems ran side by side the whole time.
Thought Machine, Temenos, Mambu and FIS have all built this kind of side-by-side running into their platforms. Smaller moves are easier to test. They’re easier to undo if something goes wrong, too.

Cloud-Native Platforms Have Matured

Five years ago, running a core banking system on public cloud felt risky and new. Today, it’s becoming normal. Google Cloud’s partnership with Temenos, AWS’s banking program and Microsoft Azure’s compliance tools for financial services have all built environments that even cautious regulators now accept.
According to IDC’s Worldwide Banking IT Spending Guide (2024), banks’ cloud spending grew 22% year over year. Core system modernization was the biggest reason why.

AI Accelerates the Migration Itself

One trend that doesn’t get enough attention: banks are using AI to speed up the migration itself. They’re using large language models to read millions of lines of COBOL code, map out the business logic inside it, and write documentation that’s never existed before.
This doesn’t replace human judgment. But it can shrink the discovery and analysis stage from 18 months down to four. For a bank sitting on 40 million lines of undocumented old code, that’s a massive difference.

A Practical Framework for Core Banking Modernization

We pulled this together from patterns we keep seeing in successful projects across North America, Europe and Asia-Pacific. It works.

1. Start with Business Outcomes, Not Technology Selection

Banks that effectively navigate this process clearly define their goals before selecting a platform, whether that's rapid launch, real-time payments, or reducing the cost per customer. Each goal requires a distinct setup, so prioritizing these objectives is crucial.

2. Decouple Before You Replace

Before anyone touches the core, the smart move is to build a layer that sits in front of it, often called a “strangler fig” pattern. It hides the old system behind modern APIs. That buys time for the bigger migration, while new digital features can go live right away.

3. Migrate by Product Line

Start with the simplest products. Savings accounts and term deposits are much simpler than commercial lending or trade finance. Each clean move builds confidence and sharpens the playbook for what comes next.

4. Invest in Data Migration as a First-Class Workstream

Data is where most modernization projects actually get stuck, not the platform itself. Old systems store information in strange, custom formats shaped by decades of workarounds, renamed fields, and quiet exceptions that nobody ever wrote down. It looks like a small problem on a slide. It takes eighteen months to actually fix. Cleaning, mapping and checking that data needs its own team, its own tools and its own schedule. It deserves its own budget, not a footnote.

5. Run Parallel Operations with Automated Reconciliation

During the switch, run the old and new systems side by side. Use automated checks to catch mismatches as they happen, not weeks later. That safety net is what separates a controlled migration from a TSB-style disaster.

Who's Getting It Right: Real-World Examples

Standard Chartered is a good example, through Trust Bank, the Singapore digital bank it backs with FairPrice Group. Trust Bank built its core on Thought Machine’s Vault platform. It proved the model worked in one market before anyone talked about expanding it.

JPMorgan Chase spends more than $15 billion a year on technology. A good chunk of that goes toward modernizing its core systems. Much of its new platform was built in-house, which tells you something about how big a bank needs to be before that choice makes sense.
Neither bank tried to do everything at once. Both proved the model small, then scaled it.

The Challenges That Still Trip Banks Up

Core banking modernization remains hard, even with better tools available. These are the obstacles we see trip banks up again and again.

Talent scarcity. This tops the list, and it isn’t close. The engineers who understand COBOL are retiring faster than anyone can replace them. The engineers who understand modern cloud banking platforms are expensive, and they don’t stay on the job market for long. No amount of new tooling fixes that gap on its own.

Organizational resistance. This one is quieter but just as real. Old systems often hold years of institutional knowledge, and managers in risk and operations know it. Managing that change matters as much as picking the right technology.

Vendor lock-in. Swapping one closed platform for another doesn’t actually fix the problem. Judge vendors on how open their systems are, their API standards, and how easily you can move your data out if you need to.

Underestimating total cost. This is the most common planning mistake. Licensing a new core banking system usually makes up only 20% to 30% of the total cost. Integration, data migration, testing, training and running two systems at once make up the rest. Banks that budget for software alone almost always go over budget.

What Banks Should Do Right Now

If your bank hasn’t started modernizing its core yet, 2026 is the year to move from planning to action. Here’s a realistic place to start:

Assess your legacy systems to understand your technical debt, how everything connects, and the quality of your data.
Build a two-to-three-year migration plan, organized by product line and market.
Choose a technology partner with real banking migration experience, not just a sales pitch.
Set up a dedicated transformation team with executive backing and people from across the business.

The banks that act decisively in 2026 won’t just modernize their technology. They’ll change how well they can compete in a market where speed, personal service and resilience decide who wins.

That gym membership everyone keeps meaning to use? The treadmill’s already running. The only question left is whether you step on, or keep watching from the bench.

Top 10 Agentic AI Companies to Watch in 2026

Priya Sharma — Wed, 26 Aug 2026 08:47:21 +0000

The Rise of AI That Actually Does Things
For years, enterprise AI was mostly about insights. Dashboards, predictions, recommendations — useful, but still dependent on humans to act. That's changing fast.
Agentic AI represents a fundamental shift. Instead of waiting for a prompt, AI agents can perceive their environment, reason through complex problems, plan multi-step workflows and execute tasks autonomously. They don't just suggest the next best action. They take it.

And the market is paying attention. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024 (Gartner, "Technology Trends 2025: Agentic AI," October 2024). Meanwhile, a March 2025 report from MarketsandMarkets projects the AI agents market will grow from $5.1 billion in 2024 to $47.1 billion by 2030, at a CAGR of 44.8%.

This isn't hype. Enterprises are actively deploying agentic AI to automate procurement, accelerate software development, manage supply chains and transform customer service. The question isn't whether agentic AI matters. It's which companies are best positioned to deliver it at enterprise scale.
Here are 10 agentic AI companies to watch in 2026 — selected based on their AI expertise, digital engineering capabilities, enterprise impact, cloud transformation experience and ability to deliver real-world results.

What Makes Agentic AI Different from Generative AI?
Before diving into the list, let's clarify a distinction that trips up even experienced technology leaders.
Generative AI creates content — text, images, code — based on prompts. It's reactive. You ask, it generates.

Agentic AI goes further. AI agents can break down goals into sub-tasks, use tools, collaborate with other agents, learn from feedback and operate with minimal human oversight. Think of generative AI as a brilliant assistant who answers questions. Agentic AI is the colleague who takes ownership of an entire project.

This distinction matters because enterprise AI automation increasingly demands systems that can handle ambiguity, make decisions across multiple steps and integrate with existing business processes. That's the playing field these companies are competing on.

Top 10 Agentic AI Companies to Watch in 2026

1. Microsoft
Microsoft has embedded agentic AI deeply into its product ecosystem. Copilot Studio now lets enterprises build autonomous agents that work across Microsoft 365, Dynamics 365 and Azure. Their multi-agent orchestration framework — built on AutoGen — allows agents to collaborate on complex tasks like IT incident resolution and sales pipeline management. With Azure AI Foundry providing the infrastructure layer, Microsoft is making agentic AI accessible to enterprises already invested in its stack.

2. Google DeepMind / Google Cloud
Google's approach combines cutting-edge research with enterprise-grade deployment. Gemini models power agentic capabilities across Google Cloud's Vertex AI platform, while Project Mariner and Astra explore agents that can navigate the web and interact with the physical world. For enterprises, Google Cloud's Agent Builder framework offers a practical path to deploying AI agents that integrate with existing data and workflows.

3. Salesforce
Salesforce launched Agentforce in late 2024, and it's quickly become one of the most visible enterprise agentic AI platforms. What sets Salesforce apart is context. Their agents operate natively within the Customer 360 ecosystem, drawing on CRM data to autonomously handle service cases, qualify leads and manage marketing campaigns. The Atlas Reasoning Engine gives these agents the ability to plan and refine their actions in real time.

4. Amazon Web Services (AWS)
AWS has taken a characteristically infrastructure-first approach. Amazon Bedrock now supports multi-agent collaboration, allowing enterprises to build agent systems that coordinate across functions. Their partnership ecosystem is broad, and the integration with AWS's dominant cloud infrastructure gives enterprises a scalable foundation for deploying AI agents in production. Amazon Q, their AI assistant for business, is evolving agentic capabilities for software development and business intelligence.

5. ServiceNow
ServiceNow's AI agents are purpose-built for enterprise workflows — IT service management, HR operations, customer service and security operations. Their Now Assist platform leverages agentic AI to autonomously resolve incidents, process requests and orchestrate cross-departmental workflows. What makes ServiceNow compelling is the tight integration with structured enterprise processes, where autonomous action needs guardrails and auditability.

6. Persistent Systems
Persistent has built a differentiated position in agentic AI by combining deep digital engineering expertise with enterprise AI delivery at scale. The company's approach centers on building AI agents tailored to specific industry and functional contexts — not generic copilots, but agents designed to operate within complex enterprise architectures. Persistent's work spans agent design, orchestration, integration with legacy systems and responsible AI governance. Their partnerships with hyperscalers and AI platform providers give them access to leading foundation models, while their engineering-first DNA ensures these agents actually work in production environments. For enterprises exploring how to operationalize agentic AI across the software development lifecycle, Persistent's Agentic AI services and solutions provide a useful reference point for understanding what enterprise-grade agent deployment looks like.

7. Cognizant
Cognizant has invested heavily in agentic AI through its Neuro AI platform and a dedicated AI lab structure. Their focus on industry-specific agent deployments — particularly in healthcare, financial services and manufacturing — gives them credibility with large enterprises navigating complex regulatory environments. Cognizant's acquisition strategy has also bolstered its AI engineering talent pool.

8. CrewAI
CrewAI has emerged as one of the most popular open-source frameworks for building multi-agent systems. While not an enterprise services company, CrewAI's influence on how developers architect agentic AI solutions is significant. Its role-based agent design pattern — where agents are assigned specific roles, goals and tools — has become a de facto standard for multi-agent orchestration. Enterprises and systems integrators alike are building on CrewAI's framework.

9. LangChain / LangGraph
LangChain evolved from a prompt-chaining library into a comprehensive platform for building agentic applications. LangGraph, their framework for creating stateful, multi-step agent workflows, has become essential infrastructure for enterprises building custom AI agents. LangSmith adds observability and testing capabilities that production deployments demand. Their developer community and ecosystem momentum make them a foundational player in the agentic AI landscape.

10. UiPath
UiPath's evolution from robotic process automation (RPA) to agentic AI is a natural progression. Their platform now combines traditional automation with AI agents that can handle unstructured data, make judgment calls and adapt to exceptions. For enterprises with significant existing automation investments, UiPath offers a practical bridge between rule-based bots and truly autonomous AI agents. Their Agent Builder platform, announced in 2025, signals a full commitment to the agentic paradigm.

Key Trends Shaping Agentic AI in 2026

Multi-agent orchestration is becoming standard. Enterprises are moving beyond single-agent deployments to systems where multiple specialized agents collaborate. This mirrors how human teams work — and it's where the real productivity gains emerge.
Governance and trust remain the biggest challenges. When AI agents act autonomously, enterprises need robust frameworks for monitoring decisions, managing exceptions and ensuring compliance. Companies that solve the trust problem will win enterprise adoption.
Industry-specific agents are outperforming generic ones. A healthcare claims agent needs different guardrails, data access and reasoning patterns than a supply chain optimization agent. The companies on this list that invest in vertical specialization will pull ahead.
The build-versus-buy decision is getting more nuanced. Frameworks like CrewAI and LangGraph empower enterprises to build custom agents, while platforms like Salesforce and ServiceNow offer pre-built agents within existing ecosystems. Most enterprises will use both approaches.

What This Means for Enterprise Leaders
If you're evaluating agentic AI companies, focus on three things:

1.Production readiness. Can the company move agents from proof-of-concept to production at scale? Many can demo impressive agents. Far fewer can deploy them reliably.

2.Integration depth. AI agents that can't connect to your existing systems, data and workflows are just expensive demos. Prioritize companies with strong enterprise integration capabilities.

3.Responsible AI practices. Autonomous systems need human-in-the-loop mechanisms, explainability and compliance frameworks. Ask every vendor how they handle agent governance.

The agentic AI landscape is evolving rapidly. The companies on this list aren't just building AI agents — they're shaping how enterprises will operate in an increasingly autonomous future. The winners will be those who combine technical innovation with the engineering discipline and enterprise understanding needed to make AI agents work in the real world.