As businesses grow and evolve, they collect reams of data, and over time, that information can become siloed and difficult to use. Recent research suggests that knowledge workers spend more than eight hours per week simply finding and interpreting the information they need to do their jobs. But generative artificial intelligence (Gen AI) can help. Over the last 10 years, I have consulted on digital transformation and AI adoption at large enterprises and have seen firsthand the pivotal role that Gen AI can play in streamlining knowledge management and boosting productivity.
One of the most promising ways to deploy AI for operational efficiency is via generative business interfaces. These tools allow users to search company databases in a conversational style that doesn’t depend on exact keywords (also known as semantic or conversational search). 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. These powerful tools construct a personalized answer to each query by combining relevant information scattered across different data sources. 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. (New entrants to this space include Glean, an enterprise AI startup that received more than $200 million in March 2024 at a $2.2 billion valuation, and Hebbia, which raised $130 million in July 2024 at a $700 million valuation.)
SaaS vs. 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 suited for companies that want to adopt generative interfaces with minimal implementation time and are satisfied with basic, out-of-the-box features to manage routine tasks.
However, an API is the better choice for companies that need highly customized solutions or want their tool trained on a specialized LLM for improved accuracy. 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. But implementation requires skilled developer and data science resources.
In terms of pricing, options are available at all price points. Some vendors offer a freemium pricing model which includes free usage up to a certain limit of search queries. Beyond the free tier, most vendors provide subscription-based or pay-as-you-go pricing ranging from a few hundred dollars per month for small businesses to fully customized enterprise-scale solutions that could cost hundreds of thousands of dollars per year 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. Moreover, it ensures that the information can always be traced back to its original source for easy verification.
Some providers may also employ bespoke LLMs based on highly specific training data, customized vector databases that use machine learning to categorize information, and the deep learning capabilities of neural search to make their search results and summaries more accurate for their specific audiences.
How Does AI Improve Operational Efficiency?
In general, AI can automate routine or administrative tasks, increasing 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:
Eliminating Knowledge Silos for Developers
Software teams are the backbone of growth for many enterprises. Maximizing the amount of time developers can spend working on code is crucial for success. Yet a wide range of developer-focused surveys show that developers spend significant amounts of time searching for information. According to a survey by Stack Overflow, developers run into knowledge silos at least once a week and a majority spend more than 30 minutes each day searching for answers or solutions to problems.
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. Generative interfaces also significantly reduce onboarding time by making it easier for new employees to get quick answers to their questions.
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
According to McKinsey research, about three-quarters of customers say they’re more likely to purchase, repurchase, and recommend products when the buying experience is personalized. 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.
Generative interfaces can quickly pull actionable insights from customer relationship management (CRM) platforms, customer emails, billing or configure/price/quote (CPQ) software, and other sales tools to provide sales teams with the most comprehensive and up-to-date 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.
