Overcoming the Top Barriers to GenAI Adoption in the Enterprise

If you're struggling to take advantage of GenAI in your business, your core underlying problem is probably a lack of sufficient data management tools and processes.

That's the short version of the main roadblock enterprises encounter along their GenAI journeys. It is based on my experience working with businesses to define and execute data management strategies to power GenAI. Keep reading for the longer version as I unpack why implementing enterprise GenAI can be so challenging and what businesses must do to take full advantage of GenAI.

How enterprises aim to use GenAI

It's no secret that enterprises of all types are eager to take advantage of generative AI technology. As of late 2023, 66% of organizations surveyed by IDC reported exploring GenAI. Likewise, Gartner found that in the same period, 55% of businesses were piloting GenAI projects or (in limited cases) already had them in production.

Yet, there's a big difference between experimenting with GenAI or implementing it in a handful of areas and taking full advantage of it across the business. It's easy to talk about GenAI or identify potential use cases; putting them into practice is the real challenge.

Roadblocks on the enterprise GenAI journey

There are many reasons why fully embracing GenAI in the enterprise can prove so challenging. Here's a look at the top issues I've witnessed in my work helping businesses establish the data foundation for successful GenAI initiatives.

Not knowing which use cases to target

GenAI can potentially do many different things—from helping employees draft emails or reports to powering chatbots that engage with customers, documenting products and much more.

However, whether a given business will benefit from a particular GenAI use case—and whether it can successfully implement the use case in the first place—depends on factors that some enterprises struggle to decipher. For example, they may not know whether they have the right data to enable a specific use case. They may also not know which use cases will deliver the greatest ROI.

As a result, enterprises struggle to decide which GenAI use cases to focus on, stunting their GenAI implementation plans.

Uncertainty about which GenAI models to use

There are many GenAI models available today. Some are proprietary, and some are open source. Some are easier to customize than others, and some excel at specific tasks where others come up short. Some require you to share sensitive data with third parties, while others let you keep your data in-house.

Given these choices, it can be challenging to decide which model or models to use and how to use them. For example, you might find yourself weighing the pros and cons of taking a pre-trained open source model and retraining it on your data. This approach gives you more control over how the model works, a greater ability to customize it and the peace of mind of not having to share data with a third-party vendor. However, this strategy also requires more effort than using a model that is available as a service. In addition, if you use an open source model, you must ensure that you have sufficient data (and data of adequate quality) to retrain the model.

On the other hand, perhaps you will see better ROI if you use a proprietary model. These are typically easier to work with because they don't require you to operate the model independently. The drawback is that proprietary models are harder to customize and using them usually requires sending your business's proprietary data to a third-party vendor. There may also be major security and data privacy issues that need to be weighed.

The bottom line here is that evaluating different model options requires a deep understanding of GenAI from both technical and business angles, and it can be very tough to bring together the right types of expertise to provide accurate insights.

Difficulty predicting GenAI ROI

The cost of investing in GenAI technology can vary depending on how you go about it. For instance, developing and training your model typically requires a higher upfront investment than using a third-party model. Still, no matter your approach, investing in GenAI is not cheap.

At the same time, predicting how much money GenAI investments will save by increasing revenue and/or productivity can be challenging because the technology remains so new and it's hard to put figures on the potential ROI. For instance, an enterprise might not know whether the cost of collecting, transforming and managing all of the data it will need to power custom GenAI solutions will be justified by the savings that those solutions produce.

Given this uncertainty, some enterprises hesitate to embrace GenAI fully because they're unsure if the investment will be worth it.

Focusing on tools but not processes

An increasingly large ecosystem of tools is available to help companies manage GenAI and the data that GenAI models rely on. However, tools alone don't move the needle when implementing GenAI strategies. Businesses also need processes to accompany the tools.

For instance, imagine you're an enterprise trying to figure out whether you have the right data types to develop a GenAI chatbot. You could buy a data discovery tool that creates an inventory of your data. That's one step toward building your chatbot, but it won't get you all the way there. You also need processes that allow you to collect the data, manage its quality, perform any relevant transformations and so on before you can feed the data into the GenAI model that powers your chatbot.

My point is that it's easy to focus on tools, but tools alone are not a complete solution for implementing GenAI.

Solving the GenAI data challenge

If you read between the lines, you'll notice a common theme that runs throughout all of the enterprise GenAI challenges I just described: Data management. Without the proper data management tools and processes, enterprises struggle to figure out which GenAI use cases to target because they don't know if they have the right data—and the right data governance and quality management controls—to enable use cases they are considering.

They also can't decide which models to use or how to retrain or customize them. Nor can they accurately predict the cost of investing in GenAI because they don't know how expensive it will be to implement the requisite data management processes.

This is why implementing an effective data platform is the single most important step that enterprises can take to enable GenAI. A data platform means a holistic set of tools that allow businesses to discover, monitor, govern, secure and transform all their data. Building on that foundation, they can enable DataOps and MLOps processes that allow them to collect whichever data they require to support a given GenAI use case, feed the data into their models, and deploy them into production.

To be sure, there are other challenges to overcome as well. For example, enterprises must acquire personnel with the requisite talent to build and train GenAI services and/or integrate with third-party GenAI vendors to deploy the required solutions. They must also invest in infrastructure to power GenAI model training and inference.

However, the above hinges on having the right data management solutions in place. Enterprises too often stumble in this area, and they should focus on it if they want to stop simply talking about GenAI and start using it.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/0802290022