Businesses collect a lot of data as they grow and change, and over time, that data can become separated and hard to use. According to recent research, knowledge workers simply find and interpret the information they need to do their jobs for more than eight hours per week. But generative artificial intelligence (Gen AI) can help. I have witnessed firsthand the significant role that Gen AI can play in streamlining knowledge management and increasing productivity over the course of the past ten years as a consultant on digital transformation and AI adoption at large enterprises. One of the most promising ways to deploy AI for operational efficiency is via generative business interfaces. The term “semantic” or “conversational” search refers to the method by which users can search company databases without relying on particular keywords. Such interfaces are powered by large language models (LLMs) that use natural language processing (NLP) to understand the user’s intent better than traditional search functions. By combining relevant data from various data sources, these potent instruments create a customized response to each query. Some interfaces can also generate new content grounded in the company’s data sources. McKinsey & Company estimates that in the banking industry alone, Gen AI could increase efficiency by as much as 5%, resulting in cost savings of between $200 billion and $300 billion across the sector.
Many large enterprise resource planning (ERP) providers, including Microsoft Copilot, Salesforce Einstein, and HubSpotAI, offer generative interfaces that work with their own platforms and integrate with others. Numerous third-party options are also available, including Coveo, Bloomreach, Algolia. (Newcomers to this market include enterprise AI startup Glean, which raised over $200 million in March 2024 at a valuation of $2.2 billion, and Hebbia, which raised $130 million in July 2024 at a valuation of $700 million.) SaaS versus API: Which Generative Interface to Choose
Third-party options are either available as SaaS offerings with dedicated search portals or as application programming interfaces (APIs) that a company’s engineers can integrate with their existing systems. SaaS platforms are best for businesses that want to implement generative interfaces quickly and only require basic, pre-installed features to handle everyday tasks. However, businesses that require highly individualized solutions or want their tool trained on a specialized LLM for improved accuracy should go with an API. API-based offerings are most commonly used for product search and discovery use cases. They allow companies to provide generative interfaces within their existing apps and offer much more sophisticated model training and customization capabilities. However, data science resources and skilled developers are required for implementation. In terms of pricing, options are available at all price points. Free use up to a certain number of search queries is included in some vendors’ freemium pricing models. The majority of vendors offer subscription-based or pay-as-you-go pricing beyond the free level. Prices range from a few hundred dollars per month for small businesses to fully customized enterprise-scale solutions that may cost hundreds of thousands of dollars annually or more. Depending on the provider they choose, companies may be able to select which LLM database they wish to use—such as Anthropic’s Claude, OpenAI’s GPT-4, Facebook’s LLaMA, or Google’s Gemini. Each one offers different benefits; for example, GPT-4 is the most powerful and versatile, while Gemini offers the best integration within the Google ecosystem. Your use case should drive that decision.
One of the key technologies that these models utilize is called retrieval-augmented generation (RAG), which delivers real-time insights based on the most recent information available. In addition, it ensures that the information can always be easily verified by tracing it back to its original source. Customized vector databases that use machine learning to categorize information and the deep learning capabilities of neural search may also be used by some providers to improve the accuracy of their search results and summaries for specific audiences.
How Does AI Improve Operational Efficiency?
Automating routine or administrative tasks can generally boost productivity. AI-enabled search interfaces allow workers to find relevant information that is siloed in disparate databases or difficult or time-consuming to interpret. Generative interfaces can even customize and personalize the answer to a query depending on who is asking and what level of data access they have. While this may not directly reduce operational costs, it allows employees to focus more on the strategic activities that drive results.
Generative interfaces can help any department that manages a large amount of information, such as human resources, inventory management, and supply chain management, but in my experience, developers, sales executives, and customer support agents stand to benefit the most immediately.
Here’s how:
Getting rid of information silos for developers Software teams are the backbone of growth for many enterprises. For success, developers must maximize the amount of time they can spend working on code. Yet a wide range of developer-focused surveys show that developers spend significant amounts of time searching for information. A survey conducted by Stack Overflow found that developers encounter knowledge silos at least once per week and spend more than 30 minutes per day looking for answers or solutions. Generative interfaces for engineering help improve operational efficiency by connecting development, deployment, ticketing, and project management resources to enable employees to semantically search and easily find answers from codebase documentation, code change histories and comments, ticket histories, best practices, and more. This, in turn, helps developers write, review, and deploy new code or fix existing code faster than before. By making it easier for new employees to get quick answers to their questions, generative interfaces also significantly cut down onboarding time. Most of the established developer-focused tools and platforms, including Jira, Confluence, GitLab, and Visual Studio have some form of generative interface options, and third-party SaaS solutions are available as well.
Personalizing Sales Outreach
McKinsey research shows that when the buying experience is personalized, about three-quarters of customers say they are more likely to buy, repurchase, and recommend products. This means that sellers must stay abreast of their buyers’ latest purchase preferences, buying stage, and other relevant information—and then use that knowledge to generate an enormous amount of unique content.
Customer relationship management (CRM) platforms, customer emails, billing or configure/price/quote (CPQ) software, and other sales tools can all be quickly pulled into generative interfaces to provide sales teams with the most up-to-date and comprehensive information on customers. These tools can then use that information to deliver highly targeted and personalized outreach emails, sales pitches, value propositions, and other sales collateral with significantly less effort.
Teams may be able to respond to customers more quickly and in a more tailored manner thanks to this capability. Teams can use it to: Develop account dossiers that are more in-depth. Consolidate real-time data, documents, and contact information across multiple applications.
Produce useful information about products and sales regions. Finally, it can accelerate the time to revenue for new sales executives, who otherwise might need up to a year to learn the ropes and carry their full weight in the organization.
Most of the established sales-focused tools and platforms in the market have released their own generative interfaces, including Salesforce’s EinsteinGPT, Microsoft Sales Copilot, and HubSpot AI. However, there are some newer third-party startups that have recently entered the space, including 1up, Regie.ai, and Chatspot.
Speeding Up Customer Support
Customer service teams are having to do more with less, as customer call volume continues to increase, and attrition and talent shortages in the field remain high. Meanwhile, these departments are increasingly responsible for revenue growth and other metrics.
Generative interfaces can support these hardworking teams in a variety of ways. The interfaces connect across the ticketing and communication tools used by support personnel—such as Zendesk, ServiceNow, FreshService, Google Calendar, Slack—and provide data-driven insights to customer care agents about ticket history and resolution, customer value and interaction history, and product how-to’s. This can help agents improve resolution rates, quickly discover upselling/cross-selling opportunities, and even onboard more efficiently when first hired.
Most established sales tools and platforms, such as Freshdesk, Zendesk, Sprinklr, and Zoho have already incorporated generative interfaces into their products; third-party options such as Yuma AI, Tidio, and Intercom are also available.
Customers increasingly prefer self-service over interacting with representatives, and generative AI interfaces play a crucial role there too. AI-powered chatbots and enhanced website search functions rooted in natural language processing help reserve human interventions for the most complex or high-value customer inquiries. Investing in a better customer experience is also good for the bottom line—companies that do so improve customer retention by 19 percentage points and improve customer lifetime value by 25 percentage points, according to recent research from Forrester and Adobe.