AI adoption: why the slowdown is structural

AI adoption slowdown has less to do with technology than with the conditions around it. A study by the consultancy Algomarketing, based on more than 300 global marketing leaders in companies with over 10,000 employees, found that 51.3% name the cost of the investment as the main obstacle to adopting AI and automation in marketing operations. The finding is significant because it comes from organisations with the budgets to experiment, which suggests the barrier sits deeper than available cash.

The barriers inside large teams

The same study found wide differences between regions. Only 26.7% of US marketers and 14% of those in Singapore said they had used AI tools in the previous three years, against 54.5% in Australia and 45.3% in the United Kingdom. The Australian and British teams were also the most likely to increase spending and to hire, while US respondents lagged on both counts.

Skills are the second constraint. Around 44.4% of respondents said they struggled to find people with combined marketing and AI skills, and only 23.2% of the large companies surveyed had adequate internal resources to exploit the new tools. More than half, 52%, chose a mixed approach that combines internal staff with external suppliers.

What the tools are used for

The main applications reported were customer segmentation, planning and budget balancing, content creation, outbound marketing, knowledge management, campaign optimisation and lead scoring. The expected benefits follow from those uses: better decisions, risk mitigation, improved return on investment, cost savings and stronger innovation. Where the tools were used, the reported returns were high, with 23.2% seeing a return above 75% and 68% reporting between 50% and 74%.

Ethics as a brake

Data ethics slowed adoption more than expected. Around 77.5% of companies said concerns about bias and fairness had forced them to delay implementation, and 32.7% reported significant delays. That is a revealing result: the obstacle sits in the absence of governance that makes the use of the technology defensible, rather than in its availability.

What small companies can learn

The barriers described by the largest organisations apply at every scale. Cost, skills and governance decide whether a project reaches production. For an SME, the practical lesson is to start from a defined process, use existing data honestly and set up rules for how the system is used and monitored before scaling it. The technology is rarely the bottleneck. The conditions for using it well are.

The slowdown is not a reason to wait, and a digital strategy built for growth is where adoption turns into results.