The headline number and what sits behind it
Forrester's 2027 technology and security predictions put a concrete figure on a trend every CIO has felt anecdotally: global technology spending will reach 6.1 trillion dollars in 2027, and AI alone will account for 20 percent of all business technology spend. That is not a forecast of AI spending growing at the margins of an otherwise stable budget, it is a forecast of AI becoming the single largest identifiable category inside enterprise technology spend within roughly a year.
Sharyn Leaver, Forrester's chief research officer, frames the consequence plainly: AI will command an unprecedented share of technology budgets in 2027, intensifying pressure on leaders to prove its value while maintaining control over cost, infrastructure, and risk simultaneously. That is three distinct pressures compressed into one budget line, and most organizations' current AI governance structures were not built to manage cost, infrastructure capacity planning, and risk oversight as a single coordinated discipline.
Why a fifth of the budget is a governance problem, not just a finance one
When any single category reaches a fifth of total technology spend, it stops being something a CIO can manage through the normal project-by-project approval process and starts requiring the kind of dedicated oversight structure usually reserved for the entire IT budget itself. Finance teams will want portfolio-level visibility into AI spend the same way they expect it for cloud or headcount, and most organizations do not yet have the tagging, chargeback, or reporting infrastructure to produce that visibility on demand.
The pressure to prove value that Leaver describes compounds this problem rather than existing alongside it. A budget category this large invites board-level scrutiny almost automatically, and a CIO who cannot show a credible return on a fifth of the technology budget will face that scrutiny regardless of whether the underlying AI investments are actually working. Building the measurement infrastructure now, before the 2027 budget cycle locks in, is cheaper than building it under pressure after a board meeting goes badly.
The chip diversification prediction CIOs should take seriously
Forrester predicts that a quarter of AI infrastructure RFPs will require non-NVIDIA accelerator options in 2027, driven by enterprises looking to cut costs and reduce supply chain and geopolitical risk. This is a meaningful shift from the assumption, common through the current AI buildout, that NVIDIA's ecosystem is simply the default choice with no serious alternative worth evaluating in a procurement process. A quarter of RFPs carrying that requirement is large enough to change how chip vendors compete for enterprise accounts, not just how individual buyers negotiate price on their next order.
For a CIO planning infrastructure three to five years out, this prediction is a signal to build procurement processes now that genuinely evaluate alternatives rather than treating an RFP requirement for non-NVIDIA options as a box-checking exercise. Vendor diversification in chips carries the same logic as vendor diversification anywhere else in enterprise technology: single-vendor dependency concentrates risk, and that risk becomes more expensive to unwind the longer an organization waits to build real alternatives into its sourcing strategy. Start by asking your current infrastructure partners what their non-NVIDIA roadmap actually looks like today, before an RFP forces the question under time pressure.
The outage prediction nobody wants to be the example of
Among Forrester's more pointed predictions is a major outage across multiple industries caused by faulty AI-generated code. This is a specific, concrete risk scenario rather than a general warning about AI quality, and it names a failure mode, AI-written code reaching production with defects serious enough to cause a cross-industry outage, that most current software quality and AI governance frameworks do not explicitly address as a named risk category. Naming the failure mode in advance is itself useful: a board that has heard this specific prediction is harder to surprise than one that only knows AI code quality is a vague, general concern.
The practical implication is that code review, testing, and release processes built for human-written code may not transfer cleanly to a world where a meaningful share of production code originates from an AI coding assistant. CIOs should ask specifically whether their current quality assurance pipeline was designed with AI-generated code volume in mind, or whether it simply assumes AI-assisted code behaves like human-written code for review purposes, an assumption Forrester's prediction suggests may not hold. Treating AI-generated code as a distinct review category, with its own test coverage expectations, is a cheap step relative to the cost of being the outage Forrester is describing in the abstract.
Quantum: the investment gap Forrester says will not close quietly
Forrester's data shows 87 percent of organizations already familiar with quantum computing are still not allocating any resources to it, a striking gap given how much attention quantum receives at the research and analyst level. Despite that gap, Forrester predicts an unexpected quantum advance will occur in 2027, a combination that sets up exactly the kind of surprise that tends to catch unprepared organizations flat-footed when a research breakthrough suddenly becomes a competitive or security consideration.
This is not a call for every enterprise to start a quantum program immediately. It is a case for maintaining enough situational awareness that an unexpected advance does not arrive as a complete surprise to the technology leadership team. A CIO does not need a quantum budget line in 2027, though having someone on the team whose job includes tracking quantum developments closely enough to brief leadership quickly is a cheap insurance policy against Forrester's prediction landing on schedule.
What to take into the next budget cycle
Forrester's 6.1 trillion dollar figure and 20 percent AI share are useful less as precise forecasts than as a scale check against your own organization's trajectory. If your AI spend is tracking well below that share while competitors chase the broader trend, that gap is worth understanding deliberately rather than assuming it reflects discipline. If your AI spend is tracking well above it, the measurement and governance infrastructure Leaver's warning implies becomes urgent rather than optional.
Either way, the specific predictions matter more than the headline number for planning purposes: chip diversification in procurement, AI-generated code as a named quality risk, and quantum awareness as a low-cost hedge are three concrete actions a CIO can take into the next planning cycle regardless of where their organization's AI spend currently sits relative to Forrester's 20 percent benchmark. None of the three requires a large new budget line, which makes them a reasonable starting point even for a CIO who is still building the case for a bigger AI governance investment.
