Reported impact
Business outcome
ABB reports that the program increased employee access to generative AI tools and training, enabling productivity, exploration, and self-development at scale across the workforce.
- Productivity Improvement
In-houseManufacturingEnterprise Knowledge SearchGenerative AI / LLMProductivity Improvement2026 In-houseIN#0f766eManufacturingGlobalABB wanted to give employees a safe, responsible way to use generative AI in daily work so they could improve productivity, explore ideas more effectively, and support ongoing learning and career development.Abby supports more than 15 large language models (LLMs)Apr 22, 20265Enterprise Knowledge SearchAbby0 2026-04-22 5
ABB
Enterprise Knowledge Search
ABB wanted to give employees a safe, responsible way to use generative AI in daily work so they could improve productivity, explore ideas more effectively, and support ongoing learning and career development.
Abby supports more than 15 large language models (LLMs)
As of 2025, more than 35,000 unique users used Abby for over 400,000 conversations
In 2025, we reached over 24,000 employees with our AI training
the total participation across all sessions was over 41,000
Solution
ABB built and rolled out Abby, an in-house generative AI assistant for employees, alongside a broader enablement program focused on safe and responsible AI use. Abby is integrated with ABB's internal libraries and systems so staff can retrieve company-specific information, and it also lets them search public information for broader research. Employees submit prompts in Abby for daily tasks such as content creation, data analysis, and web research, and the tool routes those requests to one of more than 15 supported large language models based on user needs. Abby also serves outputs back into employee workflows by generating presentations with ABB templates and by allowing users to create custom task agents that automate repetitive work. Training, approved-tool guidance, and internal resources act as the governance layer around adoption.
Data flow
Employees submit prompts in Abby, which pulls company-specific information from ABB internal libraries and systems and combines it with access to public information. Abby sends requests to one of more than 15 supported LLMs, then returns answers, analyses, web research, presentations built with ABB templates, or agent-driven task outputs back to employees.
Solution architecture
- Uncategorized
- Abby ABB's in-house generative AI tool that helps employees with daily tasks, content creation, data analysis, web research, presentation generation, and custom task agents.
Architecture clues
- Can generate presentations using ABB templates
- Governed through approved tools, training, and safe/responsible AI guidance
- In-house deployment
- Integrated with ABB internal libraries and systems
- Supports selection across more than 15 LLMs
- Users can create custom task agents to automate repetitive work
Evidence from the source
"Abby supports more than 15 large language models (LLMs), allowing employees to select the model that best fits their expertise and their needs."
"As of 2025, more than 35,000 unique users used Abby for over 400,000 conversations."
"Beyond that, users can create custom task agents to automate repetitive work, also known as AI agents."
"In 2025, we reached over 24,000 employees with our AI training, and the total participation across all sessions was over 41,000."
"Integrated with our internal libraries and systems, it enables employees to search both company-specific and public information."
"It can also generate presentations using ABB templates."
"we developed Abby—an in-house generative AI tool that helps our employees with daily tasks, content creation, data analysis and web research."
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value75/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
- Source authority25/25The deployment is described by the adopting organization or a government source.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/207 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.abb.com/global/en/company/stories/ai-in-the-workplaceABBFirst-party
ABB customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Apr 22, 2026 · ABB · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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