- Experian report reveals that 87% of leaders agree that responsible AI will differentiate them from the competition
- Not even half feel prepared to implement AI responsibly
- Many lack high-quality data to take AI to the next level
Although AI tools have been shown to increase productivity in some cases, the technology is not without concerns, namely job safety, 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 in the next two to three years.
How to implement AI responsibly
Experian divides responsible AI into four core principles: trustworthiness, privacy protection, bias minimization, and risk management.
At the moment, companies are grappling with technical expertise (32%), applying AI to real-world use cases (31%), and balancing the speed of innovation with governance (30%).
Furthermore, only 45% have integrated responsible AI, 10% are lagging behind, and 1% have no focus at all. Just half (48%) believe their teams are adequately prepared to implement responsible AI.
Christine Foster, managing director of AI and automation at Experian UK&I, summarized the key principles of AI: “Laying the right foundations, including high-quality data, as well as clear accountability and tools that support the adoption of AI across its lifecycle.”
Although nine in 10 agree that high-quality data is essential, only 43% are confident in the quality of their data; This is Experian’s second step in its seven principles of responsible AI.
The report advises companies to periodically evaluate AI model performance; minimize potential risks to operations, people and customers; focus on safety; implement explainability tools; ensure privacy by design; and continually check for bias.
Some of the tips include starting small to demonstrate value before scaling, running scenarios and tests in simulation before deploying, and diversifying the teams involved in AI to broaden perspectives and reduce blind spots.
“As AI evolves, especially with the rise of autonomous systems, getting it right will be critical to building trust, enabling better business decisions, and remaining competitive,” concluded Foster.
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