Productivity with AI and innovation

IBM faced obstacles similar to many of its clients. Across the business, analysts were buried in repetitive tasks instead of delivering insights.

In HR, employees collectively spent hours searching for policies on travel and benefits across over 175 countries, while managers could spend an estimated 15–20 minutes on each of the thousands of annual employee transfers. Procurement relied on more than 40 different systems, which meant teams had to navigate dozens of disconnected repositories to answer supplier questions. In IT, support desks were overwhelmed with routine tickets, such as resetting passwords.

In Supply Chain, with over 10 million shipments, 350,000 SKUs, and 200+ direct production part suppliers supporting demand from 170+ countries, IBM faced increasing volatility, frequent disruptions, and highly fragmented data. The result was slow, manual decision-making and limited end-to-end visibility—leading to missed opportunities to optimize inventory, reduce costs, and improve logistics performance. Teams also struggled with delayed access to accurate, up-to-date information across supply chain, sales, and finance systems, resulting in inefficiencies, poor coordination between stakeholders, and an inability to identify and mitigate risks promptly.

In Sales, digital sellers collectively spent a significant number of hours each year switching between five different business applications to support prospecting efforts. Sellers were overwhelmed by the complexity of navigating multiple siloed systems—ranging from sales content platforms and product documentation to communication tools—to retrieve relevant content, craft prospecting messages, and respond to client queries. This fragmented experience reduced seller productivity, slowed deal progression, and impacted client engagement quality.

IBM’s tax department spent significant time manually aggregating and harmonizing large volumes of granular data from multiple source systems to prepare the thousands of tax returns the company files each month—each aligned to the specific regulations of individual jurisdictions.

Overall, there was less time for strategic work, as employees were bogged down with tedious tasks.

IBM’s goal wasn’t just to transform. It was to create a blueprint for enterprise-wide productivity using AI, hybrid cloud, and automation, along with strategic partner technologies and our consulting expertise.

By leveraging key enablers, including AI and other technologies, IBM realized USD 3.5 billion in productivity gains—with total benefits projected to reach USD 4.5 billion by the end of 2025.

Enterprise-wide impact

Designed more than 155 AI use cases for core functions with a goal to streamline work and scale faster decision-making.

Domain-specific impact

• Finance:
  During the initial 3 months of implementation, the team observed:
  • Approximately 90% projected reduction in cycle time across journal processing and related workflows.
  • USD 600,000 possible annual cost savings for the finance team implementing the automation solution.
     
• Supply chain & Procurement:
  • Saved approximately 26,000 hours annually in procurement with data, AI and automation.
  • Enabled procurement teams to focus on supplier strategy and strategic sourcing.
  • Reduced complexity and improved spend management.
  • Since 2022, IBM’s supply chain organization has achieved significant logistics cost reductions—approximately 30% over three years—driven by the combined use of data, AI, and automation tools.
  • Approximately 15.6% in productivity savings in supply chain cost of hardware driven by AI and automation.  
     
• IT support:
  • Automated routine IT tasks, cutting standard support tickets by 56% from 2022 to 2024.
  • Resolved 86% of queries with AI agents.
  • Delivered an initial USD 18M cost reduction, now yielding ongoing annual cost avoidance.
  • Reduced calls and chats by 74% since AskIT’s 2023 launch.
  • Boosted employee satisfaction in AskIT to a 91.6% CSAT, up 11.6 points from its initial launch.
     
• HR:
  • AI agent AskHR now resolves 94% of common inquiries, resulting in a 75% reduction in support tickets compared to historical levels—outcomes achieved through a transformation journey that began in 2017.
  • Reduced operating budget by 40% over the past four years through overall transformation efforts.
    
    
• Tax:
  • Tens of thousands of hours are expected to be saved annually from three use cases automating previously manual, time-consuming processes.
  • Over 95% first-pass invoice review accuracy with AI agents in tests across 18 of 19 invoice formats.
  • EY.ai for tax, built with IBM watsonx, in collaboration with Ernst & Young, LLP was developed to help streamline global tax compliance and support AI-driven transformation.
    
    
• Sales:
  • Sales Assistant, the most widely used AI capability within the AskSales suite, has answered over 250,000 seller questions to date in 2025—saving hours on research and insights.
  • AskSales, leveraging its diverse capabilities, marks the foundation for a unified agentic AI solution for the IBM Sales Team.

Future impact

IBM is also developing AskIBM to bring together and orchestrate domain-specific AI agents and assistants—making them more accessible and seamlessly integrated into employee workflows as part of our forward-looking strategy.

IBM is a multinational technology company with a rich history dating back to 1911. We serve a diverse clientele, offering a wide array of products and services in cloud computing, AI, data analytics and consulting. IBM is recognized for our commitment to innovation, with a significant focus on research and development. We workforce is comprised of talented professionals, contributing to IBM’s substantial revenue and influence in the tech industry.