As we know, everyone’s talking about AI. In that much, it’s yesterday’s news.
If you read our blogs, you’ll be aware that we’ve already discussed the subject more than once, most recently investigating AIs role in telco (which you can read here) and before that its likely future evolution (which you can read here). Now, an independent analyst study by Gartner throws something of a spanner into the works: it turns out that despite its importance, AI is something we’re not – to put it bluntly – very good at! Let’s investigate.
When Gartner recently asked CEOs (you can read more about the study here) which technology would most significantly impact their futures a whopping 83% chose AI (no surprise there). More interestingly, respondants also said that their peers in the C-suite lack AI savvy, defined as proficiency in the use of the technology to create, access, or fully understand and communicate about it. CEOs aren’t alone in that view, with a broader study of all company executives rating only 26% of their peers as confident and proficient in AI, and worrying that inadequate AI strategies could lead to competitive disadvantages.
All of these conclusions lead us to ask an interesting an important question: what are the difficulties involved in implementing an AI strategy? Because while we all see the potential for improved efficiency and better decision-making, etc. from the technology, it’s clear that integrating AI into business operations is complex and fraught with difficulties. Let’s spell out some of the major challenges.
1. Not enough data…
AI systems require vast amounts of high-quality data to function effectively. Not many, and possibly few companies can feed the beast adequately. Many companies struggle with fragmented systems, inconsistent formats, and incomplete data records. Familiarly (to those in IT), legacy systems are often not compatible with modern AI technologies, making data integration a major hurdle. Without clean, well-structured, and relevant data, AI models cannot be trained accurately, leading to unreliable outputs.
2. …and not enough talent
If there’s not enough data to process, another significant challenge is the lack of skilled talent to process it. AI implementation requires a combination of data scientists, machine learning engineers, domain experts, and IT professionals. However, there is a global shortage of the expertise in those domains needed to develop and deploy AI solutions. Hiring and retaining AI professionals can be difficult, especially for smaller firms that may not be able to compete with tech giants on compensation and career opportunities.
3. A lack of enthusiasm at the coal face?
This is proving to be a major barrier. Who among us hasn’t heard discussion of AI potentially leading to job loss, never mind who hasn’t experienced a lack of understanding of how the technology actually works? To be sure, at present cultural resistance often hinders adoption and slows down progress. To successfully implement AI, companies need to foster a culture of innovation and continuous learning, while also providing transparency about how AI will impact roles and responsibilities. This may be easier said than done.
The familiar issue: ethics
Like it or not, ethical and regulatory concerns complicate AI adoption. Questions around privacy, bias, and accountability are at the forefront of public and governmental conversations. AI systems can unintentionally perpetuate or even exacerbate existing biases if not carefully designed and monitored. Companies must ensure compliance with data protection regulations such as the GDPR or CCPA and develop ethical frameworks for AI usage to maintain public trust.
Lastly, there’s the issues we began with – aligning AI initiatives with business goals. Doing that is challenging, and many companies may not be very good at meeting the challenge. AI should not be adopted simply for its novelty; it must solve real business problems and generate value. Companies sometimes invest heavily in AI without a clear understanding of how it fits into their overall strategy, leading to disjointed projects and wasted resources. Effective implementation requires clear objectives, a roadmap for integration, and metrics to measure success.
Discussing AI is to participate in an ongoing conversation, sometimes without clear, immediate answers. We’ll keep returning to the subject in future blogs as the technology evolves.
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