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.

Flexera’s newly-released 2026 State of ITAM Report reveals a growing disconnect: Organisations are investing heavily in AI tools, yet many lack the visibility and governance needed to manage them effectively. This tension creates a new challenge, and IT leaders are asking themselves:

How do we govern AI usage without slowing innovation and limiting ROI?

Below, is a breakdown of the key AI-specific findings shaping enterprise ITAM strategies today.

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.