Gartner forecasts AI strain on services, costs & jobs

Gartner has published its top strategic predictions for 2027 and beyond, setting out 10 forecasts on how AI could affect business, government and public services.

The list groups the forecasts into three themes: robots everywhere, cost to value, and unknown unknowns. Several focus less on productivity gains than on the operational strain and governance challenges that may follow wider AI adoption.

Daryl Plummer, Distinguished VP Analyst and Gartner Fellow at Gartner, said: "Many of the systems we take for granted today, from public services to software, energy and workforce models, will be fundamentally transformed by AI over the next decade. The most successful organisations will be those that balance innovation with responsibility, building the capabilities needed to manage both the opportunities and the unintended consequences of an AI-driven world."

One of the most striking forecasts is that more than 10 billion autonomous agents created by individuals, companies and governments will clog public services by the end of 2030. Software agents acting on behalf of users could sharply increase the volume of applications, claims and transactions submitted to government systems.

That projection points to a less discussed side of agentic AI. If digital tools can identify eligibility for services and complete requests with little human effort, public agencies may face surges in demand that existing infrastructure was not built to handle.

Rising cost risks

Another forecast centres on the cost of public-facing AI. By 2030, 80% of organisations with such systems will have experienced a cost exhaustion attack, a scenario in which malicious actors drive up usage to inflate operating costs.

The idea reflects a shift in how AI risks are assessed. Token usage, inference volumes and consumption levels are moving beyond budgeting concerns into cyber risk, especially for customer-facing systems that can be accessed at scale.

Cost control appears repeatedly across the predictions. By 2029, 60% of organisations deploying AI will create a dedicated function to map AI total cost to value or profit. By 2028, 60% of Global 500 companies will embed AI FinOps control at inference, shifting cost governance from after-the-fact reporting to real-time oversight.

Taken together, those forecasts suggest companies will treat AI spending less as a broad innovation line item and more as a measurable operating expense. Boards and finance teams have increasingly pressed technology leaders to show clearer links between AI use and financial return.

Workforce and software

In the workplace, 80% of front-line workers employed by international companies will be assisted by physical AI systems by 2030. Gartner includes robots, drones, autonomous vehicles and other embodied systems in that category.

The forecast suggests AI adoption may become more visible in industrial, logistics and field settings, not just in software tools used by office staff. It also raises questions about safety, oversight and workforce training as machines take on a more direct role in day-to-day operations.

Gartner also forecast that by 2029, 80% of new applications will be intentionally disposable and used for less than one year. Easier AI-assisted software creation will lead employees to build short-term applications for immediate business needs.

That trend could reshape the software lifecycle inside large organisations. Temporary applications may serve narrow purposes, but they could still create governance, data handling, compliance and records retention problems if they influence decisions or access sensitive information.

Energy and accountability

Beyond software and labour, Gartner predicted a growing role for companies in electricity markets. It said USD $10 trillion in enterprise-owned energy will make Global 2000 firms unexpected power providers by 2030, with businesses selling electricity to grids and AI data centres.

The forecast reflects the pressure AI infrastructure is expected to place on power supply. If companies build or acquire more generation, storage and energy management assets, some may shift from being heavy electricity consumers to active participants in supply markets.

On governance, insurers rather than regulators will drive AI governance by 2030 as underwriting standards for AI liability insurance become stricter. By 2030, 80% of the Global 500 will also contractually make their CIO or CAIO the "Evidence Custodian" for AI accountability.

Those predictions suggest responsibility for AI behaviour may increasingly be shaped as much by financial and contractual pressure as by formal regulation. Technology leaders may be expected to retain evidence of how AI systems operate and what decisions they influence.

Gartner also forecast that by 2029, 25% of Global 500 companies will continuously innovate componentised AI-powered offerings, undermining fast-follower strategies. That view suggests some incumbents may need to rethink product development cycles if AI-native rivals can adjust offerings more quickly and more often.

Across the forecasts, the common thread is that AI adoption is likely to bring administrative, financial and organisational consequences that extend well beyond the technology function. The predictions place as much emphasis on accountability, energy, cost and institutional strain as on the software itself.