As usual, 2025 was a year of deep congressional dysfunction in the US. But state legislatures were passing laws that govern everything from AI to social media to the right to repair. Many of these laws, alongside rules passed in past years, take effect in 2026 — either right now or in the coming months.
Artificial Intelligence
Meet the new tech laws of 2026
As of January 1st, Americans should have the right to crypto ATM refunds in Colorado, wide-ranging electronics repairs in Colorado and Washington, and AI system transparency in California, among other things. But a last-minute court ruling offered a reprieve from one high-profile state law: Texas’ App Store-based age verification rule.
For a longer rundown of tech-related regulations that go into force in 2026 — including a major piece of one federal law, the Take It Down Act — read on.
California: AI transparency, chatbots, and more
California passed a parcel of AI-related rules last year. The most prominent is SB 53: a transparency law that requires major AI companies to publish safety and security details and protects whistleblowers. It’s a revised version of SB 1047, which Gov. Gavin Newsom vetoed after a heated fight in 2024, and it goes into effect on January 1st, 2026.
Several other bills deal with more specific implementations of AI. Among them is SB 243, one of the first regulations on so-called companion chatbots, requiring them to maintain protocols for preventing suicidal ideation and self-harm, as well as remind known underage users every few hours that the system isn’t human. SB 524, another of the bills, requires law enforcement agencies to “conspicuously” disclose how they use AI.
All this has set up California as a test case for how far state AI laws can push, especially as Donald Trump’s administration aims to ban them altogether. That fight, too, is poised to play out in 2026.
Colorado: Right to repair and crypto ATMs
Colorado passed one of the country’s most comprehensive right-to-repair rules in 2024, requiring manufacturers to facilitate repairs on a large swath of electronic devices. That law, HB24-1121, will finally kick in this year. The state is also adding consumer protections to a major fraud vector: cryptocurrency ATMs, which — because they let users convert fiat money into crypto and send it to an anonymous wallet — reportedly helped scammers extract hundreds of millions of dollars from victims this year. SB25-079 requires daily transaction limits for new and existing customers, plus refund options for first-time users who transfer money outside the US — a major signal they might have been sending money because they were duped by a scam.
Idaho: Speech protections
Idaho joins the long list of states with laws combating strategic lawsuits against public participation, or anti-SLAPP laws, with SB 1001. While this isn’t technically a tech law, SLAPP suits have been a key weapon of tech billionaires like Elon Musk, and limiting them helps prevent what can amount to online censorship. A much-needed federal law remains nowhere to be seen.
Illinois: Public officials’ privacy
Starting this year, Illinois will restrict sharing personal information of public officials at their request. HB 576 covers general assembly members and former members, public defenders, and county clerks, among others, and the covered information includes home addresses, home phone numbers, personal email addresses, and the identity of children under 18. The goal is preventing harassment — an increasingly prominent issue — as officials “administer their public duties.”
Data privacy is another area long neglected by Congress but taken up by states, with highly mixed results. Indiana’s Consumer Data Protection Act aims to provide a “data consumer bill of rights” that includes obtaining, correcting, and deleting personal information a company holds about you. But data privacy and consumer protection groups have denounced the law as toothless — a 2025 privacy report card by PIRG and the Electronic Privacy Information Center (EPIC) gave it an F.
HB 15 is another data privacy framework that failed the 2025 PIRG/EPIC evaluation. Kentucky and Indiana both fall under what that report dubs the “Virginia model”: a framework they allege lets companies “continue collecting whatever data they wanted as long they disclosed it somewhere in a privacy policy,” while making opt-outs onerous.
As with so many regulations, the federal rule banning difficult-to-cancel subscriptions is in legal hell, but some states have been stepping up. Maine is joining them with LD 1642, a rule modeled on the FTC standard — which means, among other things, making companies disclose the terms of subscriptions and offer a cancellation method as simple as the system for signing up.
Nebraska: Age-appropriate design
LB 504 is one of multiple state-level “age-appropriate design” rules — it restricts app features like notifications, in-game purchases, and infinite scrolling for children, aiming to combat compulsive use by stopping “dark patterns” that keep kids online. A similar code was blocked in California, however, so a legal challenge could materialize later this year.
With AB 73, Nevada joins the slew of states trying to curb undisclosed AI-powered electioneering. Its disclosure rules include letting candidates sue if they find themselves starring in unwelcome, unlabeled AI-generated ads.
Oklahoma: Data breach notifications
Oklahoma is broadening the scope of its data breach notification rules with SB 626, including by expanding them to cover biometric data and offering some new safe harbors for avoiding legal damages.
Oregon: Deepfakes, data privacy, and ticket scalpers
HB 2299 adds AI-generated (or otherwise digitally manipulated) imagery to its ban on nonconsensual sexual imagery — a move seen in nearly every state since 2019. HB 2008 bans data collectors from selling personal information and targeting ads using data from users they know are under 16, while adding a similar all-ages ban for precise geolocation data. And HB 3167 bans the sale of software designed to facilitate ticket-scalping bots, addressing a maddening problem the Federal Trade Commission focused on in 2025 as well.
Rhode Island: Data privacy
Rhode Island’s HB 7787, the Rhode Island Data Transparency and Privacy Protection Act, includes rules that require disclosure of how personal information is collected and sold. It rounds out the trifecta of “Virginia model” rules that failed a privacy evaluation and take effect this year.
Texas: AI rules — but not App Store age verification (yet)
Mere weeks ago, Texas was set to implement a new form of online age-gating: requiring app stores to check users’ ages and pass that information to app developers. But a district court granted a preliminary injunction blocking SB 2420. The law remains worth watching, however, because Texas will likely appeal to the Fifth Circuit — which is notorious for reversing lower court decisions on internet regulation.
Texas is enacting HB 149, an AI regulatory framework that prohibits using the technology to incite harm, capture biometric identifiers, or discriminate based on characteristics like race and gender or “political viewpoint.” That’s going to be another test of the Trump administration’s plan to repeal state-level AI laws, highlighting a split between state and federal Republicans on AI.
Virginia: Social media time limits
If you’re under 16 years old in Virginia, your screen time may have just been drastically reduced. SB 854 requires social media companies to verify users’ ages and limit younger teens to one hour of use per app per day. A parent can choose to increase or decrease that limit. Like many internet regulations, this one is being challenged in court, so its ultimate fate remains undecided.
Washington: Right to repair
Washington passed a pair of right-to-repair laws, HB 1483 and SB 5680, in 2025. As iFixit explains, they require companies to make repair materials available for most consumer electronics, block parts pairing, and provide specific protections for wheelchair users.
The RAISE Act has been touted as a landmark AI law that would require large model developers to follow new safety and transparency rules. But it was significantly stripped down at the last minute, lessening its likely impact. Regardless, it’ll take effect on March 19th, 90 days after being signed late last year.
Michigan: Anti-SLAPP and Taylor Swift
Michigan is another state getting a new anti-SLAPP law — HB 4045 — as of March 24th. On the same date, it’s effectuating a package of rules known as the “Taylor Swift” bills, targeting ticket bots and modeled on the federal BOTS Act.
The Take It Down Act criminalized AI-generated nonconsensual intimate imagery distribution at a federal level in 2025, a change groups like the Cyber Civil Rights Initiative (CCRI) called long overdue. But in the words of CCRI president Mary Anne Franks, it included a “poison pill” with a broad, ambiguous requirement that online platforms remove such images rapidly, raising concerns about censorship and enforcement. That platform takedown provision came with a one-year enforcement delay that will expire on May 19th — so we’ll soon figure out how effective (or disruptive) it actually is.
Utah: App Store age verification
Utah’s App Store Accountability Act, SB 142, technically took effect last year. But app stores were given until May 6th of 2026 to start verifying users’ ages with “commercially available methods” and require parental consent if they detect minors. One final piece — letting minors or their parents sue for damages if app stores don’t comply — will take effect on December 31st.
Colorado’s SB 24-205 is a named target of the Trump administration’s war on state AI laws. It requires AI companies to disclose information about high-risk systems, and more specifically, take “reasonable care to protect consumers” from algorithmic discrimination. Originally slated for February, it’s now set to take effect June 30th instead.
Arkansas: Children’s privacy
HB 1717 is a children’s data privacy rule similar to the federal COPPA law and the proposed COPPA 2.0, barring online services from collecting unnecessary personal data if they’re aimed at minors or know a user is underage. It takes effect July 1st.
Utah’s HB 418, dubbed the Digital Choice Act, aims to make social media networks less sticky by letting you move data between them. A writeup from Harvard’s Ash Center explains the nuances, but broadly, it requires social media companies to implement open protocols that allow users to share personal data across different services. Europe has mandated data portability for years and the results haven’t been revolutionary, but there’s still a chance it could promote more competition on a centralized web. Its enforcement date is also July 1st.
Did you think we were done with California AI laws? Well, a delay pushed back the original January goalpost for SB 942, which requires the government to develop standards for AI detection systems and requires covered providers to make such tools available. Now its first provisions kick in on August 2nd, with additional requirements for companies taking effect in 2027 and 2028. It’s taking on a serious issue, but also an incredibly messy one — and like other rules, it depends on preserving the right to state-level AI laws.
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Artificial Intelligence
Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical'
Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality.
Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That’s the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap.
That’s the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space ‘ever since [she] was a corporate baby’, so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate.
“If I were to be very practical, the one thing I’ve noticed is companies jump into adopting AI before they’re ready,” says Sheth. Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up.
Even as recently as 2024, Sheth saw many organisations struggling because their data was ‘nowhere where it needed to be.’ “Not even close,” she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately.
“When companies like that come to us, the first course of order is really fixing their data,” says Sheth. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that.
“Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation,” Sheth adds.
With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model. “They’ve moved from raw data to descriptive analytics, moving into predictive analytics, and now we’re actually setting up an AI strategy for them,” says Sheth.
It is this attitude and requirement for practical initiatives which will be the cornerstone of Sheth’s discussion at AI & Big Data Expo Global in London this week. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,’” says Sheth. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.”
Watch the full video conversation with Ronnie Sheth below:
Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour.
The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling.
“When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” says Holmes.
This shifts the assessment criteria for technical leaders. Rather than waiting “months or years to assess whether things are getting value,” engineering teams can track resource consumption – such as cost per transaction or API call – “straight from the beginning.”
The unit economics of scaling intelligent automation
Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities.
“If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful,” says Holmes. “But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.”
Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. “API calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,” he adds.
To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed.
Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and “not just looking at labour hours that they were saving.”
However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance “back in the hands of the developers into their development tools and workloads.”
Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates.
“Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.”
When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved.
“This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.”
The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.
“I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.”
Addressing legacy debt and budgeting for the long-term
Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.”
A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications – of various maturity stages – to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running.
“Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.”
In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues.
Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.
Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture.
“Because you’ve made those longer term commitments and you’ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says.
Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.
IBM is a key sponsor of this year’s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM’s booth at stand #362.
See also: Klarna backs Google UCP to power AI agent payments
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays.
That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains.
This is where artificial intelligence is starting to move from pilot projects into daily operations.
FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back.
Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention.
How FedEx is applying AI to package tracking
Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen.
According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time.
For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains.
This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make.
Returns as an operational problem, not a customer issue
Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs.
According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility.
This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case.
For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides.
What FedEx’s AI tracking approach says about enterprise adoption
What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist.
This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops.
While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency.
For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients.
What this signals for enterprise customers
For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation.
AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved.
That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems.
FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong.
(Photo by Liam Kevan)
See also: PepsiCo is using AI to rethink how factories are designed and updated
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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