Is All This AI Hype Worth It? Here's What Leaders Can Do to Capitalize on AI Businesses fully understand what AI can offer, but to ensure a successful AI initiative requires their leaders to treat AI as a pillar of their entire organizational structure.
By Ariel Shapira •
Key Takeaways
- There are serious obstacles to launching an AI initiative.
- Without aligning AI strategy with your overall business model and desired outcomes, any project would start on the wrong foot.
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Some 63% of respondents at organizations using AI said they expect to see their investment in the technology rise over the next three years, according to a 2022 report on the state of AI. That stat is especially noteworthy considering the report's release last December predated OpenAI's global launch of ChatGPT. At the time, 52% of organizations spent 5% or more of their digital budgets on AI.
While most CEOs and CTOs understand AI can boost productivity, simply deploying an AI tool doesn't guarantee greater efficiency or that the customer experience will be enhanced—and it certainly doesn't automatically translate into a fatter bottom line.
But amid the heightened excitement and intrigue — plus a fair amount of FOMO — businesses and organizations across all industries will undoubtedly begin their AI transformations. Or, in many cases, they'll tack on additional miles to an ongoing AI journey. The problem here is an AI inequality gap that has been manifesting for several years, as the McKinsey report highlights.
As with any economic phenomenon, the losers tend to outnumber the winners. An estimated 8% of these organizations show an inflated bottom-line impact due to AI adoption—represented by a 20% growth in EBIT (earnings before interest and taxes).
There's certainly a middle class of businesses leveraging AI to the effect of modest growth. Still, when reviewing results from a recent Altair survey, it is clear a sizable percentage of organizations' AI projects simply fail to produce results. In the past two years, one in four respondents reported that more than 50% of their AI projects failed, 42% admitted to a failed AI experience within the last two years, and 33% claimed more than half of their data science projects never made it to production in the last two years.
Let's be clear: These numbers don't discredit AI technologies or use cases. Instead, they point to serious obstacles that make launching an AI initiative difficult.
What can organizations with successful AI projects teach us, then? First off, these organizations typically adhere to a consistent set of core practices. At the center of these practices is something that seems obvious from the outside looking in but is often overlooked by organizations underestimating the amount of attention required to leverage most AI tools successfully. And this is integrating AI into the overall business strategy.
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Without aligning AI strategy with the overall business model and desired outcomes, any project would start on the wrong foot. AI isn't just something you can plug into your existing infrastructure and expect immediate results. Critical decision-makers must deeply understand everything from long-term roadmaps to every aspect of their digital ecosystems.
As such, all organizations must devise a strategic plan on how and why they plan to leverage AI. This includes assessing the structural changes they must make in their digital ecosystem and business model. If this task seems overwhelming, organizations can turn to third-party consultancies or agencies to guide them. Keenfolks, for instance, helps Fortune 500 companies strategically integrate various AI tools, enabling them to create their own data sets, algorithms and proprietary technology.
While these types of consultancies help businesses make more intelligent decisions or streamline the integration process, organizations can also boost their chances of success by identifying any readiness gaps. These typically relate to the lack of a comprehensive data strategy, which could range from the lack of data scientists to poor data quality or an ineffective data collection system.
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Organizations lacking data scientists typically pick the wrong algorithms and solutions and struggle to deploy models effectively. Bad data or poor collection methods stifle AI models' performance, wasting valuable time and resources and discouraging future AI ventures.
Addressing these gaps requires a data strategy that understands the type of data needed for AI projects and establishes mechanisms to collect the most relevant data. Additionally, it's crucial that the data is clean to ensure accuracy, integrated from various sources, and that the organization establishes clear policies and protocols that prioritize security and privacy.
Addressing this requires adding more experienced personnel with AI expertise and upgrading its data infrastructure, including processing power and cloud-computing capabilities.
Businesses fully understand what AI can offer, but to ensure a successful AI initiative requires their leaders to treat AI as a pillar of their entire organizational structure. Understanding AI's challenges and developing a strategic plan of action that considers the whole company's assets is a good starting point.
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