How GenAI Will Transform Financial Services in 2026 and Beyond

Today, we’re diving into:

The Future Use Cases Redefining Financial Services

You and your team are overwhelmed with filings, market fluctuations, and client demands that are becoming increasingly complex by the quarter. One thing is clear from the insights gleaned from Emerj’s conversation with Marco Argenti: it’s not about innovation – it’s about refocusing.

The winning firms won’t have more data. They’ll get better insights from it.

Stop Drowning in Data, Start Winning with Attention

The true advantage of generative AI isn’t simply completing tasks quicker. It’s pointing out what really matters.

Argenti explains it this way:

In the financial industry, that means identifying the clause that changes risk, the metric that changes valuation, or the signal that changes a portfolio strategy.

Saliency-filtered AI models are like super-intelligent filters. They highlight what drives outcomes and filter out what won’t.

For you, the benefit is obvious:

• Faster risk analysis

• More accurate, informed decisions

• Faster training and onboarding for new employees

The benefit isn’t more information. It’s better to focus..

Turn Overload into Opportunity - Personalize at Scale

Your relationship managers could handle 400-600 clients. Hand-crafting personalized insights at this scale simply isn’t feasible.

According to Emerj’s analysis, research indicates that as financial complexity increases, investors reduce cognitive investment. Many people find themselves drowning in the process.

The Boston College Center for Retirement Research and the CFP Board point out the problem: disengagement and regretful decisions.

This is where generative AI breaks the mold.

Imagine an assistant that:

• Alerts you to overnight events related to particular holdings

• Identifies which clients are impacted

• Prepares concise, focused outreach talking points

Your teams are no longer sending generic newsletters.

They’re having genuine, targeted conversations.

That’s not automation.

That’s enhanced advisory value.

Three High-Impact Use Cases You Can Deploy Now

No need for a moonshot strategy, start where the value is obvious.

1) Smart Document Intelligence: AI flags contracts, filings, and research, pointing to the clauses or metrics that actually matter.

2) Client Impact Mapping: Transaction and portfolio information inform event-detection algorithms to identify vulnerable clients in an instant.

3) Executive-Ready Briefing Packs: Build explainable talking points for relationship managers before client meetings. Each application addresses a real-world pain point: clients overwhelmed by complexity, advisors pressed for time.

Keeping the Human in the Loop, Where Clients Expect It

The overriding message from Argenti is clear: AI must support and augment professional expertise, not substitute for it.

Design principles are essential.

Your AI systems must:

• Be able to explain themselves (why was this flagged?)

• Enable human override

• Build and sustain trust through transparency

Your clients do not want decisions made by machines. They want trusted professionals empowered by smart tools. When AI is a co-pilot, not an autopilot, your credibility will increase.

Move Fast Without Breaking Trust: A Smart Adoption Roadmap

Transformation is not about chaos but about execution.

1) Start with a focused pilot.
Validate saliency thresholds on a single business unit

2) Integrate AI into existing processes.
Feed AI outputs directly into CRM systems, no need for separate dashboards that the team won’t use anyway.

3) Establish governance from the outset.
Integrate review gates and accountability into client-facing deliverables.

This approach keeps experimentation in bounds while providing a clear ROI.

Measure What Actually Matters - Return on Attention

The classic KPIs are not capturing the true impact of GenAI.

On the other hand, you should focus on the following:

• Time-to-insight

• Speed of outreach

• Client engagement with younger generations

• Decrease in decision regret indicators

Emerj highlights that there is an increasing difference between what you have and what you can act on. Your metrics should measure how effectively you are closing this gap.

In a noisy world, attention is money.

From Reactive to Proactive, A Real-World Scenario

A regulatory filing comes through overnight.

Instead of digging through hundreds of pages, your AI points out the one paragraph that changes the value. It identifies five vulnerable clients. It crafts three brief key points.

Your relationship manager adjusts the tone, takes the initiative to reach out, and contacts clients before the market rumor reaches them.

The conversation shifts from educating to planning.

From a defensive position to a leadership role.

Your Competitive Moat Is Relevance

Generative AI in the financial industry is not about reducing headcount or shrinking teams. It is about reclaiming focus, providing pinpoint precision, and scaling what matters most. 

The future will belong to the organizations that use AI to remove clutter, turn noise into insight, and turn insight into trust.

Facetune Creator Lightricks to Separate Consumer Apps from Generative AI Unit

You’re not merely observing a corporate shake-up in another tech company. This is a sign that should grab your attention if you are running a business, building a product, or developing a digital strategy.

Lightricks, the parent company of Facetune, has taken a very bold step. It is dividing itself into two separate entities. One will remain focused on its conventional creative products. 

The other, and the more revolutionary of the two, will focus on products that use AI. This is not a random development. It is based on one simple, irrefutable fact: AI is now growing and generating revenue faster than traditional software.

What Triggered the Shift

It’s no coincidence that Lightricks didn’t forget its origins. Facetune raised the bar for mobile editing, but the company is obviously branching out in new ways:

• AI-based product sales are growing much more quickly

• Revenue from premium AI subscriptions is exceeding traditional software sales

• Consumers crave automation over control

This isn’t simply a case of a company growing and changing. This is a case of a company accelerating. The company has stated that its AI division is growing at a rate that is creating an unbalanced scale, one that has led to a restructuring and, ultimately, a split.

When one part of your business accelerates faster than the rest, the strain can fracture your data foundation.

Restructuring and splits amplify the risk. DataManagement.AI provides the intelligent, adaptable data layer that scales with hypergrowth and keeps your information assets unified, governed, and accessible, even when your org chart doesn't.

Why AI Is Winning-And Fast

This paradigm shift is more than just a feature upgrade. It is an indication of a paradigm shift in the usage patterns of tools. 

The forces that are driving adoption are as follows:

• Speed: results are instant

• Accessibility: no expertise needed

• Scalability: one tool, multiple outputs

Monetization: premium AI services can command higher prices

For leaders, this trend is not new. It is reverberating across industries, from insurance underwriting to IT automation, where the same forces are at play.

More Than Just Reorganization

Lightricks is dividing into two entities, and this is a strategic decision, not just an internal restructuring for operational purposes.

Unit 1 deals with the traditional creative tools, such as Facetune and other applications.

Unit 2 develops an AI-first product ecosystem.

Why does this matter? 

Having both entities operate under the same umbrella was stunting growth. AI requires rapid iteration, different skill sets, and more aggressive investment. The traditional, established products require stability and maintenance.

Each side can now move at its own pace.

This is a simple case of minimizing internal friction.

What Competitors Missed-and Lightricks Fixed

Most companies are hesitant at this point. They attempt to “integrate” AI into their existing offerings.

This results in:

• Stagnated innovation

• Confused product offerings

• Weakened user experience

Lightricks did not fall into this category. They opted for structural clarity over incremental progress. This is the key takeaway.

Why Your Strategy is Already Extinct

Facetune didn’t crash or crash out. It just became less relevant as the world whizzed by at a speed that was beyond the capabilities of manual adjustments.

Lightricks recognized this reality early on and took deliberate action.

This isn’t about wiping out history. This is about getting on the same page as where the future is being created.

And if you don't make these same adjustments soon, you won’t just be behind. You’ll be playing in a game that has already passed you by.

OpenAI’s New Power Move to Take Over Big Business

This isn’t just a product launch you’re watching. It’s a strategic takeover in slow motion.

OpenAI’s latest move, a multi-year Frontier Alliance with Accenture, BCG, Capgemini, and McKinsey, is a clear signal: enterprise AI has left the lab. It’s now an operational reality, integrated into the fabric of business.

The Real Strategy: From Tools to Transformation

Deep down, Frontier is more than just another AI tool. It is supposed to be a unified intelligent system that connects all the disparate tools, data, and processes of the enterprise into one cohesive whole.

Why does this matter? It is quite simple: most AI projects fail when it comes to integration, not capability.

Instead of pilot projects in isolation, OpenAI focuses on:

• AI agents are distributed throughout operations

• Seamless system-to-system interaction

• Real-time operational intelligence

This is how AI goes from being “interesting” to essential.

Why Consulting Giants Are the Missing Piece

Here’s the truth that competitors usually want to avoid: it’s not technology that reinvents a business model, it’s execution. OpenAI is addressing this issue by placing its engineers side by side with consulting teams. 

This will mean the following for businesses:

• Define your AI strategy

• Redesign workflows

• Integrate AI into existing infrastructure

• Manage change within the organization

In essence, this fills the significant gap in enterprise AI: scaling implementation.

From Pilots to Full-Scale Deployment

The Frontier model is there to eliminate the bottlenecks that hinder innovation. Instead of a series of small experiments, it seeks widespread, enterprise-wide adoption. 

AI agents will not be relegated to silos; they will be hard at work in the large areas that count:

• Software development

• Sales and customer support

• Internal operations

The takeaway is simple: integrate AI into your workflow, don’t treat it as a side project.

The Business Signal You Shouldn’t Ignore

OpenAI is already getting 40% of its revenue from business customers, and they predict that will go up to 50% in the next year. 

This indicates two things:

1. The adoption rate of business customers is accelerating

2. The competition is heating up, and Google and Anthropic are in the game
   

This is no longer a startup environment. This is a sprint to leadership.

If you are seriously trying to integrate AI into your organization, beyond just dabbling, now is the time to take a look at what real adoption looks like.

The Shift Is Already Happening

This isn’t about what might be coming tomorrow. It’s about what’s happening right now, right here, in how we get things done.

OpenAI is bringing together tech expertise, consulting, and real enterprise needs into one seamless effort. The bottom line is simple: AI-driven change isn’t a luxury; it’s mandatory, and it won’t pause for anyone.

The real question isn’t if AI will redefine your business.

It’s whether you’ll take the lead in that shift or spend your time scrambling to keep up.

Meta’s AI Might Catch Up Faster Than You Think

It is becoming increasingly difficult to support the assertion that Meta is lagging in the AI space. While the headlines focus on the early leaders, new developments indicate a more consistent, strategic burst of momentum.

For leaders of businesses, particularly in the financial services, insurance, or IT sectors, the implications are significant.

It is not hype. It is a discussion about the future.

A Quiet but Aggressive AI Strategy

Meta doesn’t play the same playbook as its competition. Instead of relying on splashy product launches, Meta has focused on laying strong foundations, especially through open-source models and infrastructure.

And it looks like this strategy is finally paying off.

The company’s bets on large language models and increasing compute power indicate that it has a long-term strategy: scale first, monetize later.

For business leaders, this is important because it indicates that Meta is interested in reaching a wide audience and being deeply integrated into people’s lives, rather than just trying to immediately dominate.

And this is a very powerful lever.

Why Meta’s AI Progress Is Underestimated

There are two reasons why people tend to overlook Meta’s progress in AI:

• Its enterprise strategy is not as visible as its competitors’

• It focuses on building the ecosystem rather than monetizing

However, the basics are improving. Improved model performance, faster iteration, and better integration among platforms all indicate that the gap is closing.

What looks like “catching up” may actually be strategic timing.

The Power of Open-Source Advantage

Meta has taken a unique approach with its open-source AI models. This is more than just a philosophy-it’s a distribution strategy.

By encouraging developers and companies to work together on its models, Meta is:

• Enabling faster adoption

• Encouraging industry-wide customization

• Removing the obstacles to innovation

For CEOs and founders, this means ultimate flexibility. You’re not locked into a traditional ecosystem. AI solutions can be customized to your specific needs-whether it’s underwriting automation, claims handling, or customer service.

In the financial services industry, where compliance and customization are most important, this can be a definite competitive advantage.

Infrastructure Is the Real Battleground

Meta has taken a unique approach with its open-source AI models. This is more than just a philosophy-it’s a distribution strategy.

By encouraging developers and companies to work together on its models, Meta is:

• Enabling faster adoption

• Encouraging industry-wide customization

• Removing the obstacles to innovation

For CEOs and founders, this means ultimate flexibility. You’re not locked into a traditional ecosystem. AI solutions can be customized to your specific needs-whether it’s underwriting automation, claims handling, or customer service.

In the financial services industry, where compliance and customization are most important, this can be a definite competitive advantage.

Implications for Financial Services and Insurance

If you are in the financial services industry, the enhanced AI capabilities that Meta is ramping up could dramatically change several key areas.

These are not long-term possibilities. These are near-term applications that have been made possible through improved model performance and more mature infrastructure.

The key point here is that the adoption of AI will move from having access to executing.

A Shift from Leaders to Contenders

The AI race is no longer about who’s leading and who’s lagging. It’s a field of rapidly improving contenders.

Meta’s path foretells a change:

• From dominance to competition

• From closed systems to flexible platforms

• From experimentation to implementation

As business professionals, it changes how you assess AI partners. It’s no longer about who got to market first. It’s about who can scale seamlessly in your environment.

Your opinion matters!

Hope you loved reading our piece of newsletter as much as we had fun writing it. 

Share your experience and feedback with us below ‘cause we take your critique very critically. 

Thank you for reading

-Shen & Towards AGI team