These car brands could suffer the most with soaring gas prices

By Todd C. Frankel and Federica Cocco | The Washington Post on March 21, 2026

The new head of Ram trucks was confident, defiant, when he announced the return of the legendary, powerful, gas-guzzling Hemi engine.

“Ram screwed up when we dropped the Hemi — we own it and we fixed it,” chief executive Tim Kuniskis said.

The Ram website was more blunt: “**** YEAH, THE HEMI V8 IS BACK.”

The 2026 Ram 1500s equipped with the legendary V-8 engines get only 12 to 19 miles per gallon, making them among the least fuel-efficient of any new vehicle, according to Energy Department data.

But when the decision to revive the Hemi was announced last summer, national gas prices were averaging just $3.02 a gallon and on their way down to $2.70 by year’s end. The political winds were favorable — the Trump administration was moving to water down fuel efficiency standards. And Ram executives figured Americans would put the engine’s roar ahead of any worries about miles per gallon.

Now, though, the math looks very different, for both automakers and drivers.

The Iran war has pushed up gas prices about 33 percent in just three weeks, adding nearly $1 to the average gallon. This week, the nationwide average gas price hit $3.91 — the highest in almost four years.

With the International Energy Agency calling the war “the largest supply disruption in the history of the global oil market,” most experts don’t expect prices to come down soon.

Regular gas averaging $4 per gallon seems certain in the coming weeks, experts say.

“It’s more like a ‘when’ than an ‘if,’” said Patrick De Haan, petroleum analyst at GasBuddy.

A $4 national average would mean $5.79 a gallon in California, a state with among the highest prices, and $3.34 in a cheap-gas state such as Kansas. Gas prices vary depending on state taxes and fees, fuel blends and the distance from refineries.

Even $5-a-gallon average — last seen briefly in June 2022 — could be on the table for later this year.

That could hit the drivers and makers of American-brand vehicles especially hard.

U.S. automakers have among the least fuel-efficient lineups on the road, making them more vulnerable to gas-price shocks, according to a Washington Post analysis of government fuel economy data.

The new 2026 lineups from Ram, Dodge, GMC and Chevrolet have combined MPGs that are worse than any auto brand except a handful of luxury titles, such as Ferrari and Rolls-Royce.

American companies, more so than their European or Asian counterparts, have largely turned away from producing small cars in favor of what U.S. customers have shown they want to buy: crossover SUVs and trucks. It’s one reason that the average price for a new automobile is now above $50,000. The $20,000 entry-level car is gone. Affordability is a growing concern, especially as the used-car market remains tight and prices high.

That fuel efficiency gap translates directly into pain at the pump. At today’s prices, an average Ram driver spends roughly $600 more a year on fuel than an average Honda driver, and the difference only grows as prices rise.

The Post analysis shows how much the price spike is already costing drivers, depending on which brand they drive. Owners of the least efficient U.S. brands — Ram, GMC and Dodge — face annual fuel bills roughly $800 to $835 higher than before the war began.

Drivers everywhere have started to bemoan the higher costs. Among those sharing gas pump shock photos are the members of a Facebook group for owners of Ram 1500s equipped with what’s called a “mild hybrid” to boost fuel efficiency. One poster, from Texas, said he avoided topping off his tank “because I saw my mortgage amount due.” Others talked about avoiding driving as much as possible — or using their spouse’s car.

The move to bigger, thirstier vehicles got a boost from President Donald Trump, who gutted the federal push for greater fuel efficiency when he took over the White House. His administration reined in what it derided as the “EV mandate.” The EV tax credit program expired in September. Federal fuel efficiency standards were reset with softer targets. The Environmental Protection Agency recently announced plans to reduce regulatory pressure for better fleet efficiency.

But automakers also need gas prices to stay low.

“It is befuddling” that gas prices are now galloping upward under Trump, said De Haan, because the president has often touted low gas prices.

Just four days before bombing Iran, Trump in his State of the Union address last month said gas prices had been “a disaster” under President Joe Biden. He said gas was now under $2.30 a gallon in most states. “And when I visited the great state of Iowa just a few weeks ago,” he continued, “I even saw $1.85 a gallon for gasoline, the lowest in four years, and falling fast.”

“It’s a very different story now,” De Haan said.

“He went from, ‘Oh hey, Iowa, $1.85,’ to ‘Oh hey, Iowa, we’re closing in on $4,’” De Haan said.

High gas prices can influence what kind of vehicles people want to buy.

“If gas prices continue to increase with no end in sight — if this is the new normal — I think consumer behaviors will have to change,” said Abey Abraham of consulting firm Ducker Carlisle, where he leads its automotive and materials practice.

In the mid-2000s, gas prices roughly doubled from about $2 to $4 a gallon as demand surged. Sales of the biggest pickups fell. The Ford F-series — including the F-150, the perennial best-selling automobile in the U.S. — saw sales drop by about 45 percent as consumers shifted toward smaller vehicles and hybrids like the Toyota Prius.

Gas prices spiked again, nearing $4 a gallon, coming out of the Great Recession, but pickup sales softened only briefly.

Truck sales also dipped when national gas prices clipped $5 a gallon in 2022, after Russia’s invasion of Ukraine. But sales took off again as prices eased.

Today, the impact could be more muted, at least in the short term.

Consumers have become so enamored with SUVs and trucks — and there are fewer fuel-sipping sedans to choose from — that they may still buy a truck, experts say. But they may choose a model with better gas mileage, such as going with two-wheel-drive or a hybrid.

“If that auto brand doesn’t have the power train they’re looking for, they might look around at others,” said Erin Keating, executive analyst at Cox Automotive. “Do they ultimately need to have a Hemi engine, or can they go with something more fuel efficient?”

Hybrids are the most likely beneficiary of any shift away from pure gasoline engines, analysts said. Electric vehicles will continue to see growth, but sales will be constrained by EV’s higher sticker price and the loss of subsidies.

The bigger worry for automakers is that high gas prices tend to deflate consumer confidence, Keating said.

Potential car-buyers just might decide not to buy any vehicle while gas prices and uncertainty are high.

“They get more nervous about big-ticket purchases,” she said. “And if you’re the automaker, this is just one more thing that consumers are feeling down about.”

Source URL: https://www.washingtonpost.com/business/2026/03/21/gas-prices-miles-per-gallon-iran-war/

5 AI Solutions Delivering Real Value in Construction & Renovation

The hammers are swinging, the blueprints are unfurling and the aroma of construction fills the air. But in the backdrop of every modern construction site and ambitious renovation project, a quieter, yet profoundly transformative revolution is underway.

Artificial intelligence (AI) is no longer a futuristic pipe dream; it’s a tangible force poised to redefine efficiency, reduce costs and unlock unprecedented opportunities within the vast and complex world of construction & renovation (C&R).

But for many in the industry, the journey into AI feels less like a clear path and more like navigating a dense fog. The promise is immense, but the practical application, identifying the best use cases that truly create value, can be a daunting, time-consuming challenge.

Here are five pivotal use cases—all available as pre-built solutions for rapid and cost-effective deployment – that are already demonstrating significant returns for early adopters.

1. The Quoting AI Agent: From Dozens of Hours to a 5-Minute Click

Picture this: a stack of blueprints, meticulous specifications and the ticking clock for a bid submission. Traditionally, generating an accurate quote is a labor-intensive marathon, often spanning dozens of hours for skilled professionals poring over architectural drawings and performing complex calculations. This is where the Quoting AI Agent comes in.

AI-powered solutions can now automate the entire process of reading blueprints, identifying materials, quantifying labor, and performing precise cost estimations. By ingesting vast amounts of project data and learning from past successes, these AI agents can rapidly analyze new plans. What once took a team days can now be achieved in minutes with a simple click of a button.

This isn’t just about speed; it’s about unparalleled accuracy, consistency and freeing up your most valuable human talent to focus on client relationships and strategic growth, rather than tedious number crunching. The competitive edge gained by slashing quoting times is immeasurable.

2. Intelligent Data Management: Your Project History, Instantly Accessible

In C&R, every project is a treasure trove of invaluable data: past proposals, subcontractor agreements, material specifications, change orders, progress reports and countless emails. This information is typically scattered across disparate IT systems, buried in individual collaborators’ laptops or filed away in physical archives, making it a monumental task to locate specific details from previous jobs.

The result? Wasted days, missed opportunities and the frustrating reinvention of the wheel.

AI-driven data management solutions tackle this head-on by creating a centralized intelligent hub capable of ingesting, organizing, and making accessible all your historical project data, regardless of its original format or location. With advanced AI search capabilities, you no longer spend days hunting for that crucial proposal from five years ago.

Instead, you simply query the system something like “Find proposals for commercial kitchen renovations over $500,000 in the last three years” and retrieve the exact document in less than 30 seconds. This transforms your accumulated knowledge into an active, strategic asset, empowering faster decision-making and more informed future bids.

3. Harnessing AI for Next-Generation Lead Generation

The days of simply optimizing your website for Google search to capture leads are rapidly evolving. A new frontier in client acquisition is emerging, driven by the rise of conversational AI platforms like ChatGPT. Consumers and businesses alike are increasingly turning to these AI agents to ask for recommendations: “Who are the best general contractors for a luxury home renovation in Aspen?” or “Which construction firms specialize in sustainable commercial buildings?”

This shift represents a unique and fleeting window of opportunity. While everyone will eventually adapt, those who move now to optimize their presence and demonstrate their expertise within these new AI ecosystems will gain a significant competitive advantage.

AI can help you analyze natural language queries, understand client intent, and craft compelling narratives that position your firm as the go-to expert. By proactively integrating AI into your marketing strategy, you can get ahead of your competitors and capture new leads from an entirely different and rapidly growing source.

4. Automating Accounts Payable and Receivable: The Machine Does It Better

The financial backbone of any C&R business relies on the efficient handling of invoices, payments and cash flow. Yet Accounts Payable (AP) and Accounts Receivable (AR) often remain manual, tedious and error-prone processes. Creating invoices, matching purchase orders and chasing outstanding payments are tasks perfectly suited for automation.

AI-powered solutions for AP/AR are exceptionally good at these precise, repetitive tasks. They can automatically extract data from invoices, verify details, flag discrepancies, schedule payments and even send automated reminders for overdue accounts. The savings generated by reducing human error, accelerating payment cycles and optimizing cash flow are substantial.

By offloading these “busywork” financial tasks to AI, your human finance team can shift their focus from transactional processing to strategic financial planning, analysis and, most importantly, supporting your sales efforts.

5. AI for Administrative Automation: Freeing Your Team from the Mundane

The administrative burden in C&R firms is immense, often consuming a significant portion of valuable employee time. While Robotic Process Automation (RPA) offered some relief, its rule-based limitations often proved too rigid for the dynamic, often unstructured nature of construction operations.

But with the advent of Large Language Models (LLMs) and advanced AI, the possibilities for administrative automation have expanded dramatically.

Imagine AI bots handling the creation of CRM inputs after a client meeting, automatically drafting security updates for ongoing projects, checking inventory levels across multiple sites or sending personalized reminders to clients and suppliers about upcoming deadlines or deliveries. These “boring stuff” tasks, which collectively drain countless hours from your human teams, now can be seamlessly managed by AI.

This isn’t just about efficiency; it’s about enhancing job satisfaction by allowing your employees to focus on complex problem-solving, creative solutions, and direct client engagement—the aspects of their roles that truly add value and intellectual stimulation.

The future of construction & renovation isn’t just about bigger buildings or more innovative designs; it’s about smarter operations. While the initial steps into AI may seem daunting, our advice is clear: don’t get lost in the overwhelming potential.

Start with these five proven use cases. They represent the most direct and impactful paths to leveraging AI to create substantial value, drive efficiency, and give your business a significant competitive edge in an increasingly digital world. The time to build smarter is now.


Fabien Cros is the Chief Data & AI Officer of Ducker Carlisle and the head and founder of its Data & AI practice (previously called SparkWise Solutions). He previously served as Data & AI Country Lead for Manufacturing at Google France. Ducker Carlisle’s Data & AI team offers a range of services to help companies leverage AI to create value, from AI strategy and assessment to the development of bespoke AI‑based technology solutions.

For more information, email dataAI@duckercarlisle.com

Source URL: https://ccr-mag.com/5-ai-solutions-delivering-real-value-in-construction-renovation-2/

IT leaders share enterprise AI change management tips

By Beth Pariseau, Senior News Writer | Informa TechTarget on March 16, 2026

Despite years of hype and technical development, most enterprise AI initiatives are stuck at the starting gate. But some large companies have made headway in deploying AI productively, starting with a foundation of organizational change.

That generative AI (GenAI) and AI agents have yet to fulfill vendors’ heady promises or be deployed widely at scale by enterprises has been the growing consensus among Big Tech leaders and market research reports so far this year.

In January, PwC’s 29th Global CEO Survey report, based on responses from 4,454 chief executives, found that 56% of respondents have not realized revenue or cost benefits from AI. Just 12% reported realizing benefits from AI in both categories.

During the Cisco AI Summit earlier this month, executives from Cisco, AWS, Google and OpenAI said that AI is moving faster than enterprise customers can absorb.

At Insight Enterprises, a global systems integrator based in Chandler, Ariz., the disconnect between the breakneck pace of AI development in the industry and typical enterprise change management processes has been most evident so far in software development. This was true both internally during Insight’s initial phases of adopting GenAI and among clients, according to the company’s CTO for North America, Juan Orlandini.

“That’s actually a work in progress as an industry, and I’d be remiss telling you that we have it all figured out — anybody that tells you that they’ve got it figured out, they’re [wrong],” Orlandini said in an interview with Informa TechTarget. “Because we can generate code so quickly, we’ve forgotten the very front end of the application development cycle. … Part of the guidance that we give to customers is that, yeah, there are some parts of the workflow that have gotten significantly faster, but some of those structures that we’ve developed over 50, 60 years of running it properly — don’t forget those.”

Developers keen on shipping code faster with AI sometimes chafe at the enterprise change management processes that remain in place, Orlandini said. But these processes are crucial to weeding through a mass of prototypes to identify the projects that will have a significant impact on a business when deployed at scale.

Take, for example, Insight’s development of an AI agent for its website. The minimum viable product for that was developed using AI in three weeks, but it took three months for the rest of the change management process to vet the agent, Orlandini said.

“The scaling and the continual verification and all that … security, cost controls and governance … developers typically tend to struggle against, but there’s a reason why you have a security team and a governance team and a FinOps team,” he said. “They’re not there to prevent innovation. They’re there to make sure that you’re doing it fiscally responsibly.”

People and process as an AI foundation

Friction remains between coding agents and the rest of the software development process, but an even bigger problem for enterprise AI lies in broader organizational change management issues. In a Feb. 4 Process Optimization Report by data processing firm Celonis, among 1,649 surveyed businesses, the top three hurdles to AI in production were people and process problems: a lack of expertise, cited by 47% of respondents; misalignment between departments by 45%; and difficulties getting AI to understand business context by 45%. Difficulties driving automation across disjointed systems were also cited as a blocker by 34% of respondents.

A comprehensive change management process for people in the organization that started early was even more important for AI adoption at Insight Enterprises than software delivery checks and guardrails. When ChatGPT launched in 2022, evaluating how people in the organization responded to it was a key part of how Insight assessed the potential benefits of the technology, Orlandini said. Before the company developed any AI apps, a “walled garden” for internal experimentation by 14,000 employees served as the setting for an organizational approach to AI change management.

During this early experimentation, Insight observed three categories of responses: highly — sometimes overly — enthusiastic early adopters; reluctant opponents of the technology; and, for the majority, “‘Hey, this sounds really cool, but I don’t know how to use it,'” Orlandini recalled. “‘Where’s the manual, where’s the training?'”

In response, the company created a training program and platform called Flight Academy, where users start from very basic knowledge, such as “What is a prompt?'” and progress into deeper prompts, then connect the results of those prompts to their work. As users progressed, they competed individually and as teams. Flight Academy was initially an internal tool at Insight, but the company now sells it to clients.

“That lowered the barrier of entry for that broad middle group,” Orlandini said. “The ones that were [saying] ‘Burn it with fire’ are finding out, ‘This is a tool for me. I’d better use it, or I’m not going to be as useful to our company.’ And the overly enthusiastic ones became some of the leaders.”

Evaluating ‘a zillion good ideas’

Flight Academy helped prepare Insight Enterprises for AI, but there was more to AI change management than employee training. Next, the company had to whittle down “a zillion good ideas” to focus on the ones that would deliver an ROI for the business. To do that, Insight created a platform called Insight Prism, which it also now sells to clients.

“We created an onboarding process for these ideas to be brought forth. Then [Prism] runs those ideas through an engine that spits out a business case and says, ‘This idea is going to be amazing because it’s going to generate this much more revenue, or it’s going to save us this much more money,’ or both,” Orlandini said. It gives you a business justification for whether this thing is good or not. And for some of those ideas, the numbers are actually not so good, so we don’t invest in those.”

There are other tools companies can use to evaluate ideas for AI apps, ranging from hosted cloud services to open source AI risk assessment tools. Ducker Carlisle, a global consulting and M&A firm, uses StackAI to host a similar platform for its citizen developers to build and evaluate apps created using AI agents. This decentralized approach to AI application development and evaluation emerged because the initial phase of adoption generated an overwhelming number of niche requests for the company’s centralized engineering team, raising concerns that employees would resort to shadow AI, according to Fabien Cros, chief data and AI officer at Ducker Carlisle.

In response, Cros drew on previous experience as data & AI country lead for manufacturing at Google Cloud in France to create a citizen developer and tool discovery program.

“You let the users come up with ideas, build some stuff, even if it’s limited, and then when you see adoption, you say, ‘Is it core DNA for organization, or is it not?'” Cros said. “When it’s not, you let it run through the [SaaS] platform and the [citizen developer] program. When it’s core DNA you want deep monitoring. You want to control everything, end to end. It’s like a pyramid, where you have a lot of use cases at the bottom, and then you bubble up the core use cases, and your central team [takes] over.”

Ducker Carlisle also uses gamification techniques to assess the popularity of users’ apps, which is reflected in a leaderboard and a rating system akin to GitHub stars.

“When we see something that is rising fast, we look at it and we say, ‘That’s a good use case, and apparently people like it,'” Cros said. “And we ask, ‘Can we do it better? Can we do it faster? Should we move it in-house?'” 

‘The hackathon mentality’

Telus, a Canadian telecommunications and technology company, used its internal developer platform (IDP) to host a hackathon where users tested AI tools, including AI infrastructure utilities, to determine which of the many choices in a teeming market would be most useful.

“We really adopted the hackathon mentality, especially last year,” said Kulvir Gahunia, site reliability office director at Telus. “It’s [done in] a controlled environment, but at the same time, we didn’t put guardrails on what users wanted to hack on. The tool they build might or might not help, but sometimes the technology to get to that point is a game-changer.”

One example of that is n8n, an AI workflow automation platform created by a company of the same name in Germany that has a source-available, self-hostable free version, which lent itself to use during the Telus hackathon.

“In [about] 100 ideas that were submitted, something like 14 or 15 of them used n8n, so there were tons of little n8n instances running around,” said Dana Harrison, principal site reliability engineer at Telus. “And we went, ‘Oh, before this goes completely off the rails, this is clearly a need. So we met that need, got licensed, and we now have an agreement with n8n.”

Given how fast AI tools are emerging and changing, following those indications of user need are a good way for an enterprise platform team to keep up with what’s important to secure and support, Harrison said. Three days into setting up n8n as part of the internal platform, it had 1,300 users.

The fact that the company had already taken steps to centralize on an IDP based on CNCF’s Backstage gave it a strong foundation for AI adoption. It had also taken steps to  consolidate its IT and business data using Dynatrace, and control and moderate corporate access to large language models with the Fuel iX platform. “We are trusted in what we do, which is a privileged place to be in,” Harrison said of the Telus platform team. “What it also means is that when we develop, people listen.”

One of the early internal AI adoption wins for Telus was a Slackbot, combined with an open source search engine tool called turbopuffer, that gave users an easy way to search Dynatrace data.

It’s a natural position for enterprise platform engineers to be in, according to Gahunia — AI was created to remove toil and offload repeated tasks, which is also the mission of platform teams, he said.

“We really embrace that mantra,” he said. “A lot of the stuff that we started coming out with was, ‘Hey, we do this all the time. Let’s just automate this piece. Can we now leverage this somewhere else? Oh, yes, we can.’ And it just organically started growing into this path that we’re on now.”

Final thoughts: lessons learned for IT leaders

Finding opportunities to automate and remove toil from existing workflows using AI helps center the most useful tools but can also be a good starting point for people fearful about being replaced by the technology, Gahunia said.

“It helps remove that fear of, ‘AI is taking over my job,’ because [users can see how to] use AI to enhance [their] work,” he said. “It’s not going to replace you, but you can leverage it to enhance your work and the outcomes you deliver. … That’s a very key message for any organization to drive adoption.”

For some workers, however, disruption is already undeniably taking place, Insight’s Orlandini said, especially among software developers. AI is now performing the simple, entry-level tasks that used to help junior developers learn to build larger, more complex systems. Senior developers sometimes find themselves in a role more like a product manager, without the craft of developing code they’ve spent years honing and have come to enjoy.

“We need to be very mindful as leaders of understanding that this isn’t just a technology thing,” Orlandini said. “We have to manage the people and manage the expectations as much as we have to be able to consume this new capability.”

For junior developers, managers should encourage them to start thinking about “the whys, rather than the hows, of building applications,” he said. For senior developers, if a product manager role doesn’t suit them, leaders should find other ways to put their expertise to use for the organization.

“People are wary of change because change implies the unknown — help them through that unknown,” Orlandini said. “Any amount of education that you put into your organization is going to pay dividends down the road.

“The other thing I tell IT leaders is, don’t forget your roots. All the things that we’ve learned over the decades that we’ve been doing in IT still apply. Some of the things might happen faster, but not all of them, and some of the fundamentals are still there.”

Source URL: https://www.techtarget.com/searchitoperations/news/366640354/IT-leaders-share-enterprise-AI-change-management-tips

SaaSpocalypse? Maybe not, but SaaS applications are changing

By Beth Pariseau, Senior News Writer | Ben Lutkevich on March 5, 2026

Rumors of a ‘SaaSpocalypse’ might be greatly exaggerated, according to IT leaders, but the way enterprises interact with SaaS applications is changing for good with the development of homegrown apps generated by AI agents.

With the rise of AI coding agents that can create applications from natural-language inputs alone, major SaaS vendors’ stocks have taken significant damage over the last three months. The Wall Street Journal estimated that investor fears about AI threats to these businesses wiped out $1.6 trillion in stock value in 2026 alone. The agent-based platform OpenClaw made headlines for disrupting traditional SaaS applications and raising software supply chain security concerns, while Citrini Research published nightmarish speculative fiction about an AI-fueled white-collar job-market collapse. In the past week, Twitter founder Jack Dorsey’s fintech startup Block Inc. eliminated 4,000 jobs, citing AI automation.

The democratization of application development brought about by AI agents is putting undeniable pressure on traditional SaaS vendors, eating away at some of their value proposition to users, and ultimately, long-term revenue growth, said Angie Jones, vice president of engineering, AI tools and enablement at Block, in an interview last month with Informa TechTarget.

Jones cited the examples of Spotify and Stripe, which use Block’s open-source framework Goose to coordinate agents and code custom apps, as an indication of the real risks faced by SaaS companies, even those that offer AI agents to customers.

“Instead of going and buying an agent from companies, they say, ‘Hmm, I can take an open source agent, I can then build on top of it for my needs. I can customize it in ways that we need. I can control the security, I can control the connections to other data, and I’m not relying on a SaaS company to provide that,” Jones said. “Those sorts of layers face a bit of risk.”

Reading the tea leaves for SaaS

For power users, AI agents can create apps or features that eat away at the edges of traditional SaaS vendors’ businesses. For example, one DevOps engineer at a Fortune 100 company singlehandedly developed an internal FinOps tool to monitor AWS accounts, resulting in significant savings without vendor intervention.

Suresh Gangula, who requested that his company not be named as he is prohibited by policy from speaking about it in the press, developed the tool during a hackathon at his company, using TypeScript, Amazon Bedrock and Claude 4.5. The tool monitors usage metrics such as CPU and memory utilization, feeds them to an AI agent that analyzes them according to rules defined by Gangula’s team, and produces a score based on those metrics and the expected service level, enabling the team to quickly delete, shut down, or resize services as necessary.

In the past, the company might have looked to a FinOps SaaS provider to achieve a similar end, but none of these tools provided the single-click functionality based on internal rules that Gangula’s in-house tool provides.

“If a particular resource is an orphan, if it’s not in use, with a single click we can just delete that resource,” he said. “We don’t even need to go into a particular account, log in and then search for that resource.”

Although the tool is in limited preview, Gangula estimated that the cost savings his tool could help the company realize would be huge, citing its success with one of the company’s smaller AWS accounts among hundreds across the company.

“That particular service bill is $30,000 a month, and we identified $10,000 in savings, so I think it could lower the cost by 30% at minimum,” Gangula said. Cloud resources for workloads such as log analysis, alert correlation, and vulnerability prioritization are other functions ripe for optimization, he said.

Still, while Gangula’s example might send a chill down the spines of companies whose bread and butter is exactly that type of FinOps feature, his company is far from unplugging any existing tools just yet. The cost management tool he vibe-coded is still limited to the AWS accounts Gangula works on, and it remains to be seen whether the 30% savings rate would remain consistent with scale.

“It’s very easy to build something that is shiny, but those things don’t run properly. They are vibe-coded. They are not on a proper IT infrastructure. They are not secure,” said Fabien Cros, chief data and AI officer at Ducker Carlisle.

Block’s Jones acknowledged that while the company’s finance team was reviewing tools it could eliminate with substitutes created using Goose, it hadn’t yet unplugged any. It was also unclear as of press time whether Jones was affected by the Block layoffs, which occurred after the interview with Informa TechTarget.

Ultimately, big SaaS vendors’ margins might be under pressure, but Microsoft, Salesforce and Oracle aren’t going anywhere, said Fabien Cros, chief data and AI officer at Ducker Carlisle, a global consulting and M&A firm that has begun to embrace AI agent coding tools for citizen developers in its organization.

“It’s purely a financial analyst’s reaction,” Cros said of the Wall Street SaaS stock sell-off. “It’s very easy to build something that is shiny, but those things don’t run properly. They are vibe-coded. They are not on a proper IT infrastructure. They are not secure.”

Block’s Jones countered that concerns about long-term maintenance could also shift with a vastly lowered barrier to writing – and rewriting – applications.

“My take is that, if I can spin up a new SaaS tool for myself in a day, then why do I even need to necessarily maintain it? If it starts acting wonky the next day, I’ll just build it all over again,” she said. “The cost of these things is much cheaper now.”

Meanwhile, the productivity gains for AI-driven application development have been pronounced, Jones said. For example, business users can now query data in natural language without writing SQL or waiting for a response from the analytics teams.

“We see our salespeople are able to do things like segment 100,000 leads in an hour, whereas it would’ve taken a whole week to get through that list,” she said.

SaaS application sea change underway

While no one can say for certain what the long-term future will hold, the role of SaaS applications at companies such as Ducker Carlisle has already profoundly changed with the introduction of AI, beginning with a citizen developer program in which business users develop their own software tools.

The program gives shy business users the support they need, bold business users the guardrails they need, and insulates the most technical cohort from small use cases that non-technical users can now handle themselves, freeing them up for bigger projects, Cros said.

SaaS is still present at Ducker Carlisle, but in new ways: the firm uses a drag-and-drop platform called StackAI to help business users create projects. StackAI also hosts evaluation and adoption monitoring, so that projects that naturally gain traction get adopted in production. When user-generated projects that address core functions at the company gain traction, they get referred to the expert cohort to build and own.

“It’s like a pyramid, where you have lots of use cases at the bottom, and then you bubble up the core use cases so your central team can take over,” Cros said.

The StackAI platform is good for simple apps, but core functions still require deep monitoring and full end-to-end control. Still, Cros estimated that 80% of Ducker Carlisle’s use cases now run through StackAI, while the central tech team manages the remaining 20% of high-value apps.

Ducker Carlisle has been able to eliminate several translation, market data, and AI meeting recorder apps the team was using, opting instead to stick with Microsoft Foundry to call Anthropic and OpenAI models using homegrown agents. Overall, the company achieved a 3% cost reduction, resulting in an annual savings of $1 million.

Another SaaS shift driven by AI at Ducker Carlisle is that the company has begun selling some of the tools created by its citizen developers to clients facing similar problems.

“We are becoming kind of a SaaS provider. Not by design, but the client says, ‘I want to do XYZ.’ And we’re like, ‘Yeah, we already did that for us because we had the same issue,'” Cros said. “‘Okay, can we just buy it from you because it’s cheaper than just hiring a bunch of people from Google?'”

The challenge Ducker Carlisle faces now is sustaining use cases beyond the excitement of the initial build stage in the citizen developer program, past the plateau of fatigue.

“We have an endless roadmap of use cases that we will extract from the program and build on our own,” he said.

What enterprises should do right now

One IT industry leader said he sees the creativity and personalization that are possible with agentic systems as a return to an earlier, pre-SaaS mode for applications, when engineers built custom systems for individual customers. At a certain point, it became too expensive to build custom systems, so people began to move to off-the-shelf products, said Bill Vass, CTO at consulting company Booz Allen Hamilton.

Now, agentic systems could read your business processes and build systems exactly the way you want, allowing business users to innovate again.

Instead of logging into and paying for 10 different SaaS services to complete a process such as employee onboarding, a business could train an agent on its specific process.

However, organizations must experiment with AI while ensuring security is built in by design.

As enterprises deploy AI agents, they must ensure proper authentication, auditing, reasoning rules, memory management, and cryptography.

With the right guardrails in place, enterprises can balance SaaS vendors and internal development by being model agile—able to switch between AI models and platforms to optimize cost and performance.

Source URL: https://www.techtarget.com/searchitoperations/news/366639662/SaaSpocalypse-Maybe-not-but-SaaS-applications-are-changing

NETA World – Global Energy Transition: What Will it Mean for Electrical Testing?

By Kevin G. Sarb, Ducker Carlisle

The global energy system is undergoing a fundamental shift that will reshape how electricity is generated, distributed, and managed over the coming decades. Electrification is accelerating across transportation, manufacturing, and digital infrastructure, while clean and renewable sources are rapidly expanding their share of global power generation.

For the electrical testing community, this transition is not theoretical. It is already changing the complexity, configuration, and risk profile of the systems that must be tested, commissioned, and maintained.

Ducker Carlisle’s most recent Global Energy Transition Outlook examines how the world’s energy mix is evolving under three scenarios: Net Zero Emissions, Announced Pledges, and Stated Policies. The report identifies five core findings that define the direction of travel. Together, these trends point to a future where electrical testing expertise will be more critical than ever.

ELECTRICITY’S EXPANDING ROLE IN THE GLOBAL ENERGY MIX

Today, electricity accounts for roughly 20% of global energy consumption, with the majority still coming from liquid, gaseous, and solid fuels. That balance is changing rapidly.

By 2050, electricity is expected to represent at least one-third of global energy use and potentially as much as 55%, depending on policy and technology outcomes.

This shift reflects broad electrification across the economy. Electric vehicles, electrified industrial processes, data centers, and distributed energy systems are all contributing to rising demand.

From a testing perspective, this growth places increasing pressure on generation assets, substations, protection systems, and distribution infrastructure that were not designed for today’s load profiles or operating dynamics.

CLEAN AND RENEWABLE GENERATION BECOMES THE MAJORITY

As electricity demand grows, the sources used to generate that power are also changing.

Clean and renewable sources are projected to supply between 67% and 88% of global electricity by 2035, up from 38% in 2023.

While the shift to clean power is essential, it introduces new challenges for grid stability. Variable generation from wind and solar requires more sophisticated protection, control, and coordination across the grid.

For testing professionals, this means validating systems that must operate reliably under a wider range of load conditions, fault scenarios, and dynamic operating states than in traditional centralized generation models.

SOLAR AND WIND DRIVE RENEWABLE GROWTH

Solar photovoltaic (PV) will be the fastest-growing clean energy source, expanding from 14% of global clean electricity generation today to:

  • More than 30% by 2030
  • Nearly 50% by 2050

Wind power will also grow, reaching approximately 25% of clean generation and holding relatively steady beyond that point.

The rapid deployment of solar and wind is supported by declining costs and strong policy momentum, particularly in China and the European Union.

However, these technologies are inherently intermittent. Their integration into both transmission-scale and behind-the-meter systems increases the importance of proper testing of:

  • Inverters
  • Protection schemes
  • Grounding systems
  • Power quality controls

ASIA PACIFIC LEADS CLEAN ENERGY DEVELOPMENT

Geographically, the energy transition is not evenly distributed.

By 2030:

  • The Asia Pacific region will account for nearly half of all global clean electricity generation.
  • China alone will represent more than two-thirds of that total.

The region is also projected to have nearly double the nuclear electricity-generating capacity of Europe or North America.

This concentration of investment reflects aggressive industrial growth, government support, and long-term energy planning.

It also underscores the global nature of equipment standards, testing methodologies, and best practices. As systems are deployed at scale in Asia and exported globally, alignment on testing rigor and performance validation becomes increasingly important.

TECHNOLOGY AND POLICY AS ENABLERS

Achieving a cleaner global energy mix will require more than generation capacity. Critical enablers include:

Battery / Storage Capacity

  • Current battery storage technology is not sufficient.
  • New technologies under consideration include hydrogen storage and flow batteries.
  • Government regulation and incentives are needed in the short term to scale new technologies and reduce costs.

Grid Capacity & Resiliency

  • Aging grid infrastructure, especially transmission, cannot handle higher and more fluctuating loads.
  • Significant investment is required.
  • There are bottlenecks in new grid connection projects for renewables.

Energy Management

  • Advanced monitoring and controls for load balancing.
  • Hybrid integrated systems (wind/solar plus storage).
  • Artificial Intelligence may play a significant role.

From a testing standpoint, this creates demand for new skill sets, expanded commissioning scopes, and deeper system-level understanding rather than asset-by-asset testing alone.

WHAT THIS MEANS FOR ELECTRICAL TESTING

The convergence of renewable generation, energy storage, and advanced control systems is fundamentally changing how power systems are designed and operated.

These systems must function as integrated ecosystems rather than isolated components, and each configuration can be highly customized.

Behind-the-meter and microgrid installations are a clear example. Large electricity users—including hyperscale data centers—are increasingly deploying on-site generation and storage to meet near-term power needs.

These systems often combine:

  • Natural gas generation
  • Solar PV
  • Battery energy storage
  • Sophisticated energy management software

Testing and commissioning these hybrid systems requires expertise across multiple technologies and operating modes.

Electrical testing professionals must validate:

  • Individual assets
  • System interactions
  • Protection coordination
  • Control logic
  • Response under abnormal conditions

CONCLUSION

As the energy transition accelerates, demand for electrical testing will continue to grow, both in volume and complexity.

Firms that invest in:

  • Technical capability
  • Training
  • System-level expertise

will be well-positioned to support the next phase of grid evolution.

Author

Kevin G. Sarb is Managing Director at Ducker Carlisle, leading the Industrials Practice with a focus on energy, climate technologies, and commercial excellence. With 20 years of management consulting experience, he helps industrial clients design growth strategies and build commercial capabilities that deliver measurable results.

He holds degrees from:

  • University of Notre Dame
  • University of Michigan
  • University of Chicago Booth School of Business

Source url: https://www.duckercarlisle.com/wp-content/uploads/2026/03/NETA-World-Spring-2026-Issue.pdf

Repairer Driven News – Ducker Carlisle discusses mega-casting’s reshaping of the vehicle value chain

By Lurah Lowery on February 13, 2026
Collision Repair | Market Trends

A recent whitepaper from Ducker Carlisle explores how mega-casting in vehicle manufacturing reshapes the value chain, both in vehicle design for lightweighting and in the factory for assembly.

Leonard Ling, Ducker Carlisle’s automotive expert, writes that the winners won’t be defined by who casts the biggest piece, but by who can master the interfaces while designing for manufacturability, serviceability, and cost from day one. Those interfaces include large cast nodes, extrusions, and sheet structures.

In a conversation with Repairer Driven News, Ling and Bertrand Rakoto shared some details behind mega-casting production.

Rakoto used Ford’s capital investment in mega-casting as an example of converting existing assembly lines to adapt to the new production.

“Ford just invested in the Louisville factory to go into mega-casting; it’s a $2 billion investment into the factory to replace existing systems and put up the new systems,” he said. “The reason also is that you need big areas for the mega-castings because the machines that are coming up for creating those parts require not only space for themselves, but also you need to have big cranes within the installations to be able to move the machines and dyes.

“When you use dyes for molding and doing the mega-casting, you need to be able to move those dyes around, change them when necessary, and so on. For that, you need a very high roof or isolating factories, and you need a lot of space to install those cranes and move the machines around.”

Mega-casting saves OEMs time and money, according to Rakoto and Ling.

Simplification of the vehicle architecture, or “unboxed type of vehicles,” has three main modules that are assembled separately and joined together, Rakoto said. That way, OEMs can have different types of working stations throughout each of their factories.

Ling added that mega-casting is mostly used on the rear end of vehicles.

“On the front side, it’s another story because on the front side, you have more considerations for the crash management,” he said. “We see two pieces, so the left piece and the right piece are separate. So each one integrates on each side. You can integrate a shock tower in the longitudinal, but it can be two pieces for the left and the right.”

Ling said emerging OEMs or brands especially save money by utilizing mega-casting at their greenfield plants by starting from scratch in setting up the workspaces with equipment, rather than converting existing sites.

Rakoto noted that lightweighting is on the minds of automakers who are going for Gen-3 mega-castings.

“The reason is that Gen-1 mega-castings were not particularly lighter than the steel parts they were replacing,” he said. “Now we start to see technology improving in the last 10 years. We start to see improvements in terms of weight.”

When asked how mega-castings play into collision repair strategies and what repairers can expect, Rakoto said Tesla is the only OEM at this time that makes specific replacement parts for mega-casting pieces. However, he said if a mega-casting is broken, it’s likely the vehicle will be totaled.

“So far, the financial effort to replace the mega-casting can be done, maybe, within the first two years of the lifetime of the vehicle,” he said. “After that, the value of the vehicle makes it something that does not make sense. Even if we are making the replacement or the repair of mega-casting more efficient than it was at the beginning, it’s still quite a burden. There are some repair procedures now that are being put together by the carmakers, but the goal is not really to look into replacing a mega-casting. It’s more about changing some areas where it’s broken, and we still have to learn a lot about the process and get some return on experience.”

Rakoto noted that smaller castings, such as multi-piece components, can be replaced, and it’s been known for at least 20 years how to fix or replace those parts within vehicles.

Ling added that automakers implement two-way mega-casting strategies.

“One way is that they’re improving the repairability of the mega-casting itself, so they improve the alloy to make it more weldable,” he said. “If there’s damage on the perimeter of the mega-casting, it’s very likely to be repairable today with welding or maybe some glue technology. In addition to the repairability of the mega-casting itself, they put more protection outside of the mega-casting component.

“Imagine the rear end of the vehicle. Before the crash hits the mega-casting, you have the bumper on the very outside of the vehicle. After the bumper, you have the crash can, and after the crash can, you have the longitudinal. Those parts are either bolted on or welded on, so you can easily replace them. After those zones are totally crashed, then the energy will come to the mega-casting, which means if the mega-casting is damaged, it’s a really bad crash.”

Ling writes in the whitepaper that joining has evolved from “make it stick” to “engineer the interface.”

“Today’s mixed-material strategies rely on adhesives to carry structural load, supported by self-piercing rivets or flow-drill screws where access allows,” he wrote. “Welding remains, but mostly for extrusion-to-extrusion joints away from critical cast surfaces. The new frontier is service. OEMs now design sacrificial extrusions that bolt to robust cast nodes, enabling sectioned repairs instead of full replacements. This reduces total-loss rates after moderate crashes and shortens turnaround time for insurers and repair shops.

“Mega-casting is less about consolidation and more about coordination. The era of ‘bigger parts’ is giving way to the era of smarter assemblies. Those who engineer for manufacturability, service, and sustainability together will define the next generation of automotive structures.”

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Ducker Carlisle Named One of America’s Top Management Consulting Firms 2026

Ducker Carlisle Named One of America’s Top Management Consulting Firms 2026

Independent Recognition by Business Insider Validates Strategic Excellence and Client Value

Troy, MI (February 17, 2026] — Ducker Carlisle, a global market research, strategy consulting and M&A advisory firm focused on automotive, industrial equipment, building and construction and private equity, has been named one of Business Insider’s America’s Top Management Consulting Firms 2026. The recognition is based on direct feedback from more than 25,000 professionals who work with consulting firms as hiring decision-makers and project collaborators.

The evaluation assessed firms across 15 industries and 14 practice areas on six key performance areas: strategic insight and innovation, measurable impact and outcomes, team expertise and collaboration, transparency and communication, value for investment, and adaptability and customization. The ranking incorporated third-party validation from credible industry sources and comprehensive media monitoring to ensure ethical and legal compliance.

This distinctive methodology highlights the value of Business Insider’s assessment. Rather than relying on brand awareness or self-submitted data, the ranking is based on direct client experience, feedback from professionals who served on hiring committees that selected consulting firms, and team members who worked on projects. This approach gives clients an objective measure of consulting excellence grounded in real outcomes and measurable business impact.

“This recognition is especially meaningful as it comes from the clients and professionals we work with every day,” said Joanne Ulnick, Ducker Carlisle Interim CEO. “Being recognized for strategic insight, measurable impact and value for investment by the decision-makers who select consulting partners and the teams who work alongside them reinforces our commitment to delivering tangible results. This shows our evidence-based, industry-specific approach sets us apart and reinforces our partnership with clients, as we work together to pursue growth and solve business challenges, with lasting impact.”

Ducker Carlisle’s services span research and analytics, industry-specific benchmarking, strategy consulting, pricing solutions, AI and data analytics, supply chain optimization, and both buy-side and sell-side M&A support for private equity firms and corporate clients.

Ducker Carlisle has developed these capabilities over 65 years and today serves leading U.S. and global clients in the automotive, heavy truck & equipment, industrial, building and construction and private equity firms.

For more information about the recognition and Ducker Carlisle’s approach to strategic consulting, visit here. The complete list of firms recognized on Business Insider’s America’s Top Management Consulting Firms 2026 report is available here.

About Ducker Carlisle

Named one of America’s Top Management Consulting Firms 2026 by Business Insider, Ducker Carlisle is a global market research, strategy consulting and M&A advisory firm that helps the world’s largest companies and private equity firms optimize business performance and accelerate growth. Founded in 1961 with offices across the US, Germany, France, UK, India and China, the firm leverages proprietary data, deep industry knowledge and proven methodologies to deliver tailored, industry-specific insights and recommendations across the automotive, heavy truck & equipment, general industrial, building and construction, and private equity sectors. For more information, visit DuckerCarlisle.com | LinkedIn

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Design News – Megacasting: From Proof of Concept to Production Discipline

By : Leonard Ling

At a Glance

  • – Next-gen casting aims to deliver better crash management, faster rework, and shorter lines.
  • – What matters most is not which material is used, but how well it is used.
  • – There’s a new emphasis on integrating multiple materials and engineering interfaces to achieve design goals.

Megacasting has moved past its experimental phase. The question now isn’t if it works, but how it reshapes the value chain. The winners won’t be defined by who casts the biggest piece, but by who can master the interfaces, between large cast nodes, extrusions, and sheet structures, while designing for manufacturability, serviceability, and cost from day one.

This isn’t a revolution that wipes out incumbents. It’s an architectural shift that redistributes work, favoring suppliers that align precision casting with smart assembly integration and repair logic.

From big & bold to smart & serviceable

Early large castings, the Gen-1 era, proved the concept but revealed the pain points. Thin walls that led to distortion, scrap, and inconsistent properties. Weight savings often fell short, repairability was minimal, and replacement was the default fix. Gen-1’s real value was learning what the factory, insurer, and service bay could not easily handle.

Gen-2 programs, especially among fast-moving Chinese OEMs, absorbed those lessons. Section thickness increased where stiffness mattered, alloys were tuned for as-cast stability, and replacement logic became intentional cut points, fastener access, and repair-friendly geometry. Scrap dropped, dimensional stability improved, tolerances were adjusted, and credible repair pathways began to change the conversation with insurers.

Now Gen-3 has arrived: modular megacasting. Instead of chasing a single monolithic rear floor or front structure, OEMs are combining large cast nodes—rear corners, longitudinals, shock towers, cradles—with extrusions and selective sheet reinforcements. The goal is balanced architecture: better crash management, faster rework, shorter lines, and more practical service. The frontier has shifted from making the monolith bigger to making the interfaces smarter.

OEM megacasting adoption summary (Sept 2025)

OEMRegion of productionPress sizeSOP year
TeslaNA, EU, CN6,000 & 9,000 ton2020
FordNA6,100 & 9,300 ton2027 (planned)
VolvoEU8,400 & 9,000 ton2026 (planned)
VolkswagenEU4,400 ton2027 (delayed to 2032)
Mercedes-BenzEUNot disclosed (~6,000 – 8,000 ton est.)Concept/demo
BMWEUNot disclosedEvaluation
AudiEUNot disclosedEvaluation
ToyotaJPLarge-scale (not disclosed)Concept/demo
HyundaiKRNot disclosedPlanned, TBD
HondaNA, CN6,100 & 12,000 tonPlanned, TBD
XPengCN6,800 & 12,000 & 16,000 ton2021
GeelyCN7,200 ton2021
NIOCN6,800 & 8,800 ton2022
AITOCN9,000 ton2023
Li AutoCNNot disclosed (~5,000+ ton)2024
Xiaomi AutoCN9,100 ton2024
ChangAnCN7,700 ton2024
CheryCN8,800 & 16,000 ton2025
BYDCN9,000 ton2026 (planned)
NezaCN20,000+ ton (R&D)Planned, TBD

Source: Ducker Carlisle, OEM and tier suppliers’ announcement

Joining has grown up 

Joining has evolved from “make it stick” to “engineer the interface.” Today’s mixed-material strategies rely on adhesives to carry structural load, supported by self-piercing rivets or flow-drill screws where access allows. Welding remains, but mostly for extrusion-to-extrusion joints away from critical cast surfaces.

The new frontier is service. OEMs now design sacrificial extrusions that bolt to robust cast nodes, enabling sectioned repairs instead of full replacements. This reduces total-loss rates after moderate crashes and shortens turnaround time for insurers and repair shops.

Alloy & foundry strategy: From parts to playbooks

Material strategy is converging on alloys that deliver more of their strength as-cast, minimizing post-treatment and distortion, while maintaining ductility through tight impurity control. Wall-thickness discipline now prevents hot spots and reduces rework. 

Sustainability pressures are accelerating the use of higher recycled content and closed-loop remelt. That brings tighter chemistry windows and demands collaboration between casting, alloy, and joining engineers. 

The next differentiator isn’t a new alloy; it’s a playbook. Foundries that co-locate near assembly plants, master die thermal management, and build geometry libraries with proven access points are emerging as partners, not just part makers. Those that add repair documentation, quick-change die strategies, and traceability will own the next phase of supplier value.

What it means for stampings, extrusions, & assembly

Megacasting will reduce some traditional underbody stamping content, but it won’t erase it. Stamping retains meaningful roles in closures, upper-body structures, inner reinforcements, and battery enclosures for variants that remain sheet-intensive. Also, incumbents create mechanical assemblies with legacy materials. The winners will be those who pivot from defending every panel to enabling the cast-centric or pre-assembled architectures. They will deliver stampings that are truly cast-compatible in flatness and tolerances, provide adhesive-ready surfaces, and crucially offer fixtures and cell integration that make mixed joining reliable at rate. 

For welding and assembly providers, weld-hours per car may decline, but system integration opportunities expand. Lines now coordinate adhesives, mechanical fastening, in-line NDT (non-destructive testing), and rework strategies. These capabilities will define who controls process stability and throughput in cast-centric plants.

Right material, right place 

In a cast-centric body, there is no single material winner. The right mix depends on crash targets, mass, cost, and service goals. Cast aluminum nodes provide the hard points and stiffness that form the backbone of the structure. Extrusions absorb and route crash loads while offering predictable, service-replaceable energy paths. Aluminum sheet delivers lightweight solutions for closures and selective floor or inner panels. Steel, meanwhile, remains indispensable where cost, dent resistance, and thermal stability dominate, especially in upper-body structures and variants that still benefit from the maturity and economics of steel stamping.

What matters most is not which material is used, but how well they integrate. Flatness, coatings, bond stack performance, and tolerance alignment determine whether a structure meets its performance targets. The most advanced suppliers are engineering those interfaces, not debating material supremacy. Integration, not substitution, is where value now accumulates.

Bottom line: Where value moves next

Megacasting doesn’t eliminate suppliers; it redefines where they win. Foundries that combine casting expertise with design, alloy development, and after-sales service capabilities will lead the field. Stampers that deliver cast-ready quality, adhesive-friendly surfaces, and precise tolerances will remain essential partners in multi-material architectures. Assembly integrators that master adhesives, rivets, inline metrology, and digital verification will own the operational stability of future lines. Extrusion and sheet suppliers that standardize crash-tuned, corrosion-resistant systems will grow faster than the broader market.

Across the industry, the value chain is converging on a single differentiator: interface mastery. The ability to make cast, sheet, and extrusion materials behave as one structure will determine who gains share as megacasting moves from showcase to standard practice. The next era of manufacturing will belong to those who engineer cohesion, not competition between materials, processes, and partners.

In short: Megacasting is less about consolidation and more about coordination. The era of “bigger parts” is giving way to the era of smarter assemblies. Those who engineer for manufacturability, service, and sustainability together will define the next generation of automotive structures.

About the Author

Leonard Ling

Leonard Ling

Leonard Ling is an automotive expert at Ducker Carlisle, a global consulting firm specializing in market research, strategy consulting, and M&A advisory services.

Source URL : https://www.designnews.com/auto-design/mega-casting-from-proof-of-concept-to-production-discipline

Top Cyber Threats to Your AI Systems and Infrastructure

From data poisoning to prompt injection, threats against enterprise AI applications and foundations are beginning to move from theory to reality.

Diverse Team of Professionals Meeting in Office at Night: Brainstorming IT Programmers Use Computer Together, Talk Strategy, Discuss Planning. Software Engineers Develop Inspirational App Program

Attacks against AI systems and infrastructure are beginning to take shape in real-world instances, and security experts expect the number of these attack types will rise in coming years. In a rush to realize the benefits of AI, most organizations have played it fast and loose on security hardening when rolling out AI tools and use cases. As a result, experts also warn that many organizations aren’t prepared to detect, deflect, or respond to such attacks.

“Most are aware of the possibility of such attacks, but I don’t think a lot of people are fully aware of how to properly mitigate the risk,” says John Licato, associate professor in the Bellini College of Artificial Intelligence, Cybersecurity and Computing at the University of South Florida, founder and director of the Advancing Machine and Human Reasoning Lab, and owner of startup company Actualization.AI.

Top threats to AI systems

Multiple attack types against AI systems are arising. Some attacks, such as data poisoning, occur during training. Others, such as adversarial inputs, happen during inference. Still others, such as model theft, occur during deployment.

Here is a rundown of the top threat types to AI infrastructure experts warn about today. Some are more rare or theoretical than others, though many have been observed in the wild or have been demonstrated by researchers through notable proofs of concept.

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Data poisoning

Data poisoning is a type of attack in which bad actorsmanipulate, tamper with, and pollute the data used to develop or train AI systems, including machine learning models. By corrupting the data or introducing faulty data, attackers can alter, bias, or otherwise render inaccurate a model’s performance.

Imagine an attack that tells a model that green means stop instead of go, says Robert T. Lee, CAIO and chief of research at SANS, a security training and certification firm. “It’s meant to degrade the output of the model,” he explains.

Model poisoning

Here, the attack goes after the model itself, seeking to produce inaccurate results by tampering with the model’s architecture or parameters. Some definitions of model poisoning models also include attacks where the model’s training data has been corrupted through data poisoning.

Tool poisoning

Invariant Labs identified this type of attack in spring 2025. When announcing its findings, Invariant wrote that it had “discovered a critical vulnerability in the Model Context Protocol (MCP) that allows for what we term Tool Poisoning Attacks. This vulnerability can lead to sensitive data exfiltration and unauthorized actions by AI models.”

The company went on to note that its experiments showed “that a malicious server can not only exfiltrate sensitive data from the user but also hijack the agent’s behavior and override instructions provided by other, trusted servers, leading to a complete compromise of the agent’s functionality, even with respect to trusted infrastructure.”

These attacks involve embedding malicious instructions inside MCP tool descriptions that, when interpreted by AI models, can hijack the model. These attacks essentially corrupt the MCP layer “to trick an agent to do something,” says Chirag Mehta, vice principal and principal analyst at Constellation Research.

CSO Smart Answers

Prompt injection

During a prompt injection attack, hackers use prompts that look legitimate but actually have embedded malicious commands meant to get the large language model to do something it shouldn’t. Hackers use these prompts to trick the model to bypass or override its guardrails, to share sensitive data, or to perform unauthorized actions.

“With prompt injection, you can change what the AI agent is supposed to do,” says Fabien Cros, chief data and AI officer at global consulting firm Ducker Carlisle.

Several notable prompt injection attacks and proofs of concept have been reported of late, including researchers tricking ChatGPT into prompt injecting itself, attackers embedding malicious prompts into document macros, and researchers demoing zero-click prompt attacks on popular AI agents.

Adversarial inputs

Model owners and operators use perturbed data to test models for resiliency, but hackers use it to disrupt. In an adversarial input attack, malicious actors feed deceptive data to a model with the goal of making the model output incorrect.

The changes to the perturbed input are typically small, or the deceptive data may be noise; the changes are deliberately designed to be subtle enough to evade detection by security systems but still capable of throwing off the model. This makes adversarial inputs a type of evasion attack.

Model theft/model extraction

Malicious actors can replicate, or reverse-engineer, a model, its parameters, and even its training data. They typically do this using publicly available APIs — for example, the model’s prediction API or a cloud services API — to repeatedly query the model and collect outputs.

They then can analyze how the model responds and use that analysis to reconstruct it.

“It’s enabling unauthorized duplication of the tools itself,” says Allison Wikoff, director and Americas lead for global threat intelligence at PwC.

Model inversion

Model inversion refers to a specific extraction attack in which the adversary attempts to reconstruct or infer the data that was used to train the model.

The name comes from the hackers “inverting” the model, using its outputs to reconstruct or reverse-engineer information about the inputs used to train the model.

Supply chain risks

Like other software systems, AI systems are built with a combination of components that can include open-source code, open-source models, third-party models, and various sources of data. Any security vulnerability in the components can show up in the AI systems. This makes AI systems vulnerable to supply chain attacks, where hackers can exploit vulnerabilities within the components to launch an attack.

For recent examples, see “AI supply chain threats loom — as security practices lag.”

Jailbreaking

Also called model jailbreaking, attackers’ goal here is to get AI systems — primarily through engaging with LLMs — to disregard the guardrails that confine their actions and behavior, such as safeguards to prevent harmful, offensive, or unethical outputs.

Hackers can use various techniques to execute this type of attack. For example, they could employ a role-playing exploit (aka role-play attack), using commands to instruct the AI to adopt a persona (such as a developer) that can work around the guardrails. They could disguise malicious instructions in seemingly legitimate prompts or use encoding, foreign words, or keyboard characters to bypass filters. They could also use a prompt framed as a hypothetical or research question or a series of prompts that leads to their end objective.

Those objectives, which also are varied, include getting AI systems to write malicious code, spread problematic content, and reveal sensitive data.

“When there is a chat interface, there are ways to interact with it to get it to operate outside the parameters,” Licato says. “That’s the tradeoff of having an increasingly powerful reasoning system.”

Counteracting threats to AI systems

While their executive colleagues jump into AI initiatives in search of enhanced productivity and innovation, CISOs must take an active role in ensuring security for those initiatives — and the organization’s AI infrastructure at large — is a top priority.

According to a recent survey from security tech company HackerOne, 84% of CISOs are now responsible for AI security and 82% now oversee data privacy. If CISOs don’t advance their security strategies to counteract attacks against AI systems and the data the feeds them, future issues will reflect on their leadership — regardless of whether they were invited to the table when AI initiatives were conceived and launched.

As a result, CISOs have a “need for a proactive AI security strategy,” according to Constellation’s Mehta.

“AI security is not just a technical challenge but also a strategic imperative requiring executive buy in and cross-functional collaboration,” he writes in his 2025 report AI Security Beyond Traditional Cyberdefenses: Rethinking Cybersecurity for the Age of AI and Autonomy. “Data governance is foundational, because securing AI begins with ensuring the integrity and provenance of training data and model inputs. Security teams must develop new expertise to handle AI-driven risks, and business leaders must recognize the implications of autonomous AI systems and the governance frameworks needed to manage them responsibly.”

Strategies for assessing, managing, and counteracting the threat of attacks on AI systems are emerging. In addition to maintaining strong data governance and other fundamental cyber defense best practices, AI and security experts say CISOs and their organizations should be evaluating AI models before deploying them, monitoring AI systems in use, and using red teams to test models.

CISOs may need to implement specific actions to counter certain attacks, says PwC’s Wikoff. For example, CISOs looking to head off model theft can monitor for suspicious queries and patterns as well as have timeouts and capture rate-limited responses. Or, to help prevent evasion attacks, security leaders could employ adversarial training — essentially training models to guard against those types of attacks.

Adopting MITRE ATLAS is another step. This framework, short for Adversarial Threat Landscape for Artificial-Intelligence Systems, provides a knowledge base mapping how attackers target AI systems and details identifying tactics, techniques, and procedures (TTPs).

Security and AI experts acknowledge the challenges of taking such steps. Many CISOs are contending with more immediate threats, including shadow AI and attacks that are getting faster, more sophisticated, and harder to detect, thanks in part to attackers’ use of AI. And given that attacks on AI systems are still nascent, with some attack types still considered theoretical, CISOs face challenges in getting resources to develop strategies and skills to counteract attacks on AI systems.

“For the CISO this is something that’s really difficult, because attacks on AI backends is still being researched. We’re at the early stages of figuring out what hackers are doing and why,” Lee, of SANS, says.

Lee and others recognize the competitive pressure on organizations to make the most of AI, yet they stress that CISOs and their executive colleagues can’t let securing AI systems be an afterthought.

“Thinking about what these attacks could be as they build the infrastructure is key for the CISO,” says Matt Gorham, leader of PwC’s Cyber and Risk Innovation Institute.