5 technology trends for financial services organizations in 2026

Financial services organizations are entering 2026 under materially different conditions. Margins are always under pressure, regulatory scrutiny remains high, and customer expectations continue to rise. It's not exactly an ideal environment that rewards experimentation for its own sake. Technology investments must deliver tangible outcomes (operational leverage, revenue protection, and defensible differentiation) or they will be deprioritized.

As a result, leadership teams are shifting from buzzword-driven innovation to execution-focused strategy. Artificial intelligence, personalization, automation, integration, and data all remain critical, but success now hinges on how deliberately these capabilities are applied to real business problems. Against a backdrop of rate volatility, rising acquisition costs, and event-driven surges like mortgage refinancing, institutions that move faster, operate more efficiently, and scale without added complexity will outperform. The five technology trends shaping financial services in 2026 reflect this shift toward pragmatic, outcome-driven transformation.

This article will discuss 5 technology trends in the financial services industry, including: 

1. Why AI strategies must produce measurable value
2. Balancing hyper-personalization with trust
3. How refi cycles are shifting the competition to speed and efficiency
4. Scalable infrastructure is becoming more accessible
5. Expect continued pressure for data visibility and data quality

We take a deeper look at each of these trends as well as what financial services institutions can do to better prepare for 2026. 

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1. Why AI strategies must produce measurable value

In 2026, financial institutions will aggressively build AI strategies, but most will still be grappling with what to automate first, why it matters, and how to measure success. The risk doesn’t necessarily center on technical debt, but rather a misalignment of AI use cases with clear business requirements, measurable outcomes, and ROI accountability. Without a clear strategy, businesses get hype-driven pilots that never scale, and AI investments that fail to move key performance metrics.

Agentic AI must be linked to operational metrics

Agentic AI are autonomous systems capable of executing complex tasks with minimal human oversight. Salesforce research reveals that 61% of CFOs say AI agents are changing how they evaluate technology ROI, broadening it to include both cost savings and strategic revenue effects. 

Back-office areas such as loan processing, compliance checks, document handling, and financial forecasting are prime candidates for measurable efficiency gains. For example:

• Risk assessments and expense management (two functions automatable with agentic AI) are explicitly cited by 74% and 54% of firms respectively as top tasks to delegate. 
• Internal Salesforce/IBM data shows that generative AI (not fully autonomous agents yet) has driven 26% increases in productivity in financial contexts where it’s properly aligned to business process transformation. 

But real ROI comes only when institutions define baseline performance metrics, such as cycle time, error rates, cost per transaction. Once they have that figured out, they can deploy agents and measure performance against those benchmarks (something that fewer than half of pilots today do systematically). 

Back-office automation must be tied to specific cost-to-serve reductions, productivity metrics, or risk mitigation goals — not tech adoption calendars.

Customer interactions must be tied to business outcomes

The most visible AI deployments are in customer interactions, from virtual assistants to proactive engagement and contextual recommendations. Salesforce data shows financial customers and service professionals have evolving expectations:

• 76% of consumers expect AI to be standard in financial services within five years.
• 65% believe AI will speed up transactions, up from 46% a year ago.
• Only 15% of customers say their institutions currently exceed expectations on delivering actionable insights. 

Customers want AI-enhanced experiences, but few institutions are currently configured to deliver consistent personalization with measurable business impact.

Deploying AI for customer service metrics like speed to answer or first-call resolution is necessary, but insufficient unless it also drives retention, cross-sell conversions, Net Promoter Score (NPS), or lifetime value improvements. Too often, institutions will measure technology adoption (like “number of bots deployed”) rather than outcomes that touch revenue or risk.

Strategic requirements for AI ROI in 2026

To overcome the “shiny object” trap and build a ROI-centric AI strategy, financial institutions must:

• Define business goals first: Start with desired outcomes (i.e., “reduce loan processing time by 30% in 6 months” or “increase cross-sell conversion by 15% over baseline”). Technologies should be chosen based on their ability to deliver against these goals.
• Treat AI agents like employees: Assign each AI agent specific “job descriptions” with clear KPIs. Measure performance against baseline metrics at regular intervals (30/90/180 days). It’s important to treat agents as autonomous collaborators, not magic miracle workers. 
• Use the right type of AI for the right task: Not all use cases require true agentic autonomy. Sometimes generative AI, analytics, or workflow automation yields better ROI with lower complexity. A disciplined ROI framework, akin to workforce planning, is essential.
• Align data, governance, and integration upfront: Without unified, high-quality data and clear governance, even the best agentic platforms cannot deliver reliable outcomes. Data readiness is a prerequisite for ROI — a theme we’ll explore later in this article.

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2. Balancing hyper-personalization with trust

Hyper-personalization has been a recurring trend in financial services for years, but in 2026 it finally becomes unavoidable. Customers now expect institutions to recognize their needs, context, and preferences across every interaction. The challenge is providing all of that without your customers feeling surveilled. The challenge is all about how to personalize responsibly and at scale across digital and human channels.

Modern hyper-personalization is often reliant on AI, real-time data, and unified customer profiles to tailor experiences across web, mobile banking, email and SMS, social channels, branches, and contact centers. Salesforce research shows that over 60% of consumers expect companies to deliver personalized experiences in real time, and many are willing to switch providers when interactions feel generic or disconnected.

Financial institutions using advanced personalization strategies report higher engagement, improved conversion rates, stronger cross-sell performance, and increased customer lifetime value. Salesforce data indicates that AI-driven personalization can materially improve conversion and retention when it is tied to clear outcomes, not just campaign execution.

However, trust remains the core of the issue here. Financial services customers are highly sensitive to how their data is used. Hyper-personalization must be grounded in first-party data, transparent consent, and clear value exchange. The goal is to feel helpful and relevant (such as surfacing timely financial guidance or contextual offers) rather than intrusive or unsettling. When customers understand why data is being used and see tangible benefits, they are far more willing to engage.

Execution in 2026 requires consistency across channels. Whether a customer is browsing a website, opening a mobile app, calling a service center, or walking into a branch, employees and digital systems must operate from the same real-time view of the customer. Salesforce platforms such as Data 360 and Marketing Cloud are designed to support this orchestration by unifying identity, behavior, and preferences into a single actionable profile.

Ultimately, hyper-personalization will become a core operating capability for many financial service operations. Institutions that get it right will strengthen loyalty, drive measurable revenue impact, and differentiate through trust. Those that get it wrong risk customer attrition, reputational damage, and regulatory scrutiny.

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3. How refi cycles are shifting the competition to speed and efficiency

Financial services leaders should prepare for a renewed mortgage refinancing wave in 2026 as interest rates remain on a downward trajectory. While rate cuts naturally stimulate demand, the competitive edge in a refi boom isn’t just about offering the lowest rate, but also closing faster and more accurately than every other lender in the market.

In refinancing cycles, borrowers shop execution velocity as much as pricing. Faster approvals, fewer errors, and predictable closing timelines are tools to drive conversion, retention, and referral economics. Institutions that lag here risk losing margin and wallet share even if their headline rates are attractive.

Automation and operational efficiency are the core technological levers that deliver on speed. Lenders who have invested in straight-through processing (STP), intelligent document classification, automated underwriting rules, and real-time eligibility checks consistently outperform peers during high-volume periods. These capabilities reduce manual bottlenecks, decrease error rates, and compress cycle times, which translates directly into higher throughput and lower cost per loan.

Organizations using workflow automation and AI-driven review tools see measurable reductions in cycle time and operational cost (key differentiators when volumes spike). In addition, firms that embed AI into their origination systems — intelligently surfacing exceptions, pre-populating data, and automating compliance checks — report meaningful lift in productivity compared to purely manual processes.

From a strategic standpoint, the priority in a refi environment shifts from “build capabilities” to operationalize them with metrics and governance. That means:

• Defining and tracking turnaround time (TAT) from application to commitment
• Setting measurable targets for manual touchpoint reductions
• Measuring error and rework rates across processing stages
• Using real-time dashboards to manage capacity and predict bottlenecks

It also means preparing scalable exceptions handling because even the best automation needs human oversight in more complicated cases. The institutions that get this right are those that have tightly integrated their loan origination system (LOS), workflow automation, and AI tools into a unified operational fabric, with clear KPIs and ongoing performance measurement.

In 2026, speed will be as strategic as pricing. Lenders that invest in automation and operational discipline will convert more refi demand into closed loans, protect margins, and enhance customer satisfaction. All measurable outcomes that directly impact profit and share, which takes us back to the first point of this article.

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4. Scalable infrastructure is becoming more accessible

Over the past few years, major advances in cloud platforms, integration technology, and automation have fundamentally changed what’s possible for smaller and mid-sized financial institutions. Capabilities that once required enterprise budgets and large IT teams are now reachable, cost-effective, and faster to deploy.

We’re likely going to see many institutions shift focus from short-term survival to longer-term scalability. With these tools being more accessible, smaller organizations will revisit how technology can support growth without driving operational complexity or cost.

Here are a few reasons why scaling is returning to the main priorities for all financial institutions. Several market and technology dynamics are converging in 2026, such as: 

• Cloud maturity has reduced infrastructure costs and deployment timelines
• Composable platforms allow institutions to scale incrementally rather than all at once
• Automation and AI reduce reliance on manual processes as volumes grow
• More predictable market conditions create space for strategic, not reactive, investment

Together, these factors make scaling a realistic and necessary discussion for organizations that previously lacked the resources to pursue it.

Salesforce as a scalable growth platform

Salesforce continues to invest in financial services–specific capabilities that help institutions scale operations and customer engagement without over-customization.

Salesforce Financial Services Cloud provides:

• A unified data model for customers, households, and financial relationships
• Industry-specific workflows that reduce build time
• A shared platform across sales, service, and marketing teams

This approach enables institutions to grow product lines, customer volumes, and service capacity without constantly reworking their technology foundation.

MuleSoft allows scale without replacing core systems

For many smaller institutions, legacy cores and disconnected systems are the biggest barrier to scale. MuleSoft addresses this challenge through API-led integration, allowing organizations to modernize incrementally. Some of these benefits include things like:

• Connecting legacy cores, LOS, CRM, and third-party tools without re-platforming
• Reusing APIs to accelerate future projects
• Reducing integration complexity as volumes and use cases grow

Salesforce reports that organizations using MuleSoft experience faster project delivery and improved system reuse, which are critical advantages for lean IT teams.

Marketing Cloud scales engagement and operations

Scalability isn’t just about infrastructure — it’s also about customer growth and engagement. As institutions rebalance away from aggressive deposit acquisition, the ability to deliver consistent, compliant, and personalized communications at scale becomes a differentiator.

Salesforce Marketing Cloud enables:

• Cross-channel engagement across email, mobile, web, and service interactions
• Centralized governance for compliance and brand consistency
• Scalable personalization powered by customer data and AI

This allows institutions to grow engagement volume without introducing operational risk or channel fragmentation.

All these Salesforce capabilities have lowered the barrier to entry, making it possible for smaller financial institutions to build resilient, flexible operating models. Institutions that invest now in platforms like Salesforce, MuleSoft, and Marketing Cloud will be better positioned to grow efficiently, respond to change, and compete in an increasingly dynamic market.

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5. Expect continued pressure for data visibility and data quality

Across both large and small financial institutions, data is a challenge that consistently rises to the top. Not just how much data organizations have, but whether it’s accessible, connected, trusted, and usable. In 2026, data visibility and quality will no longer be viewed as backend IT concerns, but rather recognized as core business enablers, especially for AI-driven initiatives.

The reality is that AI is only as effective as the data it’s built on. Fragmented systems, inconsistent data entry, and poor governance directly undermine the ROI of even the most advanced technology investments. Conversations with financial services leaders consistently highlight two related data challenges:

1. Data availability

• Customer and account data spread across disconnected systems
• Core platforms, CRMs, LOS, marketing tools, and service systems operating in silos
• Limited real-time visibility into customer context and behavior

2. Data quality

• Inconsistent or incomplete data entry
• Duplicated records and mismatched identities
• Lack of standardized processes to maintain accuracy over time

Salesforce research reinforces this challenge. A significant majority of organizations report that data silos and poor data quality limit their ability to deliver effective AI and personalization, despite continued investment in new tools.

As discussed in earlier trends, financial institutions are investing heavily in AI, from agentic back-office automation to personalized customer interactions. However, without unified, high-quality data:

• AI models generate inconsistent or unreliable outputs
• Personalization feels generic or inaccurate
• Automation amplifies errors instead of eliminating them
• Trust erodes with both customers and regulators

At the end of the day, trusted, unified data is a prerequisite for trustworthy AI. Organizations that attempt to deploy AI on top of fragmented or low-quality data struggle to scale beyond pilots. Salesforce addresses these challenges through Data 360, which is designed to unify, harmonize, and activate data across systems in real time.

Salesforce Data 360 enables institutions to:

• Ingest data from core systems, CRMs, marketing platforms, and third-party sources
• Resolve identities across individuals, households, and accounts
• Standardize and clean data through governed processes
• Activate trusted data across sales, service, marketing, and AI use cases

This creates a single, real-time view of the customer that can be used consistently across channels and teams. But technology alone doesn’t solve data quality issues. Leading institutions in 2026 are pairing platforms with operational discipline, including things like standardized data entry and validation rules, clear data ownership and governance models, continuous monitoring of data accuracy and completeness, and alignment between business teams and IT on data definitions.

Salesforce emphasizes responsible data practices through its Trusted AI framework, reinforcing transparency, security, and governance as foundational requirements.

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Partner with TELUS Digital for your digital transformation

As financial services organizations navigate 2026, the common thread across these trends is execution. Technology alone will not drive results, but disciplined strategy, scalable platforms, trusted data, and measurable outcomes will. Institutions that move decisively now will be better positioned to compete, comply, and grow in an increasingly complex environment. 

TELUS Digital partners with financial services organizations to turn digital ambition into operational reality, helping teams modernize platforms, activate data, and deploy AI and automation with clear business impact. For institutions looking to accelerate transformation without adding complexity or risk, partnering with us provides the expertise and scale needed to move faster and with confidence.