Generative AI is a very promising tool for businesses, and the democratization of AI tools, now possible thanks to GenAI, is a paradigm shift. But before businesses can fully realize the potential of GenAI and encourage its widespread adoption, they need to consider a few important factors.
Enterprises, now in the early stages of their GenAI journey, must work in new ways and design GenAI applications with enterprise-grade features including security and compliance, explainability, traceability and lineage, scalability, and reliability.
Those who don't will find it difficult to gain the control they need later.
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 about IT
Shooting and scoring goals with GenAI is a team sport. IT professionals certainly need to be on the playing field, but they are just part of a larger team that needs to contribute to GenAI’s efforts.
Business leaders should begin GenAI efforts by defining the problems they are trying to solve.
Data scientists must help address critical data-related issues, and IT must keep the momentum going by implementing and maintaining the technology to keep it running smoothly.
Remember that data is the lifeblood of GenAI
If you don’t use high-quality data in your GenAI work, you won’t get the results you expected. And if you get unexpected results, you’ll want to be able to answer how you got that result.
That’s why explainability, traceability, and lineage are critical. It’s critical to ensure that the data underpinning your GenAI efforts is trustworthy, clean, and that you know where it came from.
Be sure to consider and address data security, copyright, and cost in your GenAI efforts as well.
The European Union’s General Data Protection Regulation (GDPR) and other privacy laws require businesses to protect their customers’ personally identifiable information (PII). Make sure your organization has the data security processes and technology in place to comply with these regulations and safeguard private data. Take steps to prevent your company’s sensitive information from being exposed and shared by mistake. One way to ensure cybersecurity is to raise awareness and create company policies about what is and is not acceptable.
Copyright is not something most companies spend much time thinking about. That needs to change with the rise of GenAI. An example from my own company helps illustrate one area where you may want to address copyright concerns. Our company has adopted Microsoft Copilot to transform coding. We take extra precautions to ensure we don’t accidentally include copyrighted code in our code. To do this, we use Microsoft and our own tools.
Additionally, the computing costs associated with GenAI can be extraordinarily high. CNBC recently reported that estimates suggest Microsoft’s Bing AI chatbot, which is powered by ChatGPT, requires at least $4 billion of IT infrastructure to serve up answers to all Bing users. And Gartner says that by 2025, growth in 90% of enterprise GenAI deployments will slow as costs outstrip value. Use GenAI wisely, leveraging it for use cases for which it is the only or best option, or it could become prohibitively expensive for you in the long run.
Keeping humans informed to validate and refine GenAI
GenAI can go a long way in increasing efficiency, managing complexity at scale, providing 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 the hardware and software and monitor the systems to ensure everything runs smoothly. Keeping data centers running is crucial 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 will potentially come with GenAI agents that will constantly monitor events and issues in the background. These GenAI agents will also be able to communicate with each other, so they can work as a team. Because of their infinite knowledge and capability, they will be able to see and enable people and systems to act on issues (like the need for load balancing, for example) before they become problems. However, when GenAI makes recommendations, you don’t just want to hand the reins to it to implement them. You need a human in the loop to validate and refine them.
You can also use Recall Augmented Generation (RAG) to avoid hallucinations and provide highly specific information that is 100% reliable. Providing highly relevant information will improve the accuracy of the results and minimize risk. Once you have optimized your data pipelines and refined 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 could become completely autonomous in the future. This is just one use case where GenAI can drive efficiency, improve customer experiences, and desired business outcomes. There are many other use cases where you can use GenAI to detect and resolve issues before they escalate, and you can become more proactive — and that quick action can be a point of differentiation for your business.
In this early phase of GenAI, there is much to consider and discover, 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 contain costs and risks and get the most value from GenAI.
The shift to GenAI has already begun. Get started now to ensure your GenAI strategy is enterprise-ready.
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