Gartner survey ranks AI strategy miscalculations among top enterprise risks

Corporate anxiety over artificial intelligence has moved beyond technical bugs and into executive decision-making. In its latest quarterly risk report released on 2 November 2026, research firm Gartner found that strategic miscalculations with AI have surged into the top five emerging business dangers for large organisations.

The study surveyed 308 enterprise risk managers, corporate auditors and senior executives. It ranked AI strategy miscalculation as the third most urgent concern facing their organisations, sitting just behind cyber security weaknesses exposed by AI and geopolitical disruptions to energy supply. A related issue, the danger of being displaced by competitors that execute AI initiatives more competently, also entered the top rankings for the first time.

Moving past basic technical worries

Gartner principal analyst Kevin Mercado explained that the primary worry among leadership teams has shifted from technical governance to fundamental strategy. Companies now worry about two opposing extremes: committing large amounts of money based on anticipated gains that never materialise, or waiting too long and falling behind rivals who implement systems effectively.

According to the report, a miscalculation usually stems from rushing into projects before an organisation is culturally, operationally or legally prepared. Leaders often select unsuitable use cases or commit capital before verifying whether their teams have the internal data quality, staffing or processes to support an automated workflow. When pressure to keep up overtakes basic operational readiness, businesses end up spending heavily on software that fails to deliver returns.

To counter this drift, the research firm recommends that organisations audit their project pipelines and measure use cases against clear business goals, actual team capabilities and realistic returns before deployment.

What this means for Australian businesses

For owners and managers across Australia, the findings provide a timely checkpoint against hype-driven investment:

• Focus on specific friction points rather than general adoption. Pick processes with measurable hours lost, such as invoice matching, intake triage or standard customer replies, rather than purchasing platform subscriptions without an assigned operational goal.
• Check data readiness before buying tools. AI automations rely entirely on clear internal records. If customer spreadsheets, accounting entries or job files are messy or scattered, the automation will fail regardless of how modern the software is.
• Start with short trial periods that include exit criteria. Avoid long annual contracts until your team confirms that a new tool saves verifiable hours during daily use.
• Involve staff early to avoid wasted licences. Workflow software only pays for itself when the workers handling the tasks actually adopt it into their routines.