Artificial Intelligence

The ‘mad rush’ to install solar panels before tax credits run out

Published

2 months ago

December 16, 2025

Ed Murray has been in the solar business long enough to remember the bloodbath of 1985. That’s when President Ronald Reagan ended Jimmy Carter-era solar tax credits meant to decrease US reliance on fossil fuels following the 1970s oil crisis.

“It was a sad time,” says Murray, who is president of the trade group California Solar & Storage Association. Membership to the organization (which hadn’t yet added “storage” to its name) fell from 670 companies to just 37 “almost overnight,” Murray says, as hundreds of businesses went under without the tax credit. “I hope that that doesn’t happen to us again.” (Then-California Gov. George Deukmejian also cut a state tax credit for solar around the same time.)

The industry has managed to make a remarkable comeback since then, thanks in no small part to Congress reinstating a federal solar tax credit in 2005. Speeding the adoption of renewables like solar was meant to limit Americans’ dependence on foreign energy sources. “By developing these innovative technologies, we can keep the lights running while protecting the environment and using energy produced right here at home,” President George W. Bush, a Republican, said upon signing the measure into law.

“I hope that that doesn’t happen to us again.”

Now, as a result of Donald Trump’s One Big Beautiful Bill Act, the tax credit is set to expire at the end of 2025, and home solar companies face another cliff. Installers are racing to complete projects for customers wanting to take advantage of the tax credit while they still can, even as they face some major headwinds in the process. After that, the industry will have to figure out how to adapt to a very different solar landscape in the coming year.

“It’s a mad rush and it’s crazy,” Murray says.

It wasn’t supposed to be like this; solar companies and their customers thought they’d have the credit for much longer. The 2005 Energy Policy Act reestablished a solar federal tax credit in the US, which the 2022 Inflation Reduction Act (IRA) expanded and extended until 2035. That gave a residential solar customer an income tax credit equal to 30 percent of the cost of installation.

This year, the Trump administration has taken a wrecking ball to the IRA, which was the Biden administration’s signature climate and clean energy bill, as part of President Trump’s pro-fossil-fuel agenda. The Republican-controlled Congress voted in July to wind down the home solar tax credit by the end of the year.

Initially, that led to a spike in customers rushing to install home solar systems before the tax credit goes away. EnergySage, a nationwide solar marketplace in the US, says it saw a 205 percent year-over-year increase in homeowners communicating with installers on their platform after the Republican spending bill passed in July. That trend was echoed by Murray, who is also president of the Northern California-based company Aztec Solar, and other installers The Verge contacted. “It increased sales immensely, and so we have a rush to the end,” Murray says.

The surge in demand, however, has also come with bigger headaches for solar companies who say they now have to work with local permitting offices and utilities similarly scrambling to keep up with the sudden onslaught of installation applications. The lead time to secure a permit has doubled since August to about four to eight weeks for Northern California company Vital Energy Solutions, according to its director of sales and marketing, Kevin McGuire.

“It’s no secret that these permitting offices are just totally overwhelmed because of the flood of customers that are just clamoring to get their systems installed before December 31st,” says McGuire, describing a packed city office in Napa that’s otherwise typically pretty quick to process permits.

“Totally overwhelmed”

California is the biggest solar market in the US, but it’s not the only state where there are delays. Slow permitting processes across the nation have been the “biggest barrier” to reaching an industry-wide goal of driving costs down to about $2 a watt, according to EnergySage director of insights Emily Walker. Long wait times increase overhead costs for companies, turn off customers, and now even jeopardize their ability to qualify for the federal tax credit this year.

Vital Energy Solutions sent out a press release in December saying that 120 of its customers in a single congressional district are at risk of losing the tax credit “through no fault of their own” as installers face delays, and calling on policymakers to grant an extension on the end-of-year deadline. Home project costs can range from $15,000 to $50,000 depending on whether they include batteries, according to Murray, and average about $37,000 according to McGuire — so customers stand to lose thousands of dollars by missing out on the 30 percent tax credit.

“Every step of the process is overextended,” Vital Energy Solutions CEO Jason Jackson, whose father started the company in 1971, says in the release. That includes longer wait times with utilities and supply chain snafus, the company adds.

Southern California Edison has had “significant delays” installing meter socket adapters needed to connect utility meters to solar and battery components between September and November as a result of the recent uptick in applications, Bloomberg reports.

Rising costs and holdups in procuring key equipment including solar panels themselves — exacerbated this year by Trump’s tariff regime — have been another hindrance according to some installers. Vital Energy Solutions has had to resort to scouring local hardware stores for basic electrical fittings as its distributors face shortages, only to find store shelves empty.

Two major solar panel suppliers have also had a tumultuous year. In November, manufacturer QCells furloughed 1,000 workers in Georgia after months of shipments detained by US Customs. Solar components have been under increased scrutiny at ports due to policies barring components suspected of being linked to human rights abuses in Xinjiang province in China. And a fire at manufacturer REC’s Singapore factory in June backed up deliveries to the US.

Virginia-based Ipsun Solar had to redesign and re-permit projects for customers unable to get the REC components they originally planned to use, according to Leon Keshishian, CEO of Ipsun’s parent company, Civic Renewables. The timing couldn’t have been worse. Ipsun saw a fourfold increase in customers since June as news began to spread that Republicans would soon end the home solar tax credit. “We had to kind of turn off new sales at a point because we just didn’t think we’d get them all billed,” Keshishian says. The company is offering to cover the loss of the tax credit for qualifying customers whose projects it couldn’t install before the end of the year due to unforeseen delays, according to Keshishian.

To survive the loss of the tax credit, installers are figuring out how to diversify or lean into other parts of their business. Some are considering offering roofing and HVAC services, or installing heat pumps and EV chargers. Murray and McGuire’s companies both do commercial installations, which they can lean into since those projects are eligible for a commercial tax credit available until 2028. They expect smaller installers, mom-and-pop shops, to face a harder road ahead.

“There’s going to be a lot of people that, when the music stops, they’re not going to have a place to sit,” McGuire says. “Those companies will either get absorbed into other companies, or they’ll go out of business.”

Even so, no one The Verge spoke to is sounding a death knell for the residential solar industry. They expect that rising electricity rates, particularly in communities near new data centers, will still drive demand for home solar systems that can cut down utility bills in the long run. More frequent power outages during extreme weather and explosive wildfire seasons are also increasing demand for residential systems with batteries that can keep a home’s lights on during a larger blackout.

Industry leaders have also come to expect wild swings from shifting regulatory landscapes and have seen the industry pivot in response. This time around, they even see some solar companies benefitting from the loss of tax credits — those that offer “third-party ownership” (TPO) in industry speak. That could be through leasing, or power purchase agreements (PPA) that allow customers to pay per kilowatt hour for solar power from a system the third party owns.

Over the years, the pendulum has swung between customers preferring to purchase solar panels or lease them. “Now it’s going to swing very strongly in the lease direction,” says Brad Heavner, executive director of the California Solar & Storage Association. That’s because the Republican spending bill preserved the solar tax credit for commercial projects for longer and allows third-party ownership to qualify.

Sunrun, a solar company that offers power purchase agreements through subscription plans, managed to successfully lobby for those protections in the bill — arguing that their fleet of solar and battery systems served as a distributed power plant that would help stabilize the power grids as they try to meet the nation’s rising electricity demands. (Republicans also faced flak for slashing tax credits that benefited developers in red states.) Sunrun’s stock is now up about 75 percent year over year.

“Having these as an option is a silver lining.”

“While we do think the industry as a whole will shrink a little bit next year, we foresee Sunrun picking up more market share,” Sunrun president and chief revenue officer Paul Dickson tells The Verge.

Keshishian also expects Ipsun — which installs panels for customers who either purchase or lease their systems — “will absolutely sell more TPOs next year.” Prior to his current venture, he was a vice president at SolarCity, which focused on leasing and PPAs before being acquired by Tesla in 2016.

One of the arguments in favor of third-party ownership is that it can potentially allow customers who might not be able to afford to purchase their own home setup to go solar. But PPAs have also gotten a bad rap for escalating rates that could wind up costing customers more each year — in some cases outpacing inflation for electricity.

The loss of the federal tax credit, however, is driving the creation of new third-party ownership options with lower rate escalators, as well as some lease-to-own models. “Having these as an option is a silver lining,” says EnergySage’s Emily Walker — even though the bill that cut clean energy tax credits “is pretty negative by and large for the solar industry.”

Ed Murray’s advice to other installers after surviving the loss of the tax credit in 1985? “Save your money,” he tells The Verge. “And hopefully we have a regime change with the midterm and we get some kind of tax credit back. It’s a tough business.”

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Justine Calma

Artificial Intelligence

Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical'

Published

10 hours ago

February 3, 2026

miraclelord4k@gmail.com

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:

Artificial Intelligence

Apptio: Why scaling intelligent automation requires financial rigour

Published

11 hours ago

February 3, 2026

miraclelord4k@gmail.com

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

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Artificial Intelligence

FedEx tests how far AI can go in tracking and returns management

Published

13 hours ago

February 3, 2026

miraclelord4k@gmail.com

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

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