Global technology company Lenovo, one of the world’s largest PC and device makers, embedded an AI agent, called iChain, at the core of its global supply chain last year to continuously monitor demand, supplier constraints and transport flows. The agent — built into Lenovo’s AI supply chain suite that it started developing nearly a decade ago — predicts disruptions and dynamically re-optimizes shipments, coordinating across manufacturing, inventory and logistics in real time. This shift improved shipment accuracy by 30%, increased delivery predictability and sustainability at scale, had a ripple effect on the thousands of vendors the Beijing-based company works with, improved customer experience and satisfaction and added to the company’s top line.
“It has been game changing,” Robert Daigle, Lenovo’s Global Head of Enterprise AI, Infrastructure Solutions Group, said in an interview with The Innovator. iChain is just one of the many ways that Lenovo has been using AI to transform its organization. Co-developing software code with agentic AI is helping the company generate 5x to 10x more code; using AI for customer service applications has netted Lenovo over $10 million in operational cost avoidance, a 26% improvement in first-time-right repair rates and a 20% reduction in contact center handle times. And using the technology for marketing content automation is 90% faster and is slashing the cost of product launches by 70%.
“Across core use cases we are seeing an average 60% productivity improvement from the use of AI, which is huge,” he says. The company was so impressed with the results that it turned iChain and some of the other internal AI applications it has developed into services it sells to its customers. “We see AI transforming our own business, inside and out, and having a profound impact across organizations,” says Daigle. “Some of the use cases are for manufacturing, while many of the others are relevant across every industry: all companies can benefit from using AI to optimize their marketing and communication strategy and content creation, fixing customer support challenges, and many can leverage AI for software development in their organizations.”
Lenovo is one of 25 companies transforming their business with AI showcased in a recent white paper published by the World Economic Forum in collaboration with Accenture. Drawing on the Forum’s AI Transformation of Industries Community, comprising more than 450 leading adopters advancing AI at scale across industries, the white paper synthesizes the organizational changes observed among successful enterprises.
Many organizations still struggle to integrate new AI technologies and operating models in ways that maximize business impact across different functional areas. As a result, only a small proportion of organizations – approximately 15% – are using AI to fundamentally redesign how work is performed, says the white paper. It focuses on five critical focus areas where AI is already driving enterprise-level impact. Read on to get some of the white paper’s key insights.
Real-time Individualized Customer Experience
AI enables organizations to sense customer intent in real time, steer experiences dynamically and act on customers’ behalf within clearly defined guardrails, says the white paper. As a result, customer experience “shifts from a series of discrete interactions to continuous, adaptive relationships, anticipating needs, resolving issues earlier and learning from every engagement.”
Case studies showcased as examples include Rabobank, which uses its AI-powered Customer Decision Hub to unify customer profiles and continuously adapt engagement across app, web, online banking and call-center channels. The AI engine aggregates behavioral and interaction data in real time to deliver next-best actions tailored to evolving customer needs, enabling over 1.5 billion personalized interactions per year, a fourfold increase in click-through rates, a 208% lift in conversion, a 4.7% increase in customer lifetime value and a 2.4% reduction in cost to serve.
Another example cited is WPP, the global advertising and marketing services holding company. WPP Open, an AI-powered operating system, unifies creative, media and operational workflows across its global network. AI integrates data, decision-making and execution to steer customer-facing actions in real time, while humans focus on strategy, judgement and oversight. The platform reduced non-essential tasks by 20%, increased creative capacity by 25% and improved productivity by 29%, demonstrating how AI can orchestrate continuous, individualized customer experience at scale.
As customer service becomes adaptive and real-time, sustained value increasingly depends on an organization’s ability to continuously refine the systems that shape interaction, says the white paper. The key takeaway? Corporates that are achieving impact with AI embed mechanisms for updating models, calibrating guardrails and learning.
Adaptive, AI-orchestrated Systems
Like Lenovo, large manufacturers are using AI for everything from manufacturing and supply chains to logistics, maintenance and field services. Historically, these functions were optimized for efficiency and stability through forecasts, standardized processes and human coordination to manage variability and exceptions. AI introduces a fundamentally different architecture across this value chain, says the white paper. By embedding real-time sensing and predictive intelligence into execution, it enables operations to shift from reactive, scheduled execution to adaptive, predictive and learning-based systems that respond dynamically to changing conditions.
For example, Allied Systems deploys agentic AI at the production line level to autonomously optimize operating parameters using real-time data and embedded operator expertise, according to a case study in the white paper. Operators remained in the loop through real-time feedback and approval. The approach scaled across sites, improving overall equipment efficiency by 10%, reducing raw material and energy waste and enabling consistent performance without additional capacity, turning local know-how into a scalable production model.
At Siemens, generative AI enables front line workers to flag design and quality issues through natural language, automatically summarizing and routing insights to the right teams, notes the white paper. Automation engineers use AI co-pilots to generate and debug programmable logic controller (PLC) code, reducing errors and cycle time, while real-time computer vision detects defects in the factories. These capabilities accelerate problem resolution, improve quality and enhance operational resilience.
Transforming R&D With Continuous, Evidence-Driven Learning
R&D is emerging as one of the areas where organizations are realizing the greatest productivity gains from AI, says the Forum white paper. Nearly 40% of senior executives identify R&D among the top functions benefiting from AI investment. AI reshapes the R&D value chain by expanding exploration upstream, shifting decisions earlier, virtualizing validation and embedding continuous learning across stages, expanding the range of options explored and shifting risk assessment from late failure to early calibration.
As an example, the white paper cites Merck KGaA, the German branch of the global pharmaceutical company, which is accelerating early-stage pharmaceutical R&D by using AI to digitalize and automate compound optimization. Through an AI-augmented in-silico platform, generative models can virtually screen over 60 billion potential chemical targets in minutes, narrowing options to a shortlist for human review and lab testing. This integrated workflow significantly compresses the journey from hypothesis to viable molecule, saving up to 70% in time and cost, while enhancing accuracy, efficiency and novelty in molecular discovery.
Google, another case study, integrates AI deeply into its product development and engineering workflows to drive speed, safety and creativity. Within research, large language models (LLMs) now generate synthetic user interactions and adversarial prompts, automating safety testing and accelerating model refinement. Beyond validation, Google also uses AI to support continuous engineering improvement by automatically addressing 12% of duplicate issues without human input.
Predictive, AI-powered Strategic Planning:
Traditionally, strategic planning has been a periodic coordination exercise anchored in annual cycles, static assumptions and delayed feedback from execution, notes the white paper. AI turns strategic planning into an active process by continuously sensing signals, testing assumptions and linking decisions to execution.
For example, Canada Goose uses an AI scenario planning system to make financial planning and analysis faster, with greater consideration for different potential outcomes, so finance teams could reallocate budgets and rerun scenarios quickly as assumptions change, according to a white paper case study. The company reports a 60% reduction in planning cycle time and a 4% improvement in revenue forecast accuracy.
S&P Global used AI to analyze over 190,000 earnings call transcripts, extracting forward-looking market signals from how executives addressed analysts’ questions and emerging risks, illustrating how AI can turn unstructured communication into predictive strategic insight.
Data-driven, Personalized Talent Experience and Workforce Planning:
AI enables near real-time skills mapping, opportunity matching, personalized learning and early risk detection – shifting talent management from traditional HR processes to a continuous, adaptive system, says the white paper. It showcases Unilever as an example. The fast-moving consumer goods (FMCG) company uses an AI-powered internal marketplace to match employees to short-term projects and assignments across the company, based on skills and development goals. Managers post a need, AI suggests near-ready people, and employees build capability by doing real work instead of formal role changes. Unilever has reported 70% cross-functional assignments, approximately 500,000 hours of capacity unlocked and 41% improvement in productivity.
Johnson & Johnson (J&J) uses AI to infer employees’ proficiency across 41 future-ready skills by combining signals beyond job titles, including learning activity and internal experience data. Leaders use a “skills heatmap” to assess capability strength by business line and geography, and decide where to build skills internally versus hire. The approach increased use of J&J’s learning ecosystem by 20% after the first round, with 90% of technologists accessing the platform.
AI As An Engine for Business Reinvention
In 2026, “Enhance/innovate/reinvent our business with AI is the #1 global business priority, overtaking productivity and profit growth,” according to Lenovo’s annual survey of 3,000 CIOs. This signals a fundamental shift: organizations now see AI as the primary lever for transformation and competitive advantage, according to the survey. In the CIO survey, 93% of CIOs said they expect positive ROI from AI, with an average return of $2.79 for every dollar invested.
The Forum’s white paper makes clear, though, that to unlock enterprise-wide impact, AI’s next phase demands a rethinking of core workflows.“Scaling AI requires a rethinking of decision ownership, operating structures and governance mechanisms so that intelligent systems are embedded into execution rather than layered onto existing processes,” says the white paper. “As AI becomes embedded in execution, sustained value depends less on technical sophistication and more on leadership’s ability to align governance, incentives and ways of working with intelligent systems. Organizations that succeed act on AI-supported evidence, continuously reallocate resources and adapt how work is done.”
