There has been a tremendous amount of press attention, investment, and new development in the space of AI over the last several years. Much of this attention has been on public-facing tools like ChatGPT and DALL-E, but investor attention has also focused on applications for businesses. Since many of our clients have explored how to integrate AI into their organizations, let’s look beyond the hype of AI at where real adoption is happening today. In many cases, AI is simply an extension of tools and technologies that were already in place, but in others, it represents a larger shift in how companies approach customers, data, and productivity.
Before the AI hype, the terms “Data Science” and “Big Data” were very popular among many clients we work with. Companies have been collecting large amounts of data for many years, but making good use of that data has proven tricky. Pre-AI tools focused on statistical analysis combined with a small army of data scientists to tease insights and connections out of large datasets with mixed success. One of the big challenges of this approach was the cost, or more specifically the return on investment when paying for data science tools and teams.
With AI, we are starting to see more holistic approaches to data usage in the business world. Sometimes called “reducing time to knowledge” or “data insight efficiency”, many companies are starting initiatives to use AI to sift through disparate datasets and surface key information for users via simple query interfaces. For example, we are seeing clients feed large sets of repair and warranty information into AI tools, allowing technicians to ask for and receive summarized instructions without needing to page through large manuals. There are some remaining challenges with this approach, including AI hallucinations and strength of self-hosted vs. cloud-based solutions, but overall, the trend and executive perception are positive.
With the windfall profits that came during the pandemic dying down, many companies are looking to increase their profits through higher employee productivity. Or, in some cases, through staff reductions pared with employee efficiency improvements. While this logic may seem cold, it is also an increasing trend as companies seek to remain competitive with peers that are pursuing aggressive technology improvement projects. In the past, productivity improvements often came through staff training and better basic technology tools. But over the last year we have seen a big shift toward businesses leaning on AI to boost employee productivity.
While exact execution of AI initiatives for employee productivity varies by industry and business, we are seeing the most activity in three areas. 1) Internal “ChatGPT-like” tools connected to internal data repositories, org charts, stored files, etc. Employees are being encouraged to use these tools for everything from asking for IT support to getting summaries of what other teams around the company are working on. 2) Document summaries and analytics for key decisions. When tied into document databases, tailored AI tools can help teams quickly make sense of contracts, legal documents, product spec sheets, and more that would normally require meetings with subject matter experts. 3) Guided content generation for internal teams. We’ve seen AI generative tools used for everything from code development to presentation images and rough drafts of press releases.
Due to the increasingly high costs of staffing for real-time interactions with customers, companies have been looking for ways to boost customer experience tools for many years. Prior to the rise of AI tools, we saw many clients try out chat bots of various intelligence levels, implement voice recognition for phone support and machine commands, and implement several kinds of sales tools based on simple heuristics. With AI technologies, we’ve seen all these applications get supercharged.
The most common AI tool we are seeing clients deploy for customer-facing applications is more intelligent “agent” interfaces accessible via company websites. These cover everything from initial field of technical support cases to sales inquiries and product information leveraging repositories of data on product capabilities, prices, specs, etc. They are usually built on the same core technology foundation as ChatGPT but trimmed down and tailored to each company’s products and information. We’re also seeing significant interest in interactive voice agents used for everything from in-vehicle interactions to kiosk and mobile app assistants.
While it’s easy to dismiss much of the current AI focus as pure hype, the reality is that many companies are already deriving significant value from AI tools for both internal and external uses. Many of these uses are improvements on tools and capabilities that existed for many years, but we’re now seeing experimentation with AI capabilities that leverage generative and “comprehension” properties as well. Looking forward, expect to see AI tools become a more standard part of many employee processes and slowly take over more and more customer-facing activities that don’t require physical presence or a human touch.
If you’re looking for ways to integrate AI into your organization, let’s set aside some time to connect.
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