The Web3 community weighs in on the AI’s intersection with Fintech and blockchain in 2026.
“AI is enabling CFOs to move beyond reporting to prediction. In AP/AR, generative and predictive AI are powering intelligent invoice routing, anomaly detection, automated dispute resolution, and cash-flow forecasting.”
– Jeremy Almond, CEO of Paystand
“For AI startups and new AI groups within existing technologies, it will be feast or famine. Those using off the shelf or lightly customized generic AI will likely, and quickly, find their way to the tar pit.”
“Virtually every financial service provider needs to support complex, objective functions that are poorly served by conventional AI. Only those solutions that can simultaneously and effectively support the interests of the borrower/customer, lender/financial institution, capital partners/shareholders, and regulators have a hope of surviving.
“Slapping a little AI on something may fool some of the venture capital and broader community, but only for a little while. There is a reason why there are very few companies making nuclear reactors or jet engines (technologies that are similarly transformative to AI): the sheer complexity and expert knowledge requirements require massive undertaking and intellectual capital. The same will prove true for AI.”
“Commercialization of end-user driven solutions that act in advisory, clerical, concierge and administrative roles will continue to grow quickly. For banks and lenders, this means setting aside how we traditionally associate risk with given industries, sectors or business function. It is a new day, and credit models need to adapt to these new realities.”
– Pat Reily, co-founder of Uplinq
“We’re still in the early years of AI, and oftentimes, the best-in-class tools change frequently. This means that companies fostering experimentation as new products emerge will see measurable ROI in 2026. The key to success will be ensuring that all teams have a high degree of flexibility in their AI tools, balanced by structured programs with tangible deliverables for experimentation. Companies that celebrate wins publicly, run hackathons to engage skeptics, and avoid the trap of locking into long-term contracts will stand out in the new year.“
– Chen Amit, CEO Tipalti
“On a macro level, Generative AI is the biggest trend impacting businesses of all sizes, affecting how they address everyday problems, how they operate, how they improve visibility, and more. But AI needs to be more than just another over-hyped technology built on hypothetical use cases. Especially in the mid-market, improving technology ROI will come from the solutions that proactively infuse AI directly into the core architecture to address real business challenges.”
– Robert Israch, president, Tipalti
“The companies that can master AI operationalization and digital currency optimization in 2026 will have a significant competitive advantage.
“Converting AI investments into measurable business outcomes is a big challenge. Every company is allocating budget to AI, but the real test is implementation: building versus buying, organizational adoption, and demonstrating ROI. Without disciplined tracking to improve your implementation, you’re just accumulating infrastructure costs.
“Synthetic identity fraud is the next arms race, as our existing verification tools are becoming obsolete. Fraudsters are leveraging AI to create sophisticated fake documents and replicate patterns that were previously difficult to forge, including invoice replication at scale. The challenge companies are facing is that traditional KYC verification methods, like document validation, are easier to bypass as AI-generated identities become more convincing.
“On the proactive prevention front, AI is helping us identify fraudulent invoices and detect anomalies in real-time, but we’re still usually in reactive mode. Fraudsters are adopting AI just as quickly as we are. To stay ahead, we’ll need to fundamentally rethink authentication, moving beyond documents and SMS-based two-factor to adaptive systems that can evolve as fast as the fraud tactics themselves.”
– Manish Vrishaketu, chief customer and operating officer, Tipalti
“The CEO and CFO must align on a clear, budgeted strategy for AI adoption that is fundamentally linked to a future-state operating model and a finance-led talent pipeline strategy.
“CFOs must stop funding AI as fragmented experiments and start treating it as a core capital expenditure for a new operating system. This conversation forces the C-suite to define the clear ROI, governance, and technology stack required.
“The real value in AI is not automation, but re-skilling. CFOs must define how cost savings from automation will be redeployed into upskilling the workforce in high-value areas like data science, strategic analysis, and business partnering.”
– Alex Cedro, VP of finance, Tipalti
“In 2026, AI won’t be something revenue teams ‘adopt’ — it will be the infrastructure they’re built on. CROs are moving from dashboards and retroactive reporting to intelligence that anticipates risk, accelerates time-to-revenue, and guides every step of the customer journey. The organizations that scale AI across their go-to-market engine will unlock predictability, efficiency, and a new level of commercial clarity we’ve never seen before.
“Pipeline reviews will finally escape the land of opinions. Continuous intelligence — fed by product usage, spend signals, service interactions, and intent data — will give CROs a true picture of deal health. In 2026, revenue leaders will identify risk before sales reps feel it, prioritize resources dynamically, and forecast with confidence, levels that were impossible even two years ago.
“AI will take the administrative and repetitive work revenue teams have carried for decades — qualification, routing, follow-ups, personalization, RFP drafting, enrichment, and more. Humans will finally focus where they’re most valuable: strategy, creativity, judgment, and building customer trust. 2026 is the year we stop debating whether AI replaces sellers and start realizing it actually frees them.”
– Michele Shepard , chief revenue officer of Emburse
“In 2026, the focus will shift from massive, general purpose models to specialized, verifiable intelligence. It could well be the year of Auditable Intelligence, where the market demands proof of privacy and provenance.
“Many highly specialized, decentralized AI models are already deployed in high stakes sectors like healthcare and finance with blockchain governance. These vertical solutions will succeed where centralized general purpose models fail, because they solve the critical issues of data sovereignty, regulatory compliance, and domain specific accuracy.
“The market will reward the protocols that can deliver real world impact and verifiable results, making industry specific DeAI the most valuable sector in the coming year.”
“The market has gone from celebrating proprietary models to scrutinizing their lack of transparency. The core issue is that centralized AI is a closed system. Training a massive, general purpose AI without violating these legal boundaries is simply impossible, exposing the fundamental weakness of the approach.
“The next wave of innovation will be defined by open source models and composable AI architectures. Developers and enterprises are demanding the ability to audit, customize, and combine AI components, which is impossible with closed off models.
“The open source ecosystem is already proving that AI innovation is now pluralistic, and this trend will accelerate as regulatory pressure forces more transparency.
“The market is recognizing that the true value in AI is not in the centralized infrastructure, but in the decentralized application layer. The surge in AI token market capitalization is a clear signal that the future of intelligence cannot be built on the same centralized models that govern today’s data economy.
“Investors will realize they were valuing the shovel, not the gold being mined, as the gold is in the decentralized application layer. This is the institutionalization of DeAI. The market is moving from hype to valuing protocols that offer utility and a clear economic model. Tokenizing AI utility is the only mechanism that can align the incentives of the model creator, the infrastructure provider, and the end user.”
“As autonomous AI agents take on more critical roles, the need for an immutable record of their actions, training data, and provenance becomes very crucial. Blockchain is emerging as the essential trust and verification layer for all high stakes AI applications.
“The challenge is conceptual – many people do not understand what technologies like federated learning is, and they certainly do not understand how blockchain makes it better.
“The industry needs a new architectural standard where raw data is kept on local devices and only insights are sent to a secure blockchain. This is how we move AI governance from corporate policy to auditable code, ensuring that the immense potential of AI serves all of humanity, alongside prioritizing ethical integrity and innovation.”
– FLock.io CEO Jiahao Sun