- Experian report finds 87% of executives agree that responsible AI will set them apart from the competition
- Not even half feel they are prepared to implement AI responsibly
- Many lack high-quality data to take AI to the next level
While AI tools have been shown to increase productivity in some cases, the technology is not without its fair share of concerns – namely job security, costs and emissions.
New research from Experian has found that three in four (76%) companies now agree that putting responsible AI into practice is now one of their biggest challenges.
This is despite 89% of UK business leaders recognizing that AI is already improving their performance, and looking ahead, 87% agree that responsible AI will become a key competitive differentiator within the next two to three years.
How to implement AI responsibly
Experian divides responsible AI into four core principles: reliability, privacy protection, bias minimization and risk management.
Currently, companies struggle with technical expertise (32%), applying AI to real-world use cases (31%), and balancing speed of innovation with governance (30%).
Additionally, only 45% have integrated accountable AI, with 10% lagging behind and 1% having no approach at all. Just under half (48%) believe their teams are sufficiently prepared to implement responsible AI.
Experian UK&I’s AI and Automation General Manager, Christine Foster, summarized the key principles for AI: “Laying the right foundations, including high-quality data and clear accountability and tools that support AI adoption across its lifecycle.”
Although nine in 10 agree that high-quality data is essential, only 43% feel confident about their data quality – this is Experian’s second step in its seven principles of responsible AI.
The report advises companies to regularly assess the performance of the AI model; minimize potential risks to operations, people and customers; focus on safety; put explanatory tools in place; ensure privacy by design; and continuously check for bias.
Some of the advice includes starting small to prove value before scaling, running scenarios and tests in simulation before deployment, and diversifying teams involved in AI to broaden perspectives and reduce blind spots.
“As artificial intelligence advances, especially with autonomous systems on the rise, getting this right will be critical to building trust, enabling better business decisions and remaining competitive,” Foster concluded.
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