From defining ROI to preparing data environments, there's a lot to consider before jumping head first into AI.
As someone who's been working closely with enterprises on their AI initiatives, I've seen firsthand the excitement and the challenges that come with adoption.
One of the key lessons I've learned is the importance of collaboration between IT and business leaders. Working together to identify relevant data sources and solve real employee pain points is critical for leaders to co-create AI systems that deliver genuine value.
The Overlooked Keys to Building Successful AI
When it comes to AI in the enterprise, it’s fear that’s driving the frenzy to try to get projects off-the-ground. There's widespread panic among every board director and C-Suite executive that a rival is building a blockbuster Generative AI application. One that is going to help them leapfrog the competition and secure more market share.
As a result, most executives now have a mandate to simply do something with AI – and quickly. But often, it's not clear what that is. So while there's intense urgency to begin, few have any actual idea how to start.
Of course, speed is important. However, putting AI into production for the sake of claiming technology superiority is a fast way to failure. There are critical steps that organizations must take before they’re capable of achieving the outcomes they want with AI.
Below are a few of the most common challenges that our customers face when considering how to get started with AI.
The ROI Paradox
There’s one thing everyone wants from their AI: a return on investment.
The days of throwing real dollars at theoretical tech with no outcome in mind are over. Now, projects have to produce value. It’s the only way to ensure continued investment. But given how new AI is, organizations are struggling to define what value is in the context of the tech.
Businesses should approach planning with an open mind. And when it comes to initial AI investments, the smaller the better. Document summarization is usually enough to wow the executive team and get them energized for more.
Naturally, businesses will want to take on more sophisticated use cases. For these, they should first build a test model to ensure the desired outcomes are even possible. While it takes just a few days to construct these rudimentary systems, they only work in test settings. However, success will give companies the confidence to embark on the hard work of actually putting the model into production.
Data Governs All
The phrase has now become a staple in corporate lexicon: AI is only as good as the data that’s behind it. However, it’s so overused because it’s that important.
Some leaders are still think that AI will suddenly give every employee access to new levels of business intelligence and automation. But rarely are data environments actually ready for Generative AI.
While it might look like magic, AI is anything but. Behind-the-scenes, these models must be continually trained on hyper-relevant data. Often, that information must come from several different endpoints. And tracking down and unifying all that data can be one of the hardest steps in the AI journey. This is especially true for organizations that might be still running 20-year-old software.
Bring in the Business
IT may be in charge of the technical work in bridging a divided data landscape. But they must rely on domain experts to actually identify the relevant information. That will make the AI systems useful for employees.
Often, those closest to the business understand the problems employees are facing. And solving those pain points requires their expertise. They’re also probably more knowledgeable about all the different data sources available.
Enterprises must remove any barriers that separate CIOs from the rest of the executive team. They must also encourage technology and business leaders to work together on developing the data and AI strategy.
AI takes realism to succeed. Companies must understand the gap between where they are and where they want to be. And then, start now to take the steps to close it. While it’s natural to feel competitive pressure to move quickly, speed shouldn’t come at the sacrifice of quality.
The risks that come with AI are very real. But so are the benefits. And taking the time now to build the right foundation can ensure that the former is a rarity, and the latter a guarantee.
