The path to AI isn’t a sprint – it’s a marathon, and businesses need to pace themselves accordingly. Those who run before they have learned to walk will falter, joining the graveyard of businesses who tried to move too quickly to reach some kind of AI finish line. The truth is, there is no finish line. There is no destination at which a business can arrive and say that AI has been sufficiently conquered. According to McKinsey, 2023 was AI’s breakout year, with around 79% of employees saying they’ve had some level of exposure to AI. However, breakout technologies don’t follow linear paths of development; they ebb and flow, rise and fall, until they become part of the fabric of business. Most businesses understand that AI is a marathon and not a sprint, and that’s worth bearing in mind.
Take Gartner’s Hype Cycle for instance. Every new technology that emerges goes through the same series of stages on the hype cycle, with very few exceptions. Those stages are as follows: Innovation Trigger; Peak of Inflated Expectations; Trough of Disillusionment; Slope of Enlightenment, and Plateau of Productivity. In 2023, Gartner placed Generative AI firmly in the second stage: the Peak of Inflated Expectations. This is when hype levels surrounding the technology are at their greatest, and while some businesses are able to capitalize on it early and soar ahead, the vast majority will struggle through the Trough of Disillusionment and might not even make it to the Plateau of Productivity.
All of this is to say that businesses need to tread carefully when it comes to AI deployment. While the initial allure of the technology and its capabilities can be tempting, it’s still very much finding its feet and its limits are still being tested. That doesn’t mean that businesses should steer clear of AI, but they should recognize the importance of setting a sustainable pace, defining clear goals, and meticulously planning their journey. Leadership teams and employees need to be fully brought into the idea, data quality and integrity need to be guaranteed, compliance objectives need to be met – and that’s just the beginning.
By starting small and outlining achievable milestones, businesses can harness AI in a measured and sustainable way, ensuring they move with the technology instead of leaping ahead of it. Here are some of the most common pitfalls we’re seeing in 2024:
Pitfall 1: AI Leadership
It’s a fact: without buy-in from the top, AI initiatives will flounder. While employees might discover generative AI tools for themselves and incorporate them into their daily routines, it exposes companies to issues around data privacy, security, and compliance. Deployment of AI, in any capacity, needs to come from the top, and a lack of interest in AI from the top can be just as dangerous as going in too hard.
Take the health insurance sector in the US for instance. In a recent survey by ActiveOps, it was revealed that 70% of operations leaders believe C-suite executives aren’t interested in AI investment, creating a substantial barrier to innovation. While they can see the benefits, with nearly 8 in 10 agreeing that AI could help to significantly improve operational performance, lack of support from the top is proving a frustrating barrier to progress.
Where AI is being used, organizational buy-in and leadership support is essential. Clear communication channels between leadership and AI project teams should be established. Regular updates, transparent progress reports, and discussions about challenges and opportunities will help keep leadership engaged and informed. When leaders are well-versed in the AI journey and its milestones, they are more likely to provide the ongoing support necessary to navigate through complexities and unforeseen issues.
Pitfall 2: Data Quality and Integrity
Using poor quality data with AI is like putting diesel into a gasoline car. You’ll get poor performance, broken parts, and a costly bill to fix it. AI systems rely on vast amounts of data to learn, adapt, and make accurate predictions. If the data fed into these systems is flawed, incomplete, misclassified or biased, the results will inevitably be unreliable. This not only undermines the effectiveness of AI solutions but can also lead to significant setbacks and mistrust in AI capabilities.
Our research reveals that 90% of operations leaders say too much effort is needed to extract insights from their operational data – too much of it is siloed and fragmented across multiple systems, and riddled with inconsistencies. This is another pitfall businesses face when considering AI – their data is simply not ready.
To address this and improve their data hygiene, businesses must invest in robust data governance frameworks. This includes establishing clear data standards, ensuring data is consistently cleaned and validated, and implementing systems for ongoing data quality monitoring. By creating a single source of truth, organizations can enhance the reliability and accessibility of their data, which will have the added bonus of smoothing the path for AI.
Pitfall 3: AI Literacy
AI is a tool, and tools are only effective when wielded by the right hands. The success of AI initiatives hinges not only on technology but also on the people who use it, and those people are in short supply. According to Salesforce, nearly two-thirds (60%) of IT professionals identified a shortage of AI skills as their number one barrier to AI deployment. That sounds like businesses simply aren’t ready for AI, and they need to start looking to address that skills gap before they start investing in AI technology.
That doesn’t have to mean going on a hiring spree, however. Training programs can be introduced to upskill the current workforce, ensuring they have the capabilities to use AI effectively. Building this kind of AI literacy within the organization involves creating an environment where continuous learning is encouraged – workshops, online courses, and hands-on projects can help demystify AI and make it more accessible to employees at all levels, laying the groundwork for faster deployment and more tangible benefits.
What next?
Successful AI adoption requires more than just investment in technology; it requires a well-paced, strategic approach that secures buy-in from employees and support from leadership. It also requires businesses to be self-aware and alive to the fact that technology has limits – while interest in AI is soaring and adoption is at an all-time high, there’s a good chance that the AI bubble will burst before it course corrects and becomes the steady, reliable tool that businesses need it to be. Remember, we’re now at the Peak of Inflated Expectations, and the Trough of Disillusionment still needs to be weathered. Businesses keen to invest in AI can prepare for the incoming storm by readying their employees, establishing AI usage policies, and ensuring their data is clean, well-organized, and correctly classified and integrated across their business
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