5 Ways Businesses are Using AI Agents [Case Studies][2026]
Artificial intelligence agents are no longer a future concept reserved for technology companies. They are autonomous systems actively transforming how businesses operate across industries, from fintech and recruitment to professional services and IT operations. Unlike traditional automation, AI agents can reason, decide, and act across complex, multi-step workflows with minimal human intervention, delivering outcomes at a speed and scale that human teams alone cannot match. This shift is already producing measurable results. In the case studies explored by DigitalDefynd, companies including Klarna, Adecco, Synthesia, Equinix, and PwC have deployed AI agents that cut resolution times by up to 96%, freed hundreds of thousands of working hours, and handled millions of customer interactions autonomously. Each example demonstrates how organizations across different sectors are moving beyond pilots into full-scale, production deployments that deliver real, quantifiable business value.
Businesses Using AI Agents [5 Case Studies]
1. Adecco: AI Agents Pre-Screen Candidates 24/7, Achieving a 4.6/5 Satisfaction Rating
High-volume recruitment at a global scale demands round-the-clock candidate engagement, yet Adecco’s AI agent deployment achieved a 4.6/5 satisfaction rating while helping 28 companies fill vacancies.
Challenge
The Adecco Group, the world’s leading talent and technology company operating across 60 countries, faced mounting pressure in high-volume, time-sensitive recruitment. Recruiters were overwhelmed by the manual workload of early-stage candidate screening, which consumed hours that could otherwise be spent on deeper, value-added engagement. Candidates increasingly expected fast, responsive communication, yet standard working hours created an unavoidable gap in outreach. Without automation, recruiters could not realistically contact and pre-screen the volume of candidates needed to fill vacancies efficiently, and response delays were resulting in lost talent opportunities for client companies.
Solution
a. AI-Powered Pre-Screening: Adecco deployed Salesforce Agentforce as its agentic AI platform, launching the first wave in the UK in 2024. The AI agent engages candidates directly via SMS or email in written exchanges that mirror live human conversations. Recruiters shape the agent’s inputs, and candidates are informed upfront that they are interacting with AI, ensuring full transparency throughout the process.
b. Candidate Engagement at Scale: The agent contacts thousands of candidates simultaneously and autonomously conducts pre-screening conversations, asking qualifying questions and capturing responses. Notably, 57% of these conversations took place outside standard working hours, directly addressing the availability gap that previously cost recruiters qualified talent.
c. Recruiter Enablement: Rather than replacing human recruiters, the agent delivers pre-screened, engaged candidate shortlists to recruiters at the start of each working day. This shifts recruiter effort from high-volume outreach to higher-value tasks such as assessing soft skills and conducting meaningful follow-up conversations.
d. Scalable Rollout Across Client Portfolios: The first wave of the implementation directly helped 28 client companies successfully fill their vacancies. The deployment is part of Adecco’s broader global AI strategy spanning its full portfolio of brands, including LHH, Akkodis, and Pontoon, with plans to scale across regions.
Result
The results from Adecco’s first agentic AI rollout demonstrated strong potential for both candidate experience and operational efficiency. Thousands of candidates completed automated pre-screening conversations, with the deployment achieving a 4.6 out of 5 satisfaction rating. Over half of all candidate interactions occurred outside business hours, proving the agent’s always-on engagement capability. By automating early-stage recruitment tasks, Adecco’s recruiters gained the bandwidth to focus on relationship-building and strategic hiring decisions, advancing the company’s mission of making the future work for everyone.
Related: Pros & Cons of AI Agents
2. Klarna: AI Customer Service Agent Handles 2.3 Million Conversations, Cuts Resolution Time 82%
Klarna’s OpenAI-powered AI customer service agent managed the equivalent workload of 700 full-time agents within its first month, cutting average resolution time from 11 minutes to under 2 minutes.
Challenge
Klarna, a Swedish fintech company serving over 150 million active users across 23 markets, faced a growing crisis in customer service scalability. As its buy-now-pay-later platform expanded globally, inbound support volumes surged across topics including refunds, payment disputes, billing errors, and order cancellations. Traditional call center operations and human-staffed chat support could not keep pace with demand across multiple languages and time zones. Resolution times were climbing, repeat inquiries were frequent, and the cost of scaling human support proportionally was unsustainable. Klarna needed a solution capable of delivering consistent, accurate, and empathetic responses at a massive scale without a corresponding increase in headcount.
Solution
a. OpenAI-Powered Conversational Agent: In February 2024, Klarna launched an AI-powered customer service agent built in partnership with OpenAI and developed using the LangGraph framework, with LangSmith used for testing and performance refinement. The agent was trained on Klarna’s domain-specific policies, product documentation, and historical customer interactions to ensure accurate, context-aware responses.
b. Multilingual, Always-On Support: The agent operates around the clock across all 23 of Klarna’s markets and supports over 35 languages. This eliminated the time-zone and language barriers that previously created service gaps for international customers, giving every user access to immediate assistance regardless of location or hour.
c. Intelligent Routing and Escalation: The agent handles up to five automated interactions per conversation before escalating complex or emotionally sensitive cases to human agents. When escalation occurs, the full conversation context is passed to the human representative, eliminating the need for customers to repeat themselves and reducing handle time for escalated cases.
d. Continuous Learning and Optimization: Klarna invested significantly in cleaning and standardizing its help center content before launch, ensuring the agent had reliable knowledge to draw from. Post-launch, the agent was continuously retrained on real conversation data to improve accuracy, reduce hallucinations, and refine responses over time.
Result
Within its first month of global deployment, Klarna’s AI agent handled 2.3 million customer service conversations, performing the equivalent work of 700 full-time agents. Average resolution time dropped 82%, falling from 11 minutes to under 2 minutes, while repeat inquiries declined by 25%. Customer satisfaction scores matched those achieved by human agents. Klarna projected a $40 million profit improvement in 2024, attributed to AI efficiencies, with an overall 40% reduction in cost per transaction recorded since the first quarter of 2023. The deployment stood as one of the largest and most closely studied AI customer service rollouts in the world.
Related: Agentic AI in Education
3. Synthesia: Intercom Fin AI Agent Cuts Resolution Time 96%, Handles 690% Customer Support Surge
Synthesia deployed Intercom’s Fin AI Agent to absorb a 690% spike in monthly support volume, enabling 98.3% of users to self-serve without human escalation and cutting resolution time by 96%.
Challenge
Synthesia, an AI video generation platform serving customers in over 100 countries, experienced explosive growth that placed its customer support operation under severe strain. In just four months, the number of customers seeking monthly support rose from 40,000 to 316,000, representing a 690% increase. The support team had no realistic path to hiring at the pace required to meet this demand. Without intervention, average resolution times would have deteriorated significantly, and customer satisfaction would have suffered at exactly the moment the company needed to build loyalty among a rapidly expanding user base. Scaling the human team to match the volume would have required growing to approximately 150 people, a cost and timeline the business could not absorb.
Solution
a. Fin AI Agent Deployment: Synthesia implemented Intercom’s Fin AI Agent, powered by Anthropic’s Claude, to serve as the primary first-response layer across its support operations. The agent was deployed to handle the highest-volume, most repetitive inquiry categories, which accounted for a large share of daily ticket intake and were the clearest candidates for automation.
b. Knowledge Base Overhaul: Before Fin could perform reliably, Synthesia’s team identified a critical prerequisite. Their existing knowledge base contained contradictions and outdated content that caused Fin to produce inconsistent responses in early testing. The team undertook a substantial restructuring of their help documentation, ensuring accuracy, consistency, and completeness before fully activating the agent.
c. Quality Assurance Through Co-Monitoring: During the optimization phase, Synthesia’s support staff joined every second Fin conversation to review response quality in real time. This hands-on monitoring allowed the team to identify gaps, refine knowledge articles, and validate that Fin was representing Synthesia’s products and policies accurately before scaling the deployment.
d. Seamless Human Escalation: Fin was configured to escalate conversations beyond its resolution capability to human agents without friction. This tiered model ensured complex or sensitive cases received appropriate human attention while routine queries were resolved autonomously, preserving team capacity for high-value interactions.
Result
Following deployment, Fin AI Agent achieved an answer rate of up to 98% across the conversations it handled, meaning it was able to understand and respond to nearly every customer query it encountered. Resolution time dropped by 96%, falling from five days and five hours to four hours and 37 minutes. During the peak surge month, 98.3% of the 316,000 customers seeking support resolved their issues through self-service, with only 1.7% requiring a human agent. Within the first six months of implementation, Fin resolved over 6,000 conversations and saved the support team more than 1,300 hours. Human agent CSAT held steady at 93%, while Fin’s own CSAT score doubled following launch.
Related: How Agentic AI Will Redefine Jobs?
4. Equinix: Moveworks AI Agent E-Bot Routes 82% of IT Support Tickets in Under 30 Seconds
Equinix deployed Moveworks’ AI agent, known internally as E-Bot, to autonomously triage IT support tickets with 96% accuracy, cutting the average ticket lifespan by nearly a third.
Challenge
Equinix, the world’s largest data center and interconnection platform company, operates a globally distributed workforce supported by an IT team of approximately 400 people. With its help desk based in the Philippines and the majority of its international workforce concentrated in the US and UK, time zone misalignment meant IT issues frequently piled up overnight, creating backlogs that damaged employee productivity and satisfaction. Service desk agents were required to manually read every incoming ticket and determine the correct assignment group from among hundreds or thousands of options, a process prone to misrouting and delays. The average time to read and route a ticket stood at five hours, and the company’s leadership recognized that continuing to scale IT support headcount proportionally was neither efficient nor sustainable as the enterprise grew.
Solution
a. Moveworks AI Agent Deployment: In April 2019, Equinix launched Moveworks’ conversational AI agent within Microsoft Teams, known internally as E-Bot. Powered by advanced natural language understanding, E-Bot enables employees to submit IT support requests through natural language conversation, regardless of how the query is phrased, and receive immediate assistance directly within the Teams interface they already use daily.
b. Intelligent Ticket Triage: E-Bot was trained to distinguish among thousands of Equinix’s IT assignment groups, enabling it to accurately route high-touch tickets that fall outside its autonomous resolution capability to the correct subject-matter experts. This triage function matches the 96% routing accuracy previously achieved only by experienced human help desk agents, eliminating the misrouting that had contributed to extended resolution timelines.
c. End-to-End Autonomous Resolution: For a broad range of common IT requests, including password updates, account unlocks, software provisioning, distribution list creation, and colleague lookups, E-Bot resolves issues entirely on its own within seconds. Employees interact through a brief conversational exchange and receive a resolution without waiting for a human agent to become available, regardless of the time zone they are working in.
d. Continuous Scaling Without Headcount Growth: Because E-Bot handles both autonomous resolution and intelligent triage, Equinix’s IT leadership was able to maintain a flat headcount while scaling support capacity across a growing global enterprise, directly fulfilling the company’s strategy of automation-driven growth.
Result
Within ten months of deployment, E-Bot was autonomously resolving thousands of IT tickets end-to-end each month without any human agent involvement. The agent now routes 82% of all Equinix support tickets, completing triage in under 30 seconds compared to the previous five-hour average, reducing the overall lifespan of tickets by nearly a third. Employee satisfaction with IT support reached 96%, reflecting the improved speed and consistency of service delivery. IT staff in the Philippines were relieved of the manual ticket-reading workload entirely, allowing them to focus on resolving complex issues rather than performing routing tasks. Equinix achieved its core objective of scaling IT support operations globally while keeping headcount flat, validating the company’s automation-first strategy.
Related: Agentic AI in Finance
5. PwC: Microsoft Copilot AI Agents Free 500,000 Hours of Capacity Across 230,000 Global Employees
PwC deployed Microsoft Copilot AI agents across 230,000 employees in over 100 countries, with staff executing 8.7 million Copilot actions in a single month and freeing over 500,000 hours of capacity.
Challenge
PwC, one of the world’s largest professional services networks, operates across more than 100 countries and employs hundreds of thousands of professionals spanning audit, tax, consulting, and advisory functions. The firm faced a dual challenge of maintaining premium service quality for clients while managing the escalating volume and complexity of internal knowledge work. Employees spent significant time on time-intensive, repeatable tasks such as document preparation, data extraction from complex files, meeting summarization, research compilation, and report drafting. Doing this at the scale and speed clients expected, while simultaneously upskilling a global workforce and meeting stringent data privacy and regulatory requirements across multiple jurisdictions, represented a significant operational constraint. PwC’s leadership recognized the need for an enterprise-grade AI solution that could be deployed responsibly and consistently across a highly distributed, compliance-sensitive organization.
Solution
a. Enterprise-Wide Microsoft Copilot Rollout: PwC deployed Microsoft Copilot across its global network, building one of the world’s largest enterprise Copilot deployments. The rollout began across 11 countries using a decentralized model that allowed each region to set its own adoption priorities and adapt functionality to local regulatory and operational needs, before expanding to over 100 countries.
b. AI Agents for Knowledge Work: PwC teams built and deployed AI agents using Microsoft Copilot Studio that autonomously query data sources, extract insights from complex documents, and surface relevant information in seconds. Research teams that previously spent hours manually processing dense files were able to redirect their time to higher-value analysis and client-facing work, with agents handling the data retrieval and extraction tasks.
c. Governance and Security Architecture: PwC embedded Microsoft’s enterprise-grade governance framework into the deployment from day one. Local data was stored and processed within regional boundaries, ensuring compliance with jurisdiction-specific privacy regulations. Role-based access controls, encryption, and audit trails were configured to meet the firm’s internal risk standards and client confidentiality obligations across every territory.
d. Workforce Enablement and Adoption: PwC established structured training programs, including live demonstrations, regular playbooks, and ongoing prompt-of-the-day campaigns to drive consistent adoption across all levels of the organization. AI Champions were embedded within teams to accelerate onboarding and share best practices, ensuring employees understood how to use agents effectively and apply responsible AI principles in daily work.
Result
By October 2025, PwC employees were executing over 8.7 million Copilot actions per month, freeing more than 500,000 hours of organizational capacity in that month alone. With 54% of PwC’s global workforce using AI tools on a weekly basis, the average employee was generating nine prompts per week. AI agents enabled research teams to extract insights from complex files in seconds rather than hours, while citizen developers across the firm built low-code applications that further automated and improved internal processes. The deployment demonstrated that a globally distributed, compliance-driven professional services organization could successfully embed agentic AI at scale, using technology to elevate the quality and speed of work delivered to clients worldwide.
Conclusion
The five case studies examined on DigitalDefynd illustrate a consistent pattern across industries: businesses that deploy AI agents with well-defined use cases, clean underlying data, and thoughtful human-AI collaboration frameworks achieve transformative operational results. Klarna automated customer service at a scale equivalent to 700 full-time agents. Synthesia absorbed a 690% support surge without adding headcount. Equinix resolved IT tickets in seconds rather than hours. Adecco pre-screened candidates around the clock. PwC reclaimed half a million hours of professional capacity in a single month. Taken together, these deployments confirm that agentic AI is not a speculative investment but a proven operational lever. Organizations that approach AI agents strategically, starting with high-volume, repeatable workflows and expanding deliberately, stand to gain a decisive competitive advantage. The era of AI agents working alongside human teams is not approaching. It is already here.
