The Shift to Generative AI: Challenges and Strategies for Businesses

Here's what it takes to make the shift to GenAI

Businesses must consider key factors before fully exploiting Generative AI's potential Generative AI holds immense promise for enterprises. And democratization of AI tools, which is now possible via GenAI, is a paradigm shift. But before businesses can fully exploit GenAI's potential and spur widespread adoption, they must consider a few important factors. Enterprises, which today are at the very early stages of their GenAI journeys, must work in new ways and design GenAI applications with enterprise features - including security and compliance, explainability, traceability and lineage, scalability and reliability. Those that don't will find it hard to get the control they need later. Here's how to ensure that your GenAI efforts are enterprise-ready from the start. Shooting and scoring with GenAI is a team sport. IT professionals definitely need to be on the field of play, but they are just part of a larger team that needs to contribute to GenAI efforts. Business leaders should kick off GenAI efforts by defining the problems they're working to solve. Data scientists need to assist by addressing the critical issues related to data. And IT must keep the momentum going by implementing and maintaining the technology to function correctly. If you don't use high-quality data in your GenAI effort, you won't deliver the expected results. And if you get unexpected results, you'll want to be able to answer how you got that outcome. That's why explainability, traceability and lineage are vital. It's critical to ensure the data that's underlying 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 companies to protect the personally identifiable information (PII) of their customers. Ensure that your organization has the necessary data security processes and technology to comply with these rules and safeguard private data. Take steps to prevent your company's confidential information from being exposed and shared by mistake. One way to ensure cybersecurity is by creating company awareness and policies on what is and is not acceptable. Copyright is not something most businesses spend a lot of time thinking about. That needs to change with the rise of GenAI. An example from my own company helps illustrate one area in which you may want to address copyright concerns. Our company has embraced Microsoft Copilot to transform coding. We take extra precautions to ensure that we don't accidentally include copyrighted code in our code. We do that by using Microsoft and our own tools. Also, computing costs associated with GenAI can be extraordinarily high. CNBC recently reported that estimates suggest that Microsoft's Bing AI chatbot, which is powered by ChatGPT, requires at least $4 billion of IT infrastructure to serve responses to all Bing users. And Gartner says that by 2025, growth in 90% of enterprise deployments of GenAI will slow as costs exceed value. Use GenAI in a sensible manner - by leveraging it for the use cases for which it is the only or the absolute best choice - or it might become cost-prohibitive for you in the long run. GenAI can go a long way in increasing efficiency, managing complexity at scale, providing recommendations and enabling company and product differentiation in various ways. Consider how GenAI could transform data centers, for example. A single data center typically runs thousands of applications and requires teams of people to manage hardware and software and monitor systems to ensure that everything is running smoothly. Keeping data centers up and 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 these applications and infrastructure components will potentially come with GenAI agents that will constantly be monitoring for events and issues in the background. These GenAI agents also will be able to talk to one other, so they can work as a team. Because of their infinite knowledge and capacity, they'll be able to see and allow people and systems to act on issues - such as the need to do load balancing, for example - before they become problems. However, when GenAI makes recommendations, you don't just want to hand over the reins to GenAI to implement those recommendations. You need a human in the loop to validate and perfect it. You may also want to use retrieval augmented generation (RAG) to avoid hallucinations and provide very specific information that is 100% sound. Providing very relevant information will improve the accuracy of your results and minimize your risk. Once you streamline your data pipelines and perfect 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 go fully autonomous in future. This is just one use case for which GenAI can drive efficiency, better customer experiences and desired business outcomes. There are myriad other use cases where you can use GenAI to spot and resolve problems before they grow, and you can become more proactive, and that fast action can be a point of differentiation for your business. At this very early phase of GenAI, there's a lot to consider and be discovered. But it's clear that business leaders, data scientists and IT teams need to work together on GenAI - and think and act in new ways to contain cost and risk and get the greatest value from GenAI. The shift to GenAI has begun. Start now to ensure your GenAI strategy is enterprise-ready. We list the best Large Language Models (LLMs).

Here's what it takes to bake the shift to GenAI

Businesses must consider key factors before fully exploiting Generative AI's potential Generative AI holds immense promise for enterprises. And democratization of AI tools, which is now possible via GenAI, is a paradigm shift. But before businesses can fully exploit GenAI's potential and spur widespread adoption, they must consider a few important factors. Enterprises, which today are at the very early stages of their GenAI journeys, must work in new ways and design GenAI applications with enterprise features - including security and compliance, explainability, traceability and lineage, scalability and reliability. Those that don't will find it hard to get the control they need later. Here's how to ensure that your GenAI efforts are enterprise-ready from the start. Shooting and scoring with GenAI is a team sport. IT professionals definitely need to be on the field of play, but they are just part of a larger team that needs to contribute to GenAI efforts. Business leaders should kick off GenAI efforts by defining the problems they're working to solve. Data scientists need to assist by addressing the critical issues related to data. And IT must keep the momentum going by implementing and maintaining the technology to function correctly. If you don't use high-quality data in your GenAI effort, you won't deliver the expected results. And if you get unexpected results, you'll want to be able to answer how you got that outcome. That's why explainability, traceability and lineage are vital. It's critical to ensure the data that's underlying 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 companies to protect the personally identifiable information (PII) of their customers. Ensure that your organization has the necessary data security processes and technology to comply with these rules and safeguard private data. Take steps to prevent your company's confidential information from being exposed and shared by mistake. One way to ensure cybersecurity is by creating company awareness and policies on what is and is not acceptable. Copyright is not something most businesses spend a lot of time thinking about. That needs to change with the rise of GenAI. An example from my own company helps illustrate one area in which you may want to address copyright concerns. Our company has embraced Microsoft Copilot to transform coding. We take extra precautions to ensure that we don't accidentally include copyrighted code in our code. We do that by using Microsoft and our own tools. Also, computing costs associated with GenAI can be extraordinarily high. CNBC recently reported that estimates suggest that Microsoft's Bing AI chatbot, which is powered by ChatGPT, requires at least $4 billion of IT infrastructure to serve responses to all Bing users. And Gartner says that by 2025, growth in 90% of enterprise deployments of GenAI will slow as costs exceed value. Use GenAI in a sensible manner - by leveraging it for the use cases for which it is the only or the absolute best choice - or it might become cost-prohibitive for you in the long run. GenAI can go a long way in increasing efficiency, managing complexity at scale, providing recommendations and enabling company and product differentiation in various ways. Consider how GenAI could transform data centers, for example. A single data center typically runs thousands of applications and requires teams of people to manage hardware and software and monitor systems to ensure that everything is running smoothly. Keeping data centers up and 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 these applications and infrastructure components will potentially come with GenAI agents that will constantly be monitoring for events and issues in the background. These GenAI agents also will be able to talk to one other, so they can work as a team. Because of their infinite knowledge and capacity, they'll be able to see and allow people and systems to act on issues - such as the need to do load balancing, for example - before they become problems. However, when GenAI makes recommendations, you don't just want to hand over the reins to GenAI to implement those recommendations. You need a human in the loop to validate and perfect it. You may also want to use retrieval augmented generation (RAG) to avoid hallucinations and provide very specific information that is 100% sound. Providing very relevant information will improve the accuracy of your results and minimize your risk. Once you streamline your data pipelines and perfect 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 go fully autonomous in future. This is just one use case for which GenAI can drive efficiency, better customer experiences and desired business outcomes. There are myriad other use cases where you can use GenAI to spot and resolve problems before they grow, and you can become more proactive, and that fast action can be a point of differentiation for your business. At this very early phase of GenAI, there's a lot to consider and be discovered. But it's clear that business leaders, data scientists and IT teams need to work together on GenAI - and think and act in new ways to contain cost and risk and get the greatest value from GenAI. The shift to GenAI has begun. Start now to ensure your GenAI strategy is enterprise-ready. We list the best Large Language Models (LLMs).