By Renata Sguario, Founder & CEO, Maxme
In boardrooms across banking and fintech, AI is now a line item measured in billions. According to the IMF, the financial sector is on track to more than double its AI spend to around US$97 billion by 2027, growing at roughly 29% a year – the fastest of any major industry.
And yet, the uncomfortable truth is this: around 70% of digital transformations still fail to meet their objectives.
It’s not because the models aren’t clever enough. It’s because we underestimate the human side of the equation.
As a founder working at the intersection of AI, human skills and the future of work, I see the same pattern globally: leaders are over-invested in tools and under-invested in the human skills required to aim, govern and actually use those tools well.
If fintech and banking want AI to pay off in 2026 and beyond, we need to treat human skills as core AI infrastructure.
AI ROI in Banking: Why Human Judgement Matters More Than Models
Let’s start with the money.
Global AI spending is exploding, and finance is at the front of the queue. At the same time, multiple studies from McKinsey, Deloitte and others show that roughly seven in ten transformation programs under-deliver or fail outright, often because of organisational resistance, poor change management and weak capability building.
In other words, the tech is advancing but our human resources aren’t keeping up.
You don’t realise AI ROI just by buying better models. You realise it when leaders and teams can:
- Frame the right business problems (“Where can AI really move the P&L or risk dial?”).
- Challenge data quality, bias and assumptions before they hit production.
- Make transparent trade-offs fast enough to keep pace with the market.
That’s critical thinking, commercial judgement and communication. Classic human skills.
Without these, the fanciest model becomes an expensive proof of concept living in a slide deck questioned by the CEO, instead of the balance sheet
Human Skills Training: The Hidden Engine of Compliance and Customer Trust
If AI ROI is the headline, compliance and trust are the non-negotiable fine print.
Consumer sentiment toward AI in banking is mixed but moving fast. JD Power’s survey showed only 28% of retail bank customers believed AI would make their financial lives better, even as a majority expect banks to offer AI-powered services. Other research finds high trust in digital channels overall, but very low tolerance when AI-driven decisions feel opaque or unfair.
Regulators are starting to respond. From the EU’s AI Act to heightened scrutiny from APRA, ASIC, the FCA and others, expectations around explainability, bias, model risk and governance are tightening by the quarter.
Here’s the key: none of that is purely technical.
When something goes wrong with an AI-enabled credit decision, fraud alert or collections workflow, it’s not the model that’s talking to your customer. It’s people. Your frontline, your risk and compliance leaders, your board.
Human skills are how you:
- Explain complex AI decisions in plain language your customers (and regulators) can understand.
- Listen to feedback and adapt products instead of getting defensive.
- Collaborate across risk, legal, tech and product rather than sitting in silos.
- Hold the ethical line when the commercial upside looks tempting but the risks are murky.
Try as you might, you can’t configure this into your AI models. You train it into your people via deliberate, targeted human skills training: empathy, storytelling, influence, ethical decision-making.
If you want responsible AI in finance, governance isn’t a PDF. It’s a set of lived human behaviours.
Leadership in an AI-Native World: Leading Humans Through Uncertainty
For many institutions, AI is shifting from an “innovation project” to the way work gets done — from underwriting and KYC to trading, wealth and call centres.
In this world, the job description of leadership evolves.
Your primary task as a leader is no longer to be the most knowledgeable person in the room. It’s to be the sense-maker: the one who turns noise into narrative, data into decisions, and fear into focused action.You are responsible for empowering your team, and for breaking down context; all in a psychologically safe environment. Mistakes will be made throughout this process of change, and these mistakes need to be embraced, and collectively learned from.
This requires three big human muscles:
- Clarity of context
People don’t just need to know what is changing; they need to understand why this, why now, why us. Leaders who can consistently frame context across teams, geographies and cultures create psychological safety and alignment — both critical for AI adoption. - Courage to decide with imperfect information
AI makes more data available, but it doesn’t eliminate ambiguity. In fast-moving markets, waiting for perfect certainty is itself a decision — usually the wrong one. Leaders need the courage to act, own the trade-offs, and adjust as new information emerges. - Humility to learn in public and course-correct
In an AI-native environment, no one has a 10-year playbook. The best leaders model curiosity, ask naive questions of their data and models, and are willing to say, “We got that call wrong; here’s what we’re changing.”
These aren’t “soft” in the sense of optional. They’re hard to build and central to performance.
Future of Work in an Age of AI: Required Skills for Fintech Leaders
So, what should fintech and banking leaders prioritise in 2026?
Beyond the specific tech stack choices, the future of work in an age of AI will reward institutions that invest in a very specific cluster of human capabilities:
- Critical thinking and problem solving – to target AI where it truly matters.
- Communication and storytelling – to bring customers, regulators and teams along.
- Collaboration across disciplines – to design AI systems that are safe, compliant and commercially sharp.
- Ethical reasoning and courage – to make decisions you’ll be proud to defend five years from now, not just at the next earnings call.
- Learning agility – because whatever you implement in 2026 won’t be the same in 2028.
These are precisely the capabilities that traditional technical training and leadership programs have tended to underweight. But they are also the capabilities that research shows make transformations more likely to succeed. McKinsey, for example, links successful digital transformations to practices clustered around leadership, capability building, empowering workers and communication — not just tooling.
The future of AI in finance is deeply human.
The Bottom Line: Invest in Human Skills First
If I had to give fintech and banking leaders one simple recommendation, it would be this:
If you want AI ROI, regulatory confidence and better decisions in 2026, invest in human skills first.
Treat judgement, communication, collaboration and courage as seriously as you treat model performance and data quality.
That means:
- Putting your leaders through leadership workshops and training that specifically focus on AI-era skills: problem framing, ethical decision-making, cross-functional collaboration and context-setting.
- Building structured human skills training for managers and frontline teams so they can handle AI-enabled customer interactions and compliance conversations with confidence.
- Measuring not just the throughput of your models, but the quality of the human decisions wrapped around them.
AI will absolutely transform your tech stack.
Human skills will decide whether it transforms your business.
And in a market where every institution will soon have access to similar tools, it’s the people who know how to use them — wisely, ethically and courageously — who will win.
About Author:
Renata Sguario is Founder & CEO of Maxme, a human skills development company, and the current chairman of the board of Future First Technology (formerly known as PS+C Limited), listed on the ASX (FFT), one of Australia’s leading end-to-end ICT and digital consulting organisations. Ren has more than 30 years of experience in people management and graduate recruitment programs with leading organisations such as Telstra, NAB and Latitude Financial Services.
