Every industry still worries that AI will replace jobs as well as tasks, all without businesses’ even measuring their AI return.
TL;DR: be clear about where AI is implemented and analysed.
AI is quickly replacing human tasks, for better or worse. The trouble is that, as research shows, marketers do not appear to be measuring the results of the AI tools they have adopted against what went before.
Delving into the survey responses of 184 agency and brand marketing and advertising professionals, it seems that fast AI appetite is not being followed up with a fundamental marketing discipline: analysing its successes and failures.
Here are some headline numbers from Basis’ “AI and the Future of Marketing” report:
- Almost 50% of organisations say they replaced human tasks in the past year, up from 27.9% from the year prior.
- Just over half run AI agents, mainly for reporting and analytics, and 61.4% use AI every day.
- Only 6.5% evaluate their AI investments against defined benchmarks.
- A staggering 65% do not measure AI return, or know if anyone does.
Elsewhere 13% of respondents described their first-party data as extensive and well-structured, which is down from the year earlier.
Also, while this research looked into AI task replacement – not the enduring ‘job replacement’ theory – just over half expect substantial job losses across the industry in the next 3 to 5 years. 70% do see little to no risk, however.
So, what does this mean for fund marketers?
Basis sells ad-tech products (that include AI tools), yet its findings are still applicable to how marketers in many fields, alternative investment included, use AI for operational efficiency.
It also highlights how executives can instill confidence in their firms by being explicit about what AI is used for, where it works, and how a hybrid setup can prove substantial, as follows.
Set baselines before you add automations
For IR teams that draft commentaries, build lists of produce reports using AI agents, you have to define metrics by which you can compare human vs AI outputs. Recording times and costs put into manual work beforehand helps measure what you save by automating tasks.
Measure quality as much as speed
Efficiency is not everything if a quickly-drafted piece of content needs heavy editing and compliance reworking before it goes out. Review cycles, corrections and approval times have to be tracked to see if any hours are saved with GenAI tools.
Data quality should be taken seriously
The reported fall in confidence in first-party data is a clear warning. Staying on top of clean, consented investor data in a CRM remains essential – where duplicates, and missing and stale contacts need to be reviewed first – to determine the future usefulness of AI.
Focus on internal AI work initially
Low-risk tasks that do not reach allocators is where to start implementing agents, such as reporting and analytics which do not require stringent human review and approvals.
Instil a positive AI culture
The fear of job losses to AI has an actual figure here, alongside being a talked-about issue. A small marketing or IR team is better served by clearly stating what tasks are left to AI, and which human skills remain as a crucial, irreplaceable investment.
Measuring AI is more important than implementing it for fund marketers to truly hone its operational advantages, and reduce the AI anxiety that still pervades many industries.
Source
The State of Brand, “Half of Marketing Teams Are Replacing People With AI. Almost Nobody Can Prove It’s Working”
