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B2B Payments Are Going Digital and Getting More Personal
November 21, 2023
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The latest business-to-business (B2B) payments innovations are turning personalization into the gold standard of payment evolution. 

That’s what PYMNTS learned from some of the payments industry executives we spoke to for a new series, “B2B Payments: Outlook 2024.”

Call it the commercial currency of convenience. 

The insiders told us that personal touches are increasingly transforming transactions into tailored experiences, helping drive end-user retention and customer lifetime value, even across B2B engagements traditionally defined by net terms and paper checks. 

After all, outside of the signature line there’s nothing personal about a paper check — and in today’s macro environment, just waiting for it to arrive and then clear can be nothing short of personally aggravating for businesses. 

But the rise of digital payments has brought with it a wellspring of new opportunities for businesses and payment ecosystem players to engage with their customers and optimize the relationships they have with their commercial partners, fostering stronger business relationships, and creating a seamless payment experience that all parties can be thankful for.

Particularly as behavioral — and generational — shifts reshape the B2B ecosystem, business customers are now starting to expect a similar level of digital ease, convenience, and visibility over their B2B payments as they get in their daily lives as regular consumers. 

Fortunately, the technology and innovation within B2B payments is already here to give it to them. 

Transforming Transaction Processes

“This is the first generation of digital natives, and they prefer digital payment methods over traditional ones,” Jacqueline White, president at i2c, told PYMNTS.

Taking B2B payments digital also brings the ability to make them personal. After all, millennial or even Gen Z decision makers expect to execute contracts and manage their business relationships with the help of digital innovations.

“All the innovation that’s happening in the consumer space will move into the B2B world, as well,” Anu Somani, senior vice president and head of global payables and embedded payments at U.S. Bank Global Treasury Management, told PYMNTS. 

While B2B transactions are historically much more complex than consumer transactions, the biggest problems frequently equal the biggest opportunities — and advances in cloud-based systems and other enterprise solutions are increasingly making integrating new B2B payment technologies into existing infrastructure setups easier than it has ever been. 

That includes things like providing a menu of payment options for buyers to make payment on, enabling a fully automated and streamlined reconciliation back to the supplier, and even leveraging emerging technologies like artificial intelligence (AI) chatbots for quick and convenient payments. 

Unlocking Growth by Modernizing

While paper checks, an 18th century invention (1762, to be exact), aren’t going away entirely any time soon, faster payments and improved communications between buyers and suppliers are already doing much to cut down the centuries of reliance businesses have built up around paper-based transactions.

“As 2024 dawns, we’ll likely see an increasing number of providers broaden their payments services for buyers to help hasten the shift away from the paper check,” Chris Wyatt, chief strategy and product officer at Finexio explained to PYMNTS. He projected that the use of virtual cards will proliferate and be tied to enterprise resource planning (ERP) and accounting software so that reconciliation is automatic and error-free.

“In general, business customers are no longer accepting of the clunky manual processes long associated with B2B accounts receivables,” Shawn Cunningham, managing vice president and head of Capital One Trade Credit at Capital One, told PYMNTS. 

Firms are reevaluating their payment processes, recognizing the strategic importance of adopting digital and automated solutions. 

PYMNTS Intelligence shows processing vendor payments is rife with friction, and exceptions have proven to be especially onerous. Nearly half of the finance teams surveyed said they spend hours responding to vendor inquiries on those exceptions. 

Ben Lamm, COO at Capital One’s Trade Credit Business, told PYMNTS in a separate discussion, “If you’ve equipped [an organization] with the tools to make customized purchasing offers in real time and reduced the friction for the buyer by giving them self-service capabilities backed by great customer service … all of a sudden, that little old AR program that was running in the background that hadn’t changed in years just became a really powerful growth lever.”

“Removing the manual element from having to process hundreds, thousands, tens of thousands of payments — having to manually post and reconcile these actions — that’s the area that needs a lot of focus [on innovation], because unless you can remove that manual process and the HR associated with it, you’re not going to get the type of adoption for digital payments that both buyers and suppliers want and need,” Dean M. Leavitt, founder and CEO of Boost Payment Solutions, told PYMNTS. “The payment piece is crucial for broader digitization at the enterprise level.”

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September 13, 2026
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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

The reason this is a recurring problem is that companies are keeping highly sensitive information about their customers’ identities, and sometimes even financial transactions, for a long time, and this data is then available to be disclosed to third parties – either in response to valid legal requests, or, as in the case of Revolut, fake ones.

One reason for this is know your customer (KYC) and anti-money laundering (AML) rules. Revolut’s current UK customer privacy notice spells it out: the company generally keeps personal data of UK customers for no more than seven years after the relationship ends, and sometimes longer – for legal reasons.

This means that even if you close your account, your identity documents don’t disappear.

And while the incident with Revolut happened in the financial sector, it’s by no means the only one that requires customers to hand over sensitive identity information. Discord, a popular chat service, said in an October 9, 2025 security update that government ID photos of approximately 70,000 users may have been exposed after a third-party customer service provider got hacked.

This was not a financial service, nor the same type of attack. But the result was similar – because the underlying business process was the same: requiring and storing sensitive identity documents. In the case of Discord, these were used to review age-related appeals.

It’s hard to do anything about a copy of your old passport, or a photo of your face, or a record of your past transactions. These can be used to identify and profile you, and can be used to carry out targeted fraud. And this can happen even if the initial disclosure didn’t result in financial loss.

The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notes, Massachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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