As enterprises rush to deploy artificial intelligence across their organizations, many are discovering that the hardest part is no longer choosing a large language model. Instead, the challenge is identifying which AI projects are worth pursuing, how to deploy them quickly and how to avoid repeating mistakes others have already made.
That realization has prompted Lenovo to spend the past three years building what it calls an AI Library — a curated collection of enterprise AI use cases, deployment templates and implementation patterns that have already been tested both internally and with customers before being offered to other organizations.
Need more clues? Ask the Sherlock chatbot in the lower right corner to summarize this story, explain technical concepts or answer other questions.
The goal is straightforward: Help enterprises stop reinventing the wheel every time they launch an AI initiative.
“We were finding that customers, ourselves included, were losing a lot of time and doing a lot of rework to try to find the right use case for the right scenario using the right AI method,” Linda Yao, vice president of hybrid cloud and AI solutions at Lenovo, said in an interview with The AI Innovator. (There is no relation to this article’s author.)
By using the AI Library, “we want to increase the probabilities of success,” she added.
The approach reflects a broader shift taking place across enterprise AI. According to Lenovo’s CIO Playbook 2026, nearly all – 96% – of respondents plan to increase AI investments over the next year. Meanwhile, AI has overtaken productivity as the No. 1 business priority for 2026. However, only 27% of organizations report having comprehensive AI governance frameworks in place, highlighting the challenge of scaling AI safely and consistently.
Lenovo’s service offering sits in an increasingly crowded market for enterprise AI implementations. Technology vendors including IBM, Accenture, Deloitte, Microsoft, Google Cloud, AWS, Nvidia and ServiceNow all offer frameworks, prebuilt AI agents or consulting services designed to help enterprises move AI projects from pilot to production.
One difference in Lenovo’s approach, Yao argued, is its emphasis on a curated library of use cases that have already been deployed, refined and validated, along with implementation templates that capture the technical lessons learned from those projects. Rather than asking customers to assemble AI systems from individual models and software components, Lenovo is positioning the AI Library as a collection of reusable deployment blueprints intended to reduce implementation time and project risk.
Building a library from experience
Lenovo started building its AI Library after noticing that many customers were pursuing similar AI projects while repeatedly solving the same implementation challenges. The company also found itself confronting many of those same issues internally.
As one of the world’s largest technology companies, Lenovo operates across numerous business units, countries and functions, creating opportunities to test AI internally and apply lessons from those deployments to customer projects.
“The reason we thought it was important to curate a library … is because AI is so exciting, and when you have an exciting hammer, everything is a nail,” Yao said. “But AI can sometimes, especially now, be a very expensive hammer to throw at every different nail.”
The goal is to apply the right type of AI to the problem. Some projects are better served by traditional machine learning, while others require generative AI, physical AI or agentic AI. “All AI is also not created the same,” Yao said.
The AI Library contains curated AI use cases, deployment templates and prebuilt AI agents. It contains just under 100 base or template agents and several hundred versions customized for different industries and domains. About 50 have proved particularly repeatable with customers.
For each use case, Lenovo documents the implementation details that enterprise IT teams typically spend months figuring out themselves.
Those templates include recommended data formats, APIs, connectors, model-selection guidance, required technical skills and advice on tuning models for particular industries. The company also identifies where specific AI agents are broadly applicable and where they perform better in certain vertical markets.
The idea is to give customers shortcuts that reduce deployment time while helping them avoid known pitfalls. Instead of starting every project with a blank slate, enterprises begin with architectures that have already been battle-tested.
Enterprise knowledge in an AI assistant
One of the library’s flagship offerings is Lenovo’s Knowledge Super Agent, a multipurpose agent designed to organize an enterprise’s institutional knowledge into a searchable, conversational assistant.
The system aggregates structured and unstructured enterprise information, harmonizes it into a knowledge base and enriches it with semantic ontology and metadata tagging before making it available through natural-language interfaces.
The application can appear inside Microsoft Teams, Google Workspace, mobile applications or other collaboration platforms, allowing employees to interact with it much like another colleague.
One common use case is employee onboarding.
Large global organizations often have thousands of internal acronyms, project names and specialized terminology that new employees must learn before becoming productive.
Rather than simply defining unfamiliar terms, the Knowledge Super Agent explains where they originated, identifies the projects in which they are used, links employees to supporting documentation and points them toward the appropriate subject-matter experts.
“Every global enterprise has a plethora of acronyms,” Yao said. “Even if you know what the acronym stands for, you don’t know the context of where it came from. You don’t know the source material of how this acronym came to be, and you don’t know the different contexts and projects in which it’s going to be used.”
Use cases for global firms
For multinational organizations, the system also accommodates different languages and regional terminology while maintaining consistent corporate knowledge.
Yao said third-party testing found that the Knowledge Super Agent reduced time spent on knowledge-related tasks by around 30%.
Yao said another common use case is enterprise translation.
Unlike public AI services, enterprise deployments allow organizations to preserve industry-specific terminology while preventing proprietary information from leaving corporate environments.
“It’s not as easy as just loading it to Google Translate because you want it to have the context of your industry, your domain, but also your own company language,” Yao said.
She added that companies also have security concerns. “There are other reasons you don’t want to just load stuff into Google or into ChatGPT for translation – it’s because your proprietary secret sauce is going out to the public domain,” Yao said.
Lenovo has customized the Knowledge Super Agent for FIFA, which must onboard thousands of temporary workers before major international tournaments.
The deployment serves multiple user groups. Coaches can retrieve specific game footage and analyze individual plays. Players can review their own performances and identify where fatigue affected their decisions. Broadcasters can search and tailor video content for different audiences. The system also supports about a dozen languages spoken across 48 teams participating in the 2026 World Cup.
