Join top executives in San Francisco on July 11-12 to hear how leaders are integrating and optimizing AI investments for success.. Learn more
Pitchbook predicts that the generative AI market in the enterprise will grow at a CAGR of 32% to reach $98.1 billion by 2026.
I have been a technology entrepreneur for over 25 years. The pace of change in this space has always been incredibly fast. I used to tell people that I was doing surgery in dog years, considering that in one year I would see about seven years of transformation.
The launch of ChatGPT late last year turbocharged that pace of innovation. Generative AI has exploded, and every day major tech players like Microsoft, Google, and Salesforce are releasing competing announcements about how they’re integrating the technology into their platforms.
Since then, I’ve seen so much progress, demand, and promise in generative AI, especially in interactive conversation, that I’ve started measuring the pace in hamster years, which is five times faster than dog years.
Join us in San Francisco July 11-12, where senior leaders will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
Sign up now!
As generative AI continues to develop and evolve, there are four trends I expect to emerge.
1. The focus will shift to training generative AI on enterprise data
Most of the tools that generate headlines work exclusively on public domain data. However, there is a whole world of possibilities that opens up as generative AI is trained on enterprise data. As Nicola Morini Bianzino, Ernst & Young’s CTO, says, it will “change the way information is accessed and consumed within the enterprise.”
This use case for generative AI is urgent because access to institutional knowledge is disappearing. Enterprise data is growing at an explosive rate, but Gartner estimates that more than 80% of that data is unstructured (ie PDFs, videos, slide decks, MP3 files, etc.), making it difficult for employees to find and use.
Much of the information teams create is wasted because employees don’t know what’s there or simply can’t find what they need. Employees spend 20-30% of their workday tracking information. When they can’t find what they’re looking for, they disrupt co-workers’ productivity by asking questions or being directed to a resource.
Time is money, and as we approach the recession, organizations are looking for new ways to increase efficiency, reduce costs and work successfully with leaner teams. We will see more companies use generative AI to easily search for data within internal files and systems and empower the workforce.
2. Integration will be a key driver of enterprise value
Today’s innovation happens on specific platforms. Take Microsoft, for example, which includes ChatGPT and generative AI in everything it offers. Microsoft recently announced Copilot 365, which can collect data from your Outlook calendar and email. from emails to create bullet points to focus on in your next meeting. It can create Word and PowerPoint documents for you based on existing documents. These features provide incredible value to users working on Microsoft tools. However, only 25% of an enterprise’s data typically resides within Microsoft.
The rest of the company’s data lives in Google Drive, ServiceNow, SAP, Salesforce, Box, Tableau dashboards, third-party subscriptions, and a variety of other systems. This is why the enterprise value of generative AI increases exponentially when combined with federated search. It can collect data from across the company’s tools and answer a question or present the information needed at the time.
Consider how Roku unified streaming services and made it easy for consumers to access all their apps in one place. That kind of integration and innovation in generative AI will transform the enterprise.
3. Companies will begin creating AI-powered strategies, policies, and standards
This is the beginning of a new frontier for AI. Features that until recently were only seen in science fiction movies are now available. Companies need to understand the various use cases for generative AI and how this technology can increase productivity and drive growth. Organizations must establish policies on how to use technology and must identify and maintain the correct compliance standards.
As companies embrace AI, teams driving strategy and implementation must decide where it makes the most sense to augment existing applications, where to build new applications, and where to invest in packaged applications.
4. Accuracy will prevail
Some organizations are hesitant to adopt generative AI because it sometimes generates answers. This phenomenon is known as “hallucination” and occurs when there is insufficient content on which to base an answer, or when the system believes that inconsistent data is correct data.
The problem is that generative AI can confidently confirm incorrect or outdated answers as fact. The ability to provide evidence for answers will quickly become table stakes for providers of AI tools. Simply seeing where the answer is coming from allows users to validate the answer before acting or making a decision based on incorrect information. They can also tell the system if an answer isn’t accurate, so the AI learns next time.
The new frontier of AI
The future of generative AI for the enterprise is very bright. Business applications are rapidly emerging that will deliver unprecedented efficiencies and competitive advantages. The pace of changes will be fast. Go ahead and stand out from your competition by defining the business goal of generative AI as your North Star. Choose to invest in the use cases that will deliver the most lasting value to your organization.
Scott Littman is the co-founder and CEO of Lucy®.
Welcome to the VentureBeat community.
DataDecisionMakers is a place where experts, including technical people involved in data processing, can share data-related insights and innovation.
If you want to read about the latest ideas and up-to-date information, best practices and the future of data and data technology, join us at DataDecisionMakers.
You might even consider contributing your own article.
Read more from DataDecisionMakers