5 real-world GenAI case studies
Real case studies show that GenAI offers value when CIOs target key bottlenecks and balance innovation with data governance.
Executive summary
TechTarget spoke with five IT leaders about their GenAI deployments.
- Landing Point: Executive search firm uses GenAI to format resumes and candidate bios.
- LegalZoom: Legal services company built an AI call simulator for sales agent training.
- Canon: Digital imaging company created a cross-functional AI committee to guide rollout.
- Samsara: Tech company uses GenAI for knowledge management and IT support.
- Sikich: Professional services company uses GenAI to reduce repetitive admin work.
Generative AI has moved quickly from experimentation to real business use cases, but results vary widely.
Some organizations see immediate gains from generative AI (GenAI), while others struggle to move beyond pilots. What separates success from failure often comes down to data readiness and how closely AI efforts align with business needs. To find out what works, TechTarget interviewed five IT leaders about how they designed, deployed and scaled GenAI in their organizations. These GenAI case studies span industries, including executive search, legal services, digital imaging and professional services. Each one highlights a different aspect of GenAI deployment.
Together, the following case studies offer a grounded look at what works, what doesn't and how CIOs can apply GenAI in their own organizations.
1. GenAI saves time for Landing Point's recruiters
At Landing Point, a New York-based executive search firm, IT leadership focused its GenAI strategy on a clear problem -- recruiters spent too much time on administrative work instead of engaging with clients and candidates. Lead AI engineer Faizel Khan built AI tools to automate resume formatting, candidate bios and contract drafting and then integrated those capabilities directly into the firm's existing applicant tracking system. This approach drove immediate adoption by improving daily workflows without forcing users to adopt a new application.
The results show how targeted automation can bring business benefits quickly. Recruiters saved about four hours per week, job postings increased and placements moved faster. The project also highlights common challenges, especially around unstructured data and governance. Landing Point had to invest in data labeling and enforce strict access controls within a private cloud environment. For CIOs, the case underscores the importance of solving a visible pain point, embedding AI into existing systems and building guardrails to protect sensitive data.
Key lessons:
- Focus on high-friction administrative tasks.
- Integrate GenAI into existing systems to drive adoption and minimize disruption.
- Address unstructured data early to unlock more advanced AI use cases.
- Use role-based access and private infrastructure.
- Measure success through time saved, not just ROI.
2. LegalZoom builds internal AI call simulator for agent training
At LegalZoom, a legal services company, an internal GenAI project began with a simple experiment. The training team used ChatGPT Voice capabilities to simulate customer interactions and saw immediate improvements in customer service and sales agent learning. That success led operations and engineering to collaborate on a full-scale internal tool. Instead of buying a vendor product, the company built its own AI-powered call simulator that acts as a virtual customer and grades agent responses against internal knowledge bases.
The project shows how a small pilot can turn into a big business benefit -- but not without some friction. LegalZoom was able to move quickly on this project because it had the right infrastructure in place. However, it still had to align teams and refine how it tested AI outputs. The system now offers real-time feedback that previously required hours of manual review, enabling new agents to learn faster. The case highlights the importance of starting small, building on what already exists and ensuring teams stay closely aligned as projects grow.
Key lessons:
- Start with small experiments to prove value before scaling.
- Expand existing infrastructure to move faster and avoid rework.
- Bring operations and engineering together early.
- Keep testing and evaluation simple as AI systems evolve.
- Focus on use cases where humans can review and validate results easily.
3. Canon's AI committee accelerates GenAI adoption
At digital imaging company, Canon, a cross-functional AI committee drives GenAI adoption across IT, legal, finance, marketing and service. The group coordinates strategy across business units and aligns use cases with operational needs. Canon reinforces that approach with broad workforce education and targeted deployments in areas such as customer service, sales and finance.
The company invests heavily in GenAI training, including prompt engineering and ongoing internal sessions, and it requires approval for new tools and use cases. The team also runs structured programs to build understanding across the workforce before expanding deployments. At the same time, Canon learned hard lessons about data quality when early customer-facing applications produced incorrect outputs. For example, the system recommended competitor products in some cases -- an issue that triggered a broader data cleanup effort.
Key lessons:
- Use cross-functional governance to align AI efforts across business units.
- Invest in education and training before scaling GenAI deployments.
- Separate horizontal initiatives such as data quality from vertical business use cases.
- Put approval and review processes in place for new AI tools.
4. Samsara uses GenAI for knowledge management and IT support
At Samsara, a tech company that offers an IoT platform, CIO Stephen Franchetti takes a venture capital-style approach to GenAI. In other words, the organization runs a constant stream of experiments, knowing most will not deliver major value but a small number will produce outsized returns. Successful projects include Samsara GPT -- an internal assistant that connects employees to company knowledge -- and AI-powered help desks that automate routine IT support requests.
The strategy combines rapid experimentation with an intense focus on business value. Samsara uses OpenAI models integrated with internal systems such as sales playbooks and product knowledge bases, which have improved sales performance and reduced support load. The company is also rethinking how older processes fit into an AI-driven workflow, while acknowledging that AI still struggles with complex enterprise actions. The case shows how experimentation at scale can drive adoption, but only when paired with clear business metrics, executive support and processes to evaluate what works.
Key lessons:
- Treat GenAI as a portfolio of experiments, not a single large initiative.
- Tie pilots to measurable business outcomes such as productivity or sales performance.
- Connect AI tools directly to internal knowledge systems.
- Expect friction when legacy processes meet new AI-driven workflows.
5. GenAI boosts productivity at Sikich
At Sikich, a Chicago-based professional services firm, CIO Scott Sanders uses GenAI to help teams do more with the same headcount. The company targets manual, repetitive processes where automation can remove friction, such as administrative tasks that previously required employees to review, move and verify documents. This approach has already offered major efficiency gains, including 2,167 hours of work saved in 12 months from a single automation.
Sikich pairs this focus on productivity with a strong emphasis on governance and security. The IT team requires strict access controls, enforces least-privilege data access and treats GenAI tools like any other enterprise system, with full vetting and policy oversight. The company also invests heavily in training through Sikich University, weekly AI office hours and an AI registry that tracks what teams build. Additionally, Sanders stresses that AI is not a magic tool and that success depends on disciplined use and effective data practices. For CIOs, the case demonstrates that GenAI value comes from focused use cases and strong operational discipline.
Key lessons:
- Target high-friction manual processes.
- Be skeptical of AI product demos before testing them.
- Enforce security controls and least-privilege data access from the start.
- Invest in continuous training and internal knowledge sharing.
- Treat AI outputs as drafts, not finished work products.
Key takeaways for CIOs
GenAI case studies show that successful projects often start with well-defined business problems. The most effective teams identify specific workflow bottlenecks, build or configure GenAI around those processes and embed tools directly into the systems employees already use, when possible.
Governance and data discipline are the other consistent success factors. IT leaders must balance speed and innovation with guardrails and reinforce those controls through internal standards and ongoing training. Additionally, organizations must measure both productivity gains and financial returns to clearly understand what delivered value and what did not.
Tim Murphy is site editor for Informa TechTarget's IT Strategy group.
