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Generative AI holds enormous promise for enterprises. And the democratization of AI tools, now possible through GenAI, is a paradigm shift. But before enterprises can fully realize the potential of GenAI and drive widespread adoption, they must consider a few key factors.
Enterprises, which are still in the early stages of their GenAI journey, must work in new ways and design GenAI applications with enterprise features including security and compliance, explainability, traceability and lineage, scalability, and reliability.
Those who don’t will find it difficult later on to gain the control they need.
Here’s how to ensure your GenAI efforts are enterprise-ready from the start.
Senior vice president, head of engineering at Hitachi Vantara.
Understand that GenAI is not just an IT thing
Shooting and scoring with GenAI is a team sport. IT professionals definitely need to be on the field, but they are just one part of a larger team that needs to contribute to the GenAI efforts.
Business leaders should start GenAI efforts by defining the problems they want to solve.
Data scientists need to help by addressing the critical issues related to data. And IT needs to keep the momentum going by implementing and maintaining the technology to function properly.
Remember, data is the lifeblood of GenAI
If you don’t use high-quality data in your GenAI effort, you won’t get the results you expect. And if you do get unexpected results, you want to be able to answer how you got there.
That’s why explainability, traceability, and lineage are vital. It’s crucial to ensure that the data underpinning your GenAI efforts is trustworthy, clean, and that you know where it came from.
Make sure you also consider data security, copyright, and costs in your GenAI efforts.
The European Union’s General Data Protection Regulation (GDPR) and other privacy laws require companies to protect their customers’ personally identifiable information (PII). Ensure your organization has the necessary data security processes and technology in place to comply with these regulations and protect private data. Take steps to prevent your company’s confidential information from being accidentally exposed and shared. One way to ensure cybersecurity is to create corporate awareness and policies about what is and isn’t acceptable.
Copyright is not something most companies spend a lot of time on. That needs to change with the rise of GenAI. An example from my own company illustrates an area where you might want to address copyright issues. Our company has embraced Microsoft Copilot to transform coding. We take extra precautions to ensure that we do not inadvertently include copyrighted code in our code. We do this by using Microsoft and our own tools.
The computing costs associated with GenAI can also be prohibitively expensive. CNBC recently reported that estimates suggest Microsoft’s Bing AI chatbot, powered by ChatGPT, will require at least $4 billion in IT infrastructure to provide answers to all Bing users. And Gartner says that by 2025, growth in 90% of enterprise deployments of GenAI will slow because costs outweigh value. Use GenAI wisely—by leveraging it for the use cases for which it’s the only or absolute best choice—or it could prove too expensive for you in the long run.
Keep people informed to validate and perfect GenAI
GenAI can go a long way in increasing efficiency, managing complexity at scale, making recommendations, and enabling business and product differentiation in a variety of ways.
For example, consider how GenAI could transform data centers. A single data center typically runs thousands of applications and requires teams of people to manage hardware and software and monitor systems to ensure everything runs smoothly. Keeping data centers up and running is critical because these infrastructure hubs run mission-critical applications for financial services, government, and a wide range of other types of businesses and organizations.
But in the future, all of these applications and infrastructure components might come with GenAI agents constantly monitoring for events and issues in the background. These GenAI agents can also talk to each other, so they can work as a team. Because of their infinite knowledge and capacity, they can see issues and enable people and systems to take action, such as the need to do load balancing, before they become issues. However, when GenAI makes recommendations, you don’t want to just hand over the reins to GenAI to implement those recommendations. You need a human to validate and perfect it.
You can also use retrieval augmented generation (RAG) to avoid hallucinations and provide highly specific information that is 100% reliable. Providing highly relevant information improves the accuracy of your results and minimizes your risk. Once you have streamlined your data pipelines and perfected your work to achieve the expected results, you may want to take the next step and automate the action as well.
It won’t happen overnight, but data centers can become fully autonomous in the future. This is just one use case where GenAI can drive efficiency, better customer experiences, and desired business outcomes. There are countless other use cases where you can use GenAI to spot and fix problems before they become bigger, and be more proactive, and that rapid action can be a differentiator for your business.
At this very early stage of GenAI, there is much to consider and explore. But it is clear that business leaders, data scientists, and IT teams must work together on GenAI – and think and act in new ways to manage costs and risks and extract the greatest value from GenAI.
The transition to GenAI has begun. Start now to ensure your GenAI strategy is enterprise-ready.
We provide an overview of the best Large Language Models (LLMs).
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