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I was recently talking to a friend of mine who is a CTO at a mid-sized company, and I was struck by the sudden change in his thinking about AI. My friend, who was initially skeptical, now believes that artificial intelligence (AI) will revolutionize the industry. But his biggest challenge will be convincing other executives to adopt his AI roadmap. Scenarios like this are not uncommon.
Over the past year, the hype cycle around AI has been winding down, leading many leaders to question whether their investments in AI will truly yield proportionate returns. These concerns are not unfounded. Venture capital firm Sequoia Capital recently estimated that even though the AI industry spent $50 billion on his NVIDIA chips last year to train AI models, revenue remained at just $3 billion. did.
Despite this disparity in investment amounts, Sequoia went on to compare AI's impact on business to that of cloud migration, hypothesizing that it is likely the “single greatest value creation opportunity” humanity has ever known. But unlike the cloud, which was replaced by software, AI has the potential to be replaced by services, which the VC firm estimates have an addressable market in the trillions of dollars. This is why tech giants like Microsoft and Amazon continue to double down on their investments in AI.
Related: What is Artificial Intelligence (AI)? Its Benefits, Applications, and More
With so many conflicting opinions about the future of AI, it's no wonder businesses can't agree on the best approach to integrating AI into their organizations. The problem is that most leaders view their AI as having limited capabilities as software and tools, rather than the ability to perform human-like functions. Here are three common mistakes companies make when it comes to implementing an AI roadmap.
Underestimating and limiting the potential of AI
Although AI is widely seen as a tool or software, it has creative and reasoning abilities that allow it to interact with human-like abilities. Just like junior employees who get better at their jobs as they gain experience, AI has the ability to learn from interactions and refine its methods to improve outcomes and take on more work during overtime.
Therefore, leaders who have thought about leveraging AI as a “smart person” rather than software are in a position to realize AI's full potential. Think about the organizational chart of your company. By writing out the skills and tasks associated with each employee, you can begin to visualize where AI can be trained to augment or automate these tasks.
According to a recent AI Index report by Stanford University, AI has already surpassed humans in areas such as image classification, visual reasoning, and even English comprehension. According to the report, as of 2023, AI will exceed human-level performance in several benchmark tasks, helping workers be more productive and do higher-quality work. Another study from the University of Arkansas showed that AI outperformed humans on a standardized test of creativity.
However, unlike humans, AI easily scales up as business demands increase and handles workloads without the physical and mental limitations of humans. Embracing AI in this way means rethinking team structures and workflows, including training your teams to work with AI to augment their roles and drive innovation.
This shift in perspective is crucial because it allows leaders who are not used to adopting technology themselves to inherently understand how to get the most out of AI across their organizations.
2. Trying to copy other companies’ AI use cases
The more we start thinking about AI as smart people, the more we realize how individualized each organization's approach to building an AI roadmap should be. I like to think of AI implementation as onboarding new team members who need to fit into a company's unique dynamics.
Using human resources as an example, a company may have 10 employees. There are only three others, even though they are the same size. This difference is not just a matter of company size or revenue. It's about how these companies have evolved.
Each business has its own structure, culture and needs, and PwC reports that to realize the full potential of generative AI, companies must embrace AI's ability to be customized to the company's specific needs and avoid use-case traps.
Of course, there are common use cases for AI. This is especially true when it comes to improving customer service and sales. But if you're looking to integrate AI more deeply into your company's operations, your approach should be custom-built rather than copy-pasted from external case studies.
Related: I tested an AI tool so you don't have to. I'll tell you what went well and what didn't.
3. Buying an off-the-shelf product – not tailoring the AI solution to your needs
There are some great off-the-shelf AI products that solve specific problems within enterprises, such as ChatGPT, Dalle, and translation tools. The challenge with investing in boxed solutions for AI is that many leaders don't understand how AI can enhance their operations at the system level.
The true power of AI lies in its ability to fundamentally transform operations, not just perform discrete tasks. PwC's 2024 AI Predictions report states that many companies will see compelling ROI from generative AI. Yet few will succeed in achieving transformational value from it. The biggest barrier is leaders' inability to think beyond boxed solutions and rethink how they work with AI.
When building an AI roadmap, leaders must first thoroughly evaluate their own processes: identify areas of overlap, recognize outsourced tasks that can be automated, and pinpoint where the company is heavily investing in talent. Understanding these trends allows leaders to tailor AI solutions to their needs and transform the way they work.
The more I talk to corporate leaders about integrating AI into their businesses, the more it becomes clear that we as leaders need to change our perspective. By viewing AI not just as a technology upgrade, but as recruiting talent, you can better integrate AI into your internal operations, enhancing performance and human ingenuity in the process.
