So, let’s start with this – so far, not many are making money on implementing AI. A Gartner study found that the top barriers to AI implementation were demonstrating value (49%) and talent shortage (42%). Additionally, only 1 in 4 companies surveyed by LucidWorks reported successful implementation of a generative AI project. McKinsey’s survey revealed that only 10% of companies are implementing generative AI projects at scale, with just 15% seeing any positive impact on earnings.
Complexity, outdated technology stacks, and data readiness are key factors slowing progress. Companies are advised to focus on a limited data set, reuse what works, and have a centralized approach to AI implementation. While there are challenges to overcome, companies must start with something that works and shows value and build from there.
Accenture conducted a multi-year survey of over 3,000 executives across 19 industries and 10 countries to compile a report on effectively utilizing artificial intelligence (AI). The study aimed to compare how different organizations leverage AI. The report emphasizes the importance of reinvention in unlocking firms’ potential for AI. This involves transforming the entire organization, investing in talent strategy, breaking down silos, embracing new ways of working, and continuously seeking reinvention. By doing so, firms can drive growth and productivity, enhance the user experience, tap into data-driven decision-making, accelerate innovation, manage risk, scale their business, empower employees, and optimize resources.
Contrary to fears of AI-driven job losses, a report by ManpowerGroup reveals that 55% of employers worldwide plan to increase headcount in the next two years due to AI. The energy, utilities, and information technology sectors will most likely expand their workforce due to AI adoption. However, 31% of employers face challenges in AI adoption due to a lack of skills in their workforce.
Why do we care?
Complexity, outdated technology stacks, and data readiness identified as key factors slowing down progress …. Means fixing those is the opportunity. I want to highlight that those are opportunities regardless of AI or not – and it’s why there is opportunity now even without implementing AI based products.
Ensuring data quality, availability, and integration across the organization is critical for successful AI implementation…. Or for a data analytics one.
Implement a centralized approach to AI governance to ensure cohesive efforts and resource allocation. This strategy can help avoid duplication and siloed projects…. And those two statements are still true without AI.
A strategic approach that focuses on small wins, centralized governance, and continuous innovation will be critical in realizing the full benefits of AI – or technology — across industries.