Organisations are increasingly using agentic coding tools to build software in-house in lieu of purchasing it, according to a McKinsey report titled “The state of AI in 2026”.

Nearly a third of respondents (32 per cent) report that their organisations have decided against buying one or more software products or features because they could be built internally with agentic coding tools.

The trend spans several sectors, with technology companies leading the shift away from external software procurement. Across industries, 41 per cent of respondents in the technology sector report forgoing software purchases in favour of internal development using AI coding tools.

This is followed closely by healthcare payers and providers at 39 per cent, while both professional services and energy and materials stand at 38 per cent.

Financial institutions register 36 per cent, media and telecom at 34 per cent, and pharmaceuticals and medical products at 33 per cent.

Lower rates appear in sectors such as insurance at 19 per cent and the public and social sector at 17 per cent.

McKinsey Senior Partner Lieven Van der Veken said, “Not only are larger organisations scaling AI faster, but many are beginning to take greater ownership of their technology and change agendas. Until recently, many leaders assumed that AI was beyond the capabilities of their own technology teams, and that moving quickly meant securing the right external partnership.”

Van der Veken added, “Now, the tone is changing. Leaders are asking what their organisations need to build AI tools themselves. The rise of software coding agents and in-house development is one clear sign of this broader shift.”

The report noted that enterprises are progressing beyond the initial phase of AI experimentation. As per the report, nearly nine in ten respondents report regular use of AI in at least one business function. Overall enterprise-scale adoption expanded over the past year, with 44 per cent of respondents reporting that AI is scaling across their organisation, compared to 38 per cent previously.

Furthermore, the proportion of organisations deploying AI across three or more functions grew from 51 per cent to 56 per cent.

Addressing this strategic evolution, Van der Veken said, “This, of course, does not mean companies should build everything themselves or turn away from forging partnerships. It means that leading organisations are engaging partners from a position of agency.”

He stated, “The organisations moving fastest are becoming more deliberate about where to buy, where to build, and where to develop enough internal capability to integrate and scale what works. They are also treating operating costs as a design constraint, not an afterthought, and making the deeper changes in workflows required for value realisation.”

Adoption patterns show larger enterprises outpacing smaller firms in deploying AI capabilities. Fifty-four per cent of respondents from organisations generating at least USD 1 billion in annual revenue report scaling AI enterprise-wide, compared to one-third of smaller organisations.

Larger enterprises also registered faster momentum in adopting agentic AI, with the share scaling agents in one or more functions increasing from 27 per cent to 40 per cent, while smaller organisations remained flat at 22 per cent.

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.

Microsoft is moving parts of its AI portfolio beyond pure per-seat pricing by pairing user licenses with charges tied to usage. The hybrid model keeps recurring seat fees in place while adding a variable billing unit as customers consume more AI capacity.

That model is beginning to show up in Microsoft’s financial results. Dynamics 365 Customer Service credit consumption rose fourfold quarter over quarter, while GitHub Copilot generated what Microsoft called “significant consumption revenue” after its June billing change.

Microsoft also reported more than 30 million paid Microsoft 365 Copilot seats, with net additions more than doubling from the previous quarter, and said GitHub continued adding Business and Enterprise seats.

The UK’s Competition and Markets Authority has opened a fresh investigation into Microsoft, this time over how the company is marketing its Microsoft 365 Personal and Family subscription plans.

A cursory glance at the probe highlights that the CMA is trying to prevent Microsoft from sneaking in Copilot and AI features as justification for recent subscription price hikes. A closer look at the investigation displays a pattern of friction Microsoft runs into with regulators regarding its subscription practices, automatic renewals, and software bundling almost every time it adjusts its services.

The CMA made the investigation public on July 29, 2026, though the case itself was opened two days earlier. According to the CMA’s case announcement, the price gap between plans is concrete. The Classic Microsoft 365 Personal plan costs £59.99 a year, compared to £84.99 a year for the Copilot-equipped version, a difference of £25 annually. The Family plan costs £79.99 a year for Classic versus £104.99 a year with Copilot bundled in.

The CMA’s announcement hinges on Microsoft’s change in January 2025, when the company automatically added new features such as Copilot to existing Microsoft 365 plans with no increase in fees for the remainder of the subscription period. Unfortunately, once the plans were up for renewal, customers who were not paying attention were re-enrolled into higher-priced plans. Customers who were paying attention could avoid the bait-and-switch by selecting a ‘Classic’ plan or cancelling during the re-enrolment period.

This price change was covered when it first happened in February 2025, and even then, Microsoft would not directly attribute the increase to Copilot in their own communications. Personal plans jumped from $6.99 to $9.99 a month in the US, and Family plans went from $9.99 to $12.99, with the Classic option only showing for existing subscribers who knew to look for it during cancellation.

CMA regulators are now examining whether Microsoft provided adequate messaging for customers to understand what changes occurred and how much more they were going to pay despite spending months using features that were initially presented as free.

As Senior Director for Consumer Protection, Hayley Fletcher puts it, “When a business changes its subscription plans, customers need clear and timely information about their options.”

As enterprises accelerate investments in AI, many are discovering that the hardest part of adoption is no longer experimenting with large language models but turning them into trusted systems capable of running critical business operations. They are also baffled about what return on investments (ROI) will AI deliver.

According to Manik Narayan Saha, Managing Director for SAP Labs, East Asia, that may no longer be the right question. Speaking at the ATxSummit, Saha argued that traditional ROI models struggle to capture the impact of AI because the technology is fundamentally changing how work is performed rather than simply making existing processes faster.

Instead of relying solely on conventional financial metrics, organisations should focus on the broader business value AI creates through productivity gains, faster decision-making and new ways of operating, he added.

Saha said traditional ROI models assume that technology simply improves existing processes. AI, however, is changing how those processes are designed and executed in the first place, making it increasingly difficult to compare performance against historical benchmarks.

“As organisations redesign workflows around AI, the baseline itself changes. You’re no longer measuring incremental improvements to the same process but measuring entirely new ways of working,” Saha said.

He added that enterprises should therefore look beyond conventional financial metrics and instead assess how AI contributes to faster decision-making, greater productivity, improved customer experiences and new business capabilities.

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:

Forrester warns that customers should brace for bigger software bills next year as software and AI vendors raise prices and pile on usage charges.

Working from a survey of more than 2,600 business and technology decision-makers, the tech research company said software budgets were expected to rise “as vendors increase prices or add usage charges to pass their AI costs to customers.”

In the last six months, Anthropic, OpenAI, and GitHub have shifted some services away from flat-rate subscriptions toward usage-based billing, prompting cost concerns among users. Forrester added Microsoft to the list, citing its recent launch of the premium E7 license, which bolts M365 Copilot, Agent 365, and security tools onto E5.

Last year, consultants Bain & Company estimated that the build cost for AI datacenters would hit $2 trillion by 2030.

Forrester said that AI would drive increases in data and software spending, with 80 percent of decision-makers expecting those budgets to rise.

Sharyn Leaver, chief research officer at Forrester, said: “The organisations that outperform in 2027 won’t be those that spend the most on AI. They’ll be the ones that invest in the foundations that make AI effective: trusted data, strong governance, organisational readiness, and the ability to continuously adapt as technology and customer behaviour evolve.”

Forrester also found that personnel costs have yet to fall, despite the “AI washing of layoffs” in the tech industry.

“While several tech giants, including Oracle, Microsoft, and Meta, have announced significant layoffs in recent months, IT staffing spend has not declined in recent years,” the report said.

Staffing accounted for 35 percent of IT budgets in 2025. For 2027, 67 percent of tech decision-makers expected to increase their staffing budget, while 23 percent said it would stay flat, and 10 percent expected it to decline. “The AI washing of layoffs will continue as vendors trim for financial and restructuring reasons. Guard against inflated promises that AI can replace employees across the board. Staffing for data/analytics-specific roles is expected to rise, with 68 percent of data technology decision-makers expecting this budget item to increase,” the report said.

Forrester said that organisations should adapt their FinOps practices to help manage the unpredictable costs associated with AI. “Traditional FinOps wasn’t built for token-based, usage-driven AI costs, but that team is certainly best positioned to build these new capabilities and must make this leap in 2027. Fund runtime cost controls such as model routing, semantic caching, and usage guardrails to prevent runaway spend,” the report recommended.

In July, KPMG research found that nearly a third of corporate leaders reported difficulty understanding and controlling operating costs when implementing business AI at scale. “As usage-based pricing models become more common, many organisations are still building the capabilities required to forecast, monitor, and manage AI spending effectively,” the consultancy said.

Enterprises are facing two simultaneous challenges with AI: The risks associated with it are evolving faster than governance frameworks, while the business benefits are often difficult to measure.

This is one of the key findings of The Value of AI, a study commissioned by SAP from Oxford Economics. Now in its second year, the study surveyed 2,600 executives from 13 countries worldwide.

As organisations scale across SaaS, public cloud and AI, ITAM teams are being asked to do more than track assets: They’re expected to restore visibility, prove value and govern environments that are growing faster than policy can keep up.

Based on a global survey of 512 professionals, Flexera’s 2026 State of ITAM Report shows clear progress. But complexity continues to outpace control.