By Alex Mifsud, CEO and Co-Founder, Weavr
In the relentless pursuit of growth, few industries pivot more frequently — or more fervently — than B2B SaaS. For product leaders and founders alike, the question is rarely should we innovate, but where to place our bets. Two clear front-runners have emerged in recent years: artificial intelligence and embedded finance. One dominates headlines. The other, perhaps more quietly, is embedding itself into the fabric of everyday product roadmaps.
We recently commissioned an independent survey to understand how SaaS companies are navigating this decision. The results were striking — not for what they predicted, but for what they revealed about the tension between promise and practicality.
Confidence vs. Commitment
At first glance, AI appears to be winning the narrative. 91% of SaaS leaders surveyed said they were confident in their ability to monetise AI. That’s a remarkable statistic, and it speaks to the sheer level of excitement — and belief — surrounding the technology.
But confidence doesn’t always equate to clarity. In many cases, the monetisation path for AI is still conceptual. Companies are largely viewing it as a way to augment existing workflows or add intelligence to existing features. What’s less defined is how those improvements translate into incremental revenue. A smarter dashboard or automated workflow can enhance stickiness, but it’s rarely something you can charge more for on its own.
Where does embedded finance (EF) sit, as a priority for SaaS product managers, against AI?. Only slightly fewer respondents (85%) expressed confidence in monetising embedded finance — a still-impressive figure, albeit marginally behind AI. But what’s notable is the level of active commitment: 61% of companies say they will be pursuing embedded finance in 2025, compared to 51% for AI. And nearly 95% of those planning to embed payments expect to go live within the next 10 months.
In other words, embedded finance may, after the hype of the last few years, be less headline-grabbing, but it’s actually being deployed rather than talked about — and monetised — at scale.
Different Tools for Different Goals
One way to interpret this divergence is through the lens of use case. Many AI initiatives are internal: customer support bots, predictive analytics, smart workflows. They promise efficiency, and for good reason — these can significantly reduce operational costs or improve customer experience.
Embedded finance, by contrast, inserts monetisable services directly into the user journey: payment accounts, virtual cards, lending products. It creates new revenue streams, often from existing users. We’ve seen businesses increase average revenue per user two-to five-fold by offering embedded financial services that fit naturally into their product workflows.
It’s a classic difference between augmenting value and creating value. AI is largely used to make what’s already there more efficient. Embedded finance builds entirely new propositions — and directly generates revenue fromthem.
Conceptually, choosing between AI and EF is a false choice – they’re not in conflict on principle. However, budgets and resources dictate prioritisation, so what is a SaaS product manager to do?. Here’s the surprising conclusion: if near-term revenue growth is the objective, the business case for embedded finance is arguably more direct as well as more urgently pursued, according to our survey results.
A Response to Competitive Pressure
Another interesting signal from the research: 43% of companies say they are losing business to “one-stop-shop” competitors. This goes beyond any single innovation trend — it reflects a broader shift in buyer expectation. Businesses increasingly favour tools that don’t just solve one problem, but streamline multiple workflows in a single interface.
This is where embedded finance becomes especially strategic. By baking payments, expenses management, or financial provisioning into a platform, SaaS companies not only increase utility — they reduce the need for third-party tools. That’s a retention and monetisation win rolled into one.
In fact, 51% of our respondents cited stemming churn as their top business priority. For those leaders, embedded finance offers a way to increase switching costs without relying on lock-in — it creates real, usable value that keeps users engaged and dependent.
Friction Isn’t Failure — It’s a Design Challenge
None of this is to say embedded finance is easy. The same research highlights concerns about compliance, financial risk, and managing relationships with banks and card schemes. These are not trivial problems. But they are increasingly solvable — especially through platforms designed to abstract complexity while maintaining regulatory rigour.
This is where we see the conversation evolving. Companies are no longer asking if embedded finance makes sense — they’re asking how to implement it with minimal overhead.
The Case for Both — A Symbiotic Opportunity
There is, as hinted earlier, room for both. Together, they can create intelligent, automated workflows that make financial activity both more seamless and more value-generating.
1. AI increases productivity through intelligent automation
AI, especially large language models and agentic systems, is transforming productivity by automating repetitive workflows, making predictions, and assisting in complex decisions. According to McKinsey, generative AI could add up to $4.4 trillion annually to the global economy, with the biggest gains in areas like customer operations, software development, and finance.
2. But AI’s automation potential is capped without access to financial actions
Many business processes rely not just on decisions but on financial execution—paying suppliers, reimbursing employees, issuing credits, allocating budgets. If the AI can only suggest a course of action, and not carry it out, productivity gains hit a ceiling. This is a common limitation in current AI rollouts in ERP, procurement, and HR tech.
3. Embedded finance unlocks the ability for AI to execute financial tasks
Embedded finance acts as a bridge between decision-making and execution when financial activities are involved in a business process. By embedding financial capabilities like the means to use virtual cards to make purchases, and accounts to initiate bank payments directly into software, EF allows AI agents to not only recommend financial actions but to perform them—e.g., issue a payment, request an approval, reconcile an invoice. This would, for the time being, require some supervision – for instance, a final approval step in the form of strong customer authentication from a human. But only for the time being, not unlike full self-driving in EVs requiring, for now, the driver to keep their hand on the steering wheel.
This is especially powerful when paired with agentic AI, which can independently initiate actions across systems. Without embedded finance, agentic AI is like a smart assistant with no bank account – it’s all talk, no enaction.
4. AI can enhance the intelligence and adaptability of embedded finance
Just as EF makes AI more operationally useful, AI makes EF smarter and more context-aware. AI systems can analyse rich contextual data to optimise financial decisions:
- Choosing the most cost-effective supplier
- Allocating spend to the right budget line
- Timing payments for maximum cash flow benefit
- Detecting fraud or policy violations in real time
In spend management platforms, for example, AI could learn from historical transactions to automatically route payments, suggest pre-approvals, or issue dynamic spending limits—all using embedded financial tools to carry out these decisions.
5. AI + EF enables intelligent, autonomous financial workflows
The ultimate synergy is the emergence of self-operating business workflows—where software can:
- Detect a business need (e.g., a new hire needs onboarding spend)
- Make a compliant financial decision (e.g., issue a virtual card with a pre-set budget)
- Execute and monitor the transaction automatically
This transforms the role of software from static system-of-record to a dynamic system-of-action, enabling “zero ops” financial workflows with minimal human intervention.
Strategic clarity is important. If your objective is margin expansion or new revenue per user, then it’s worth distinguishing between technologies that accelerate what you already do, and those that allow you to offer something fundamentally new.
AI is powerful. It promises to reshape the SaaS landscape through intelligence and automation. But its full potential is only realised when paired with systems that let it act, not just think. Embedded finance plays that role — giving AI the tools to transact, not just analyse.
Conversely, embedded finance also benefits from AI’s intelligence. Together, they don’t just streamline workflows — they create entirely new ones. Workflows that detect needs, make compliant decisions, and execute them autonomously.
So while AI and EF may seem like separate paths, the real opportunity lies in how they converge — and how that convergence drives both monetisation and differentiation.
AI will shape the future. But embedded finance is shaping the present — and when the two are brought together, the future arrives faster.
That is a dual carriageway that’s worth taking seriously.
