Why Businesses Keep Failing to Make the Most of AI
According to a PricewaterhouseCoopers study, 20 percent of executives plan to incorporate AI across their enterprises in 2019. Over the past year, countless organizations and Fortune 500 companies have boasted about their AI strategies. When it came time to put those strategies into practice, however, they realized that what they called a “strategy” was little more than tools without guidance.
Businesses today have the resources, knowledge and incentive to create effective strategies behind their AI implementations. Despite these capabilities, few companies take the time to do so. They acquire the physical tools to practice AI, but they often fail to put the same effort into learning why it’s valuable and what challenges AI poses.
By purchasing new technology before designing a strategy to make the most of it, companies attempting to get ahead of their industries ironically set themselves back. To correct this misguided approach, businesses must design real, actionable strategies before they let AI take the wheel.
A driverless car without a motor.
Imagine a company in the 1980s that saw the IT revolution coming but decided to build an IT strategy purely on mainframes. Even if that company’s leaders had the right general idea, the flawed execution would not have helped the business grow.
The same thing is happening today in AI. Companies need both the tools and the wisdom to use them properly. Leaders who want to stop relying on tech vendors have the good of their organizations in mind, but a lack of strategy means their initiatives amount to purchase orders.
Without consideration for use cases and applications, businesses that think AI will fix their problems risk burning out on some incredibly promising tools. To avoid that fate and design a strategy that gets the most out of the AI revolution, keep these three concepts in mind:
1. The application must fulfill a specific need.
A company’s infrastructure layer determines how AI technologies integrate with existing systems. The application layer determines how those technologies benefit your business.
IBM’s Watson is a powerful machine, but Watson itself is an infrastructure tool. Watson’s various domain arms (financial, healthcare, etc.) represent the applications of the AI. In the banking world, Watson’s robotic intellect helps bankers sniff out false positives in money laundering, reducing customer service times in the process. That’s a specific use case — a perfect example of strategic application.
Successful AI strategies tend to be niche-specific. Rather than seek AI empowerment throughout your company, identify a few key areas that could benefit from AI tools before finding the tools that fit those needs. Ensure your infrastructure can handle the integrations, filling in any gaps of your application layer.
2. The organization must understand microservices.
Think about how AI innovation works across different layers within your company. In the infrastructure layer, containerization (also known as modularity or microservices) helps companies implement tools in specific ways without needing to adopt an entirely new infrastructure.
IBM’s Open Banking Platform acts as a plug-and-play option for existing financial institutions to integrate microservices into their operations. Such a solution lets participating banks leverage microservices as cloud APIs to nurture fintech collaboration, streamline processes and build new revenue streams.
AI does not operate like other tech tools. Don’t look at the existing system and say, “Any AI that comes in must be able to work with this system.” Instead, look to the market with a system-agnostic approach. Find opportunities for new tools to come in and fix specific problems within your organization.
3. The tools must be real AI, not data scientists.
Data is everywhere in today’s market. Fifty-seven percent of respondents to a MicroStrategy study say they streamline their decision-making via data. Real AI uses data, but it doesn’t need a trial period and access to your company’s databases to prove its worth.
If a vendor comes forward and asks for access to data and a week (or a month) to generate insights based on that information, that vendor is not a true AI vendor. In reality, groups like these are just data consultants doing professional services work.
Good AI vendors empower their clients with general infrastructure and niche applications. They don’t care where the data comes from. Quality vendors should have no problem normalizing, unifying and using data to deliver actionable information.
Just because a company uses AI does not mean it benefits from those advanced tools. True AI empowerment arises from an action-specific strategy rather than the purchase of a tool that claims to do it all. Consider the long-term, real-world ramifications of AI investments before making the leap.