Discovering a third of UK businesses struggle with the competence to execute their AI plans, according to recent research.
In a recent report titled "Turning insight into impact: Empowering people with AI," MHR, a provider of HR, payroll, and finance software, has shed light on the current state of AI adoption among UK businesses.
According to the report, AI is being increasingly utilised by businesses for various purposes. Sixty-two percent of businesses are employing AI for financial forecasting, 60% for supporting day-to-day processes via co-pilots, 58% for improving workflow automation, and 52% for content creation, including job descriptions and employee learning courses. However, the report also highlights a growing divide between AI ambition and readiness among UK businesses.
Effective use of AI and automation has the potential to save time, reduce errors, and boost productivity. Yet, more than one in three respondents (35%) say human error in data entry is still an issue. This underscores the need for reskilling employees, involving them in shaping AI's role, and fostering a culture that embraces change.
Anton Roe, CEO of MHR, states that without the right skills and employee engagement, AI strategies may stall. Only 38% of organizations involve employees in shaping AI use, which is a concern considering that successful AI implementation requires a bottom-up approach.
The report reveals a significant skills gap among UK businesses preparing to adopt artificial intelligence. One in three UK business leaders admit they lack the skills to implement AI effectively. Despite this, 91% of UK business leaders say they are ready to embrace AI.
Legacy systems, fragmented processes, and human error are some of the barriers to effective AI adoption. AI tools risk being underused if there is no clear, inclusive strategy. Managing AI adoption with care is the only way to move beyond pilot mode and make AI stick.
However, the report also emphasises the importance of addressing the ethical and cultural challenges in implementing AI. Ethical concerns are now a barrier to AI adoption for 35% of businesses, surpassing financial concerns (27%).
The main cultural and ethical challenges in implementing AI include algorithmic bias and discrimination arising from non-representative data and lack of diversity in development teams, lack of transparency and accountability (black-box models), privacy violations, potential amplification of social inequalities, undermining democratic institutions, and environmental impacts.
To address these challenges and enable successful AI adoption, businesses can build diverse and inclusive development teams, use representative, high-quality training data, implement transparent AI governance frameworks and ethical impact assessments, develop community-informed ethical frameworks or dialogue-based approaches, design clear user feedback and problem-resolution mechanisms, balance innovation speed with responsible deployment, and consider environmental impacts.
A minority of organizations consider ethical guidelines unnecessary for AI implementation. AI should enhance human skills, not bypass them, by bringing employees into the conversation early. By addressing these challenges and fostering a culture of inclusivity and transparency, businesses can build user trust, mitigate legal and reputational risks, and reap the full benefits of AI.
[1] Bias in AI: Strategies for Fair and Inclusive Machine Learning, MIT Press, 2021. [2] Ethical AI: Designing Trustworthy Autonomous Systems, Cambridge University Press, 2020. [3] Fairness, Accountability, and Transparency in AI, Synthesis Lectures on Artificial Intelligence and Machine Learning, 2020. [4] Artificial Intelligence: A Guide for Policymakers, OECD, 2019. [5] The Carbon Footprint of AI, Nature Sustainability, 2019.
Technology plays a pivotal role in the report's findings, with artificial-intelligence being widely adopted by UK businesses for various purposes such as financial forecasting, workflow automation, and content creation. Yet, the report also cautions that AI strategies may stall without the right skills and employee engagement, highlighting a prevalent skills gap among businesses preparing to implement AI.
