4 Trends Shaping the Future of Practical Generative AI

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 technological entrepreneur for more than 25 years. The pace of change in this space has always been incredibly fast. I used to tell people that I was operating in dog years, given that I would see a transformation of about seven years in a single year.

The launch of ChatGPT late last year fueled that speed of innovation. Generative AI exploded, and every day, major tech players like Microsoft, Google, and Salesforce posted competitive ads about how they were integrating the technology into their platforms.

I’ve seen so much progress, demand, and promise in generative AI since then, specifically on the interactive chat side, that I started measuring the pace in hamster years, which is five times faster than dog years.

As generative AI continues to take off and evolve, there are four trends that I expect to play out.

1. Focus will be on training generative AI on business data

Most of the tools that appear in the headlines work exclusively with data in the public domain. However, there is another world of possibilities opening up as generative AI is trained on business data. As Nicola Morini Bianzino, CTO of Ernst & Young, says, this “will change the way we access and consume information within the company.”

This generative AI use case is urgent because access to institutional knowledge is fading. Enterprise data is growing at an explosive rate, yet Gartner estimates that over 80% of that data is unstructured (i.e. PDFs, videos, slides, MP3s, etc.), making it difficult for employees to access it. find and use them.

Most of the information teams create is wasted because employees don’t know what’s available or simply can’t find what they need. Employees spend 20-30% of their workday searching for information. When they can’t find what they’re looking for, they disrupt the productivity of their colleagues by asking questions or being directed to the resource.

Time is money, and as we head into a recession, organizations are looking for new ways to drive efficiencies, reduce costs, and operate successfully with more efficient teams. We will see more companies use generative AI to easily search data in internal files and systems and empower the workforce.

2. Integration will be a key business value driver

Today’s innovation is happening within specific platforms. Take Microsoft, for example, which is incorporating ChatGPT and generative AI into everything it offers. Microsoft recently announced Copilot 365, which can pull data from your Outlook emails and calendar to generate bullet points you can focus on in your next meeting. You can create Word and PowerPoint documents based on existing documents. These capabilities offer incredible value to users working with Microsoft tools. However, only 25% of business data typically resides with Microsoft.

The rest of a company’s data lives in Google Drive, ServiceNow, SAP, Salesforce, Box, Tableau dashboards, third-party subscriptions, and a wide variety of other systems. That’s why the business value of generative AI grows exponentially when combined with federated search. You can pull data from across a company’s suite of tools and answer a question or display the information you need on the fly.

Think about how Roku brought streaming services together 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 business.

3. Companies will begin to establish generative AI strategies, policies and standards

This is the dawn of a new frontier for AI. Capabilities that until recently were only seen in science fiction are now available. Businesses will need to understand the various use cases for generative AI and how this technology can increase productivity and drive growth. Organizations will need to establish policies on how to use technology and will need to identify and adhere to the correct compliance standards.

As businesses embrace AI, teams leading strategy and implementation will need to determine where it makes the most sense to augment existing applications, where to build new applications, and where to invest in packaged applications.

4. precision will rule

Some organizations are hesitant to jump on generative AI because it occasionally makes up answers. This phenomenon is known as “hallucination” and occurs if there is not enough content available on which to base an answer or when the system believes the inappropriate data is correct.

The challenge is that generative AI can confidently state that incorrect or outdated answers are fact. The ability to provide evidence for answers will quickly become a board game for providers of generative AI tools. Seeing exactly where the response is coming from allows users to validate the response before acting or making a decision based on inaccurate information. They can also tell the system if the answer is wrong, so the AI ​​can learn for next time.

The new frontier of AI

The future of generative AI for the enterprise is very bright. Practical applications are rapidly emerging that will offer unprecedented efficiencies and competitive advantages. The pace of change will be rapid. Stay current, and go beyond your competition, by establishing the business purpose of generative AI as your lodestar. Choose to invest in the use cases that will drive the most sustainable value for your organization.

Scott Litman is Co-Founder and COO of Lucy®.