Gartner predicts that by 2028, 70 percent of enterprises will abandon agentic AI systems built with vendor assistance as costs climb and customers struggle to modify the technology without outside help.

What the consultancy calls forward-deployed engineering (FDE) embeds a vendor’s engineers with a customer to build and deploy software for its particular requirements. Gartner argues that the model can deliver rapid early progress but leave customers dependent on expensive external expertise.

According to Gartner, FDE engagements can fail when customers do not acquire the knowledge and control needed to maintain and develop the resulting systems after the vendor leaves.

FDE “success starts with getting the engagement structure right, from scope and incentives to governance, ownership, and exit,” said Gartner senior director analyst Mukul Saha.

“The best-scoped FDE engagements have clear guidelines on governance, business value delivery, IP ownership, project co-ownership, knowledge transfer, and an exit strategy from day one,” he added.

“Many providers now use ‘forward deployed’ as a label for implementation, professional services, solution engineering, or AI consulting; some thoughtfully, others because it sounds more strategic. Some charge premium fees without the delivery depth, program management, or change management maturity to justify them.”

Gartner advises software engineering leaders to use FDE only for problems requiring deep product expertise, rapid adaptation, or close integration between the vendor’s technology and the customer’s operating environment.

Otherwise, customers may see rapid early progress without building the internal capabilities needed to take control. They could then end up paying premium rates for work that a traditional services or partner model might deliver more cost-effectively and predictably.

Gartner also predicts that through 2028, fewer than 20 percent of FDE engagements will turn recurring customer requirements into features in the vendor’s core product. It warns of a growing risk of “FDE washing,” in which conventional consulting services are marketed as something more specialised.

This isn’t the first time that Gartner has warned of the potential costs and pitfalls of AI projects.

Earlier this year, it forecast that at least half of generative AI projects will exceed their budgets due to poor architectural choices and lack of operational know-how, while most organisations that try to build custom models will abandon them for similar reasons.

It also said it expects 40 percent of AI agent deployments would be scaled back or decommissioned as organisations run into governance problems with the technology.

More recently, the analyst predicted that nearly a third of employees laid off because of AI will need to be rehired again at a future date, often at significantly higher cost.

Worldwide AI spending will increase 49.5% in 2026, to $2.7 trillion, and grow another 36.2% in 2027 as AI usage continues to expand, Gartner predicts in the latest quarterly update to its IT spending forecast.

But there’s been no diversion of money from other areas of IT to feed AI, said Gartner Distinguished VP Analyst John-David Lovelock. CIOs got some net new money for AI back in 2024, and a little in 2025, he said, “so there wasn’t a diversion, and now more of their spending is about rebranding than diversion.”

By 2030, “every dollar is going to be an AI dollar in one way or another,” he said.

Instead of buying just a laptop, companies now buy a laptop with AI chips in it, while enterprise software now has AI embedded in it. And instead of doing a project about business strategy, they now do a project about how AI might change business strategy.

“In one way, it’s kind of rebranding,” he said. “Net new spending is going more towards AI, and existing spending is being transformed towards AI.”

It’s similar with hyperscalers: “The hyperscalers haven’t diverted one dime away from their cloud. So AWS, Google, Microsoft, Meta are all continuing to build out their cloud infrastructure at the same rate they were in 2022.”

The AI buildout “is the largest infrastructure project humanity has ever undertaken,” he observed.

Gartner updates its spending forecasts quarterly. In its latest report, it predicted AI infrastructure spend will grow 51.2% this year, and AI software 60.2%. Spend on AI agents and assistants, which it breaks out separately from other software, is forecast to increase by 77.3%, and AI security spend to almost double.

As AI development platforms prove themselves, Gartner sees spending on them accelerating: It has increased its 2026 growth forecast for them from 28% in its May report to 39%, attributing the change to the development of custom AI applications by enterprises and software and service providers.

AI model providers are seeing a “small but growing opportunity” for domain-specific language models aligned with enterprise use cases, upping the 2026 growth rate for generative AI models to 117%.

Analyst firm Gartner believes over half of VMware users will start evaluating alternative hybrid cloud platforms yet still rates the Broadcom business unit a leader in two major markets.

Gartner predicted increased interest in VMware exits in its Magic Quadrant for Distributed Hybrid Infrastructure (DHI) – aka hybrid clouds – which opens with a “Strategic Planning Assumption” that “By 2029, 55 percent of enterprises will initiate proofs of concept for alternative distributed hybrid infrastructure products to replace their VMware-based deployments and embrace hybrid cloud infrastructure delivery, up from 25 percent in 2026.”

Gartner’s mention of 2029 feels noteworthy, because that year will be six years after Broadcom’s acquisition of VMware. As previously written, plenty of VMware users acquired fresh three-year subscriptions in the months before the deal closed. Migrating from VMware to a rival platform is a risky and complex project, and we often hear of customers deciding it is safer to stick with Broadcom for longer while they hatch plans. If orgs in that position refresh for another three-year sub during 2026, 2029 would be a natural jumping off point.

Numerous virtualisation contenders are trying to lure Virtzilla’s customers, yet progress is slow. Nutanix CEO Rajiv Ramaswami has often said it will be several years before some are ready to move. While Gartner predicts a majority of VMware customers will contemplate a move, the Broadcom business unit will keep most of its customers for at least another three years – and recently turned its attention to refreshing its low-end vSphere Standard product, a move that will give it a chance to retain more current users.

The DHI Magic Quadrant rates VMware as a leader in the field, alongside AWS, Nutanix, Microsoft and Oracle. Gartner rates VMware’s core virtualisation technology, formal sovereign cloud ecosystem, and AI-native infrastructure as strengths.

The cost of agentic AI workflows is forecast to increase more than fivefold by the end of 2028 as users adopt more complex applications of the technology.

As Nvidia and other tech giants push inference and agentic AI as the next stage of the AI wave, Gartner warns that the cost of implementing these systems will rise even as foundation models become cheaper.

The analyst firm has cast its eye over the nascent world of AI agents – systems designed to act independently in pursuit of a goal – and sees multiple challenges ahead.

Leaving aside the substantial security concerns, these software agents are considerably more complex than chatbots.

Gartner believes falling model prices are tempting users to build more complex workflows, whose greater token consumption can outweigh those savings and drive up overall inference costs.

In other words, tokens are becoming more cost-efficient, but those savings are not keeping pace with the rising cost of more advanced AI capabilities. The rate of innovation is outpacing the cost curve, Gartner claims.

“The harsh economics of the inference paradox are exemplified by the differences between a simple chatbot and an AI agent,” says Gartner senior director analyst Will Sommer.

“Where a simple chatbot must read and interpret a query and quickly respond with a probabilistic reasonable answer, an AI agent must constantly reason, negotiate, and question itself,” he explains.

Those processes add up: routing a task to an agentic reasoning model increases inference costs at least fivefold, and potentially by much more as the task becomes more complex.

Securing a return on investment from such advanced AI tools therefore demands either much greater returns than basic models provide or better optimisation of inference, routing, and orchestration, Gartner warns. This could mean assigning each task to the most cost-efficient model capable of handling it.

The move by some AI providers from flat-rate subscriptions to usage-based billing hasn’t helped. Token-heavy workflows can produce runaway costs under the new model.

Perhaps it is no wonder Gartner predicted earlier this year that 40 percent of organisations would demote or decommission AI agents because of problems with the heavily hyped technology.

The analyst biz also cheerily forecast last year that at least half of all generative AI projects would blow their budgets because of poor architectural choices and a lack of expertise, while most attempts to build custom models would be abandoned.

A quarter of the work performed by IT infrastructure and operations people will be handled by AI in the year 2030, according to analyst firm Gartner, despite AI also complicating your environment and possibly creating outages.

Those predictions landed on July 10 in Gartner’s 2026 Hype Cycle for AI in IT Operations, the firm’s view on the future of AI-powered infrastructure management tools.

Gartner thinks there’ll be pain before AI-powered automation pays off.

“Many AI-for-IT-operations narratives promise tool consolidation. Agents will query multiple systems, reason across silos and reduce dependence on specialised tools,” the document states, before predicting “the opposite outcome in the near term.”

For at least a couple of years, you’ll have to wrestle with “more layers, more control points, and more specialised observability, orchestration and management capabilities.”

The pain will lessen once “future market and vendor consolidation reduce the overall tooling footprint.”

Between now and whenever that happens, Gartner suggests ops teams’ strategic planning assumptions include the increased likelihood of AI having a role in an outage.

“By 2028, 40 percent of I&O organisations that use agentic I&O at scale in production will experience a business-critical service disruption, up from less than 1 percent of organisations in 2026,” the document states.

You, or your bosses, will apparently be undeterred by that trend. Gartner thinks that by 2029, 60 percent of enterprises will deploy agentic AI as a part of IT infrastructure operations – up from fewer than ten percent today. In the same year, just 20 percent of actions suggested by AI will happen after human-in-the-loop approval, down from 80 percent in 2025. That shift will happen due to increased use of “deterministic guardrails” – policy-driven rules that determine what an AI is allowed to do.

A year later, in 2030, Gartner thinks bosses will have restructured half of all infrastructure and ops teams after investing in agents to handle complex management tasks. In the same year, those of you still working in the field will use AI for every task that humans handle.

“75 percent will be done by humans augmented with AI, and 25 percent will be done by AI alone,” Gartner predicts.

Gartner thinks the following technologies will be mature in the next couple of years, and therefore set us all on the road to this fabulous future:

Gartner has slammed AI vendors’ lack of transparency, saying developers are facing sharply increased costs from coding agents.

Since the main AI coding agent vendors have shifted from seat-based licensing to consumption-based pricing, developer teams now face highly variable cost structures.

Developer teams face an emerging problem of escalating costs, said Nitish Tyagi, senior principal analyst at Gartner. AI coding bills were leaping from $20 or $100 to $2,000 to $5,000 per developer per month, while in extreme cases, the bill might hit $20,000 in token charges.

However, software engineering departments get little insight into how token consumption is calculated and billed, making it difficult for them to forecast and control costs accurately. AI coding vendors have not yet delivered built-in features to allow developers to optimise costs in AI coding agents, resulting in cost escalation, Gartner said.

“None of the vendors have incredible features when it comes to cost optimisation,” said Tyagi. Instead, he said vendors were focusing on the concept of “tokenmaxxing” to “boost the high” of token consumption, suggesting that if

developers increase the number of tokens, they will increase productivity gains.

“There is no direct relation between the increase in token consumption and an increase in productivity gains,” he said.

Gartner recommends developer teams optimise token consumption and adopt strategies such as context engineering practices, where software engineers improve the input context provided to AI systems. Another recommended strategy is model routing, where engineering and platform teams direct simpler, high-frequency tasks to smaller models, using frontier models only for complex, high-value work

“All of these things will improve the output quality, and, therefore, will increase the productivity gains as well, so while there is no direct relation between tokenmaxxing and productivity gains, there is a relation between optimisation of token consumption with the output quality,” Tyagi said.

As a result of the lack of cost optimisation tools among vendors and consumption-reduction strategies among users, AI development is in a situation where a developer’s coding agents may cost more than they earn, at least in some parts of the world. Gartner predicts that by 2028, AI coding costs will overtake the average developer’s salary due to rising LLM token consumption and the shift to consumption-based licensing models.

“We’re not saying AI token cost will be higher than every developer’s salary on the planet, because US salaries tend to be higher than in India, for example. But current token costs are already more than most of the salaries in India,” he said.

However, the cost of coding agents does not vary according to where they are consumed, meaning AI coding costs for a developer in India may be equivalent to the salary of an engineer with four to six years’ experience, Tyagi said.

“We’re not saying AI token costs will be higher than every developer’s salary on the planet, because US salaries tend to be higher than in India, for example,” Tyagi said, adding that token costs do not vary by location, and in India they may already be equivalent to the salary of an engineer with four to six years’ experience.

Organisations that decide to reduce their VMware footprints, or quit entirely, will emerge with more complex and less capable infrastructure.

That’s the view of Paul Delory, a research vice president with analyst firm Gartner, who told the company’s IT Infrastructure, Operations & Cloud Strategies Conference in Sydney that there is no technical reason for VMware users to adopt a rival hypervisor, and that no vendor offers a one-for-one replacement for the virtualisation pioneer’s flagship Cloud Foundation (VCF) suite.

But Delory said Broadcom’s licensing policies, which see it only sell VCF, mean VMware users’ licensing bills typically rise by 300 to 400 percent. Broadcom argues that the full-stack private clouds VCF makes it possible to build are so efficient that VCF quickly pays for itself.

The analyst told the conference he thinks those contemplating a move off VMware will do better if they instead focus on application modernisation. But he said Broadcom’s price changes, and the prospect the company might hike prices again in future, mean many VMware users will look elsewhere.

Those who do, he warned, will end up with more complex infrastructure for two reasons.

One is that few organisations will be able to quit VMware entirely, as they run applications with dependencies that aren’t easy or economical to unwind. Reducing or eliminating a VMware rig therefore means adopting multiple replacements, which creates more infrastructure to manage and therefore extra complexity.

The other is that no rival hypervisor can match the efficiency or VM density possible when using VMware’s products, so moving means acquiring more hardware.

Delory said the best alternatives to VMware are the public cloud, or HCI vendors – these days that acronym denotes both hyperconverged infrastructure and hybrid cloud infrastructure.

The analyst warned that HCI vendors, with the exception of Nutanix, have weak migration tools that will leave users needing to create bespoke migration automations using “Ansible and a Rube Goldberg machine.”

Public clouds, he said, will welcome customers who move 1,000 or more VMs with free migration services.

He recommended against considering OpenStack, which he said remains “too big, too complex, and has too many moving parts for the typical IT shop to handle effectively.”

Delory also warned VMware users that migration projects are significant engineering undertakings that require extensive assessment of every application in a fleet to determine its best destination, and the work required to get it there.

He reminded VMware users that not every workload is certified to run under non-VMware hypervisors, and that some vendors now offer cloud-native versions of their wares and therefore offer an easier on-ramp to containerised applications.

Delory advised exploring those options, and not making architectural decisions that mean you can’t consider moving off VMware.

“VMware is betting that you can’t move off and they can jack the price way up,” he said. “That may be a good bet. But don’t make it easy.”

The analyst finished his talk by predicting most users will minimise their VMware footprints, rather than eliminating them, and restated Gartner’s prediction that 35 percent of workloads currently running under VMware will operate on a different platform by 2028.

It’s not possible to operate a completely sovereign cloud outside of China or the USA, according to Douglas Toombs, a VP analyst at Gartner.

Speaking at the analyst firm’s IT Infrastructure, Operations & Cloud Strategies Conference in Sydney, Toombs said only the US and China make all the tech needed for a sovereign cloud. Buyers elsewhere can’t avoid relationships with foreign providers.

Toombs said that while US-based cloud vendors have created products they say can meet the needs of organisations that need a cloud that doesn’t have legal entanglements outside their chosen jurisdiction, the fact they’re ultimately owned by American corporations means it’s not possible to be certain a cloud provider can promise complete sovereignty.

Even on-prem clouds like AWS Outposts, Azure Local, or Oracle’s Dedicated Cloud Regions, “need to phone home,” he said.

The analyst doesn’t think attempts to create sovereign clouds will succeed. He mentioned past French attempts to create sovereign clouds named “Andromeda”, “Numergy,” and “Gaia-X”, which he says went nowhere – but did produce some nice white papers.

He also cited the The Rule of Three and Four, a maxim developed by Boston Consulting Group that asserts “A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest,” and argued that it predicts the cloud market has settled around AWS, Google, and Microsoft.

Toombs allowed that some smaller clouds could thrive and will make it feasible to create sovereign SaaS providers and products.

But he thinks that even aggressive moves to go on-prem won’t free organisations from dependency on US-owned clouds, an assertion he backed with the example of a Dutch healthcare provider that tried to build its own infrastructure but then experienced an outage when a supplier’s services went down along with a major cloud provider.

If sovereign clouds fail to develop, it may be problematic because some European organisations are worried US-based cloud operators might leave the continent, forcing them into hasty and risky migration projects, according to Adrian Wong, a Gartner Director Analyst who also spoke in Sydney.

Wong said “heightened geopolitical tensions” are causing customers of major clouds to rethink their strategies, a decision he welcomes because he sees very few organisations bother to develop a cloud exit strategy.

“Exit plans are overlooked,” he said, and users are “very much locked in” – especially when they use cloud-native services or platform-as-a-service.

“Exiting within a timeframe of anything less than two years takes significant planning and investment,” he warned. “Exit strategies and plans are largely swept under the rug.”

Wong says he is now seeing “the pendulum swing.”

Not developing a cloud exit strategy is one of the ten big mistakes Wong sees users make. Also on his list are starting use of clouds with mission-critical and complex applications like ERP, assuming the cloud is appropriate for all applications, and expecting to get all the benefits of the cloud with every application.

He also said it’s folly to assume that going multi-cloud will improve availability – unless users first tackle the more complex and expensive task of making applications portable. Wong said organisations that use multiple clouds should do so to access specific features of each, not to improve resilience.

VMware users considering a new home might find it cheaper to move to an IBM mainframe than adopting Broadcom’s new licenses, according to Gartner Vice President Analyst Alessandro Galimberti.

Speaking to discuss the analyst firm’s mid-April publication, “The State of the IBM Mainframe in 2026,” Galimberti said some buyers in many fields are comparing mainframes to modern environments and deciding Big Blue’s big iron comes out ahead.

“I can build a multi-region cloud application, but things like data synchronisation and high availability are things I need to build into application logic,” he said. “The mainframe has that in the platform, which shields developers from complexity.”

He also thinks mainframes are ideally suited to workloads that need many years of transactional consistency and backward-compatibility.

That said, Galimberti doesn’t recommend the mainframe for all applications. He said mission-critical applications that are unlikely to change much for a decade are best-suited to the machines, as are Linux applications because the open source OS runs on IBM’s hardware.

IBM also offers the z/VM hypervisor, which he says can make Linux “even better and more enterprise-ready.”

Which is why Galimberti thinks IBM’s ecosystem is attractive to VMware users, especially those who operate a fleet of 500 to 700 Linux VMs.

Galimberti said he has seen “multiple business cases” in which moving from VMware to IBM makes sense under Broadcom’s policy of requiring customers to buy its full Cloud Foundation private cloud stack. The analyst said he was surprised those business cases make sense, but insists a mainframe makes sense “under some circumstances.”

AI is another workload Galimberti thinks some users will find works well on mainframes, because IBM recently upgraded its Spyre accelerators

The analyst says items like Spyre show that IBM continues to invest and innovate, and that the mainframe “is not a stale platform.”

Galimberti said the idea that mainframes were a dead end has been around for decades and was justifiable at some moments in history. But he also feels the idea was oversold by consultants who promoted migrations away from mainframes as sure-fire winners – but under-estimated the complexity of a move.

“Global system integrators were always against the mainframe and encouraged migrations,” he said. “Now they are halting the mainframe exit, probably because the number of failures they had were more than their successes.”

The firm now advises assuming that by 2030, 75 percent of vendors offering mainframe exit services operating in the “mainframe exit” market will either change their businesses or vanish, and suggests that only 10 percent of mainframe users will want out by 2030.

Which is not to say mainframes are perfect or easy to live with. Galimberti noted that few third-party software developers support mainframes, and those that do understand the potential to hike prices.

Committing to mainframes therefore means planning “to spend time negotiating price and renewal protections, rather than prioritising the business value these solutions can deliver.”

Another downside is that mainframes pose clear lock-in risk, so users may hold back on useful customisations out of fear they make it harder to extricate themselves from the platform.

Access to skills remains an issue, too, as kids these days mostly don’t contemplate a career working with big iron. Galimberti sees more service providers investing in their mainframe programs, which might help. So does the availability of Linux.

The analyst thinks the mainframe will be around for some time to come, because IBM is “very engineering-focussed” and “engages with customers who are hyper-conservative” – then just creates the products they want.

Citius, Altius, Fortius. More than a slogan, “Faster, Higher, Stronger Together” is a phrase etched in the minds of many IT decision-makers. Technology is essential. Nobody wants to fall behind. Doing so could mean losing out on many advantages and, consequently, significant business.

Therefore, it’s not surprising that Gartner forecasts global IT spending to reach $6.31 trillion this year, a 13.5% increase compared to 2025 — and nearly 3% more than Gartner forecasted in February, at 10.8% growth.

A sustained push in AI infrastructure, software, and IaaS is key to this growth, according to the research firm. “These shifts are reinforcing a multi‑speed IT market, with hyperscaler purchases and AI‑centric software segments significantly outperforming more traditional categories,” Gartner reported in a statement.

John-David Lovelock, analyst and distinguished vice president at Gartner, underscored that accelerated momentum in AI infrastructure and advanced memory played a significant role in the firm’s growth revision.

“As AI workloads scale, datacenter investment is ramping rapidly, which in turn is driving increased demand for high‑performance compute,” Lovelock said in the statement. “This dynamic is creating meaningful growth opportunities for companies delivering AI‑optimised processors, accelerators, and enabling technologies.”

What will grow the most? Spending on datacenter systems, at 55.8%, to exceed $787 million in 2026.

Supply constraints and strong demand have also resulted in record price increases for high-bandwidth memory, according to Lovelock, as hyperscaler demand drives sharp increases in investments in servers. “These trends collectively make AI infrastructure the most attractive segment for capitalising on the robust expansion in IT spending,” he said.

Those same higher memory prices are impacting device replacement cycles, according to the firm. Device spending is growing, to be sure, but at a lower rate (8.2%) than the IT sector overall.

“Together, these dynamics highlight a widening divergence across IT markets, as AI infrastructure and GenAI software see substantial upward revisions while device growth reflects ongoing cost and pricing pressures,” Lovelock noted.