Artificial intelligence (AI) has been a hot topic in the business world, but many companies are finding it challenging to make AI technologies truly useful. While chipmaker Nvidia is raking in revenue from AI hardware sales, a survey by Sapio Research for Hewlett Packard Enterprise (HPE) reveals that only a third of IT leaders in the UK and Ireland feel fully prepared to harness the benefits of AI.
The HPE survey highlights key areas where businesses are falling short in their AI strategies, such as low data maturity levels, networking and compute deficiencies, and ethics and compliance considerations. Matt Armstrong-Barnes, chief technologist for AI at HPE, stresses the importance of taking a holistic approach to AI implementation to avoid misalignment and fragmentation within organizations.
Another study by KPMG raises concerns about the adoption of generative AI (GenAI), with business leaders citing inaccuracies in results, data errors, information skewing, and cybersecurity issues as major worries. Only 30% of directors have established responsible GenAI guidelines to address these concerns.
It’s important that companies thoughtfully define a clear AI strategy rather than merely chase the next technological innovation. This strategy should balance the value, cost and risk associated with AI use cases
Leanne Allen, KPMG UK
Leanne Allen, head of AI at KPMG UK, emphasizes the need for companies to develop a clear AI strategy that considers the value, cost, and risk of AI use cases to build stakeholder trust and drive progress.
Data quality remains a significant challenge in AI success, with HPE’s research showing low data maturity levels among organizations. This lack of mature data governance models and advanced analytics capabilities can slow down AI model creation and lead to inaccurate insights and poor ROI.
Additionally, a study by Vanson Bourne for Fivetran reveals that most organizations face barriers in AI adoption, with only 40% of IT leaders fully measuring the ROI of their AI programs. Misinformed business decisions based on inaccurate data from AI systems can result in significant revenue loss for businesses.
Despite these challenges, organizations in the early stages of AI adoption are seeing a positive ROI, indicating the financial benefits of AI implementation. Building custom AI models can also lead to higher financial returns compared to using third-party suppliers.
To ensure long-term success with AI, businesses need to take a comprehensive approach across the AI lifecycle to enhance interoperability, identify risks, and capitalize on opportunities, as advised by HPE’s Armstrong-Barnes.
