Each year the CIO 100 Awards showcase outstanding IT initiatives, and every year they illustrate the power and potential of technology to transform how people work, how organizations perform, and the value they offer to customers.
The 2026 cohort of winners is no different. Each one demonstrates how IT executives and their teams successfully move from ideation to deployment to scaling a solution for the future, overcoming challenges and driving adoption along the way to ensure their organization gets a return on its investment.
[ Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here ]
The winning initiatives come from a range of industries and utilize a host of technologies to achieve their goals, as is the case annually. A growing proportion of these stand-out projects leverage artificial intelligence, raising the bar on the art of the possible for all IT departments.
The following 10 award-winning projects serve as representatives for the outstanding work done by all the 2026 honorees.
ABB democratizes AI agent creation and deployment
Organization: ABB
Project: ABBY — AI Agentic Platform for Workforce Transformation
IT leader: Vikke Kandell, CIO
IT leaders at ABB, a manufacturer, had some big hurdles to clear when it came to building an AI strategy.
They had to overcome employee fears that AI would take away jobs, the potentially high cost of AI vendor licenses, and pressure from investors, customers, and executives to advance the use of AI in the enterprise.
“We looked at this and asked, ‘How do we address all this?’ and build something that the company is proud of,” says Babu Kuttala, vice president of data analytics and AI.
The answer is ABBY, an AI agentic platform that enables employees to create and deploy specialized AI agents for specific business tasks.
To build ABBY, Kuttala and his team used best-of-breed LLMs (about 25 in total). They built a centralized orchestration layer using generative AI that integrates internal knowledge bases with external ecosystems, creating a unified platform where agents can access enterprise data, understand required actions, and execute tasks across multiple systems. And they created preconfigured skills so that employees could build agents tailored to their workflows without having to code.
ABBY was rolled out in 2025 to 100 users but is now used by 63,000 (more than 75% of the company’s workforce, Kuttala notes) with an average of 10,000-plus workers using it daily. IT continues to add LLMs and capabilities to expand use of ABBY even further, Kuttala says.
Belcorp modernizes manufacturing with Smart Factory
Organization: Belcorp
Project: QPlant — Smart Factory
IT leader: Venkat Gopalan, Chief Digital, Data, and Technology Officer
Legacy processes were limiting Belcorp’s ability to scale and compete. Its manufacturing relied on ERP-driven processes with limited shop-floor automation and weak connectivity across production, packaging, quality, and maintenance. The company depended heavily on manual records and post-process reconciliation, resulting in fragmented data, limited real-time insight, inefficiencies, and higher risks for errors.
Smart Factory changed all that. The IT initiative reimagined how manufacturing teams work “by creating a connected, data-driven environment where production, quality, maintenance, and operations are aligned around real-time information and standardized execution,” says Venkat Gopalan, chief digital, data, and technology officer.
At Smart Factory’s core is a manufacturing execution system that orchestrates production workflows, quality processes, and operational execution, he explains. IoT-enabled equipment integration and a centralized SCADA platform provide real-time visibility into shop-floor operations, while electronic batch records digitize production execution, strengthen traceability, and reinforce compliance by design.
Integrating those operational technologies with the company’s enterprise platforms was another critical component of success, Gopalan says, creating a trusted flow of real-time data across manufacturing, quality, maintenance, and business systems. “This connected architecture transformed isolated data into actionable insights, enabling faster decision-making, greater operational visibility, and continuous improvement across the manufacturing lifecycle,” he adds.
The initiative generated more than $1 million in financial benefits in its first year alone.
“Most importantly, Smart Factory established the digital foundation for the future of manufacturing at Belcorp,” Gopalan says. “With real-time operational data and connected systems now in place, we’re well positioned to accelerate advanced analytics, AI-driven optimization, predictive maintenance, and other Industry 4.0 capabilities that will continue delivering value for years to come.”
Cohesity replatforms post-acquisition for commercial growth
Organization: Cohesity
Project: Lead to Cash Replatforming Program (Veritas Integration)
IT leader: Brian Spanswick, CIO
Cohesity set an ambitious objective: Complete an enterprise-scale lead-to-cash replatform in under six months.
That’s a tight timeline for any replatforming initiative, but Cohesity’s project had another layer of complexity. It followed Cohesity’s December 2024 acquisition of Veritas, a company twice its size in revenue, leaving Cohesity to integrate the majority of a global enterprise revenue engine into its own operating model without disrupting customers, partners, or sellers.
“We had to bring the two companies together, merge the workforces together, and create an overall harmonized organization and operating infrastructure platform,” says Eric Brown, who as CFO and COO led the project.
The program migrated heavily customized CRM, CPQ, PRM, ERP, and subscription platforms (some of which were “very brittle, very bespoke,” Brown says) to a unified SaaS CRM, CPQ, and ERP environment with uninterrupted selling, billing, and partner operations.
This was no lift-and shift, Brown stresses. “It was a business process optimization project as well. We want to run very efficiently, so we questioned everything and used the migration process to simplify and streamline the business in every possible respect.”
The initiative enabled continuity for 13,000-plus customers, protected revenue during integration, and established a scalable commercial foundation for future growth.
Brown cites several factors that contributed to success. First, leadership was upfront about what it would take to meet the deadline, a process that involved carefully prioritizing the capabilities that would appear in the first iteration. Leadership also streamlined decision-making, establishing office hours that “ran with military precision” to handle issues. And the company selected a specialized partner, requiring its top talent be assigned to Cohesity.
Dairyland Power goes agentic to protect field crews
Organization: Dairyland Power Cooperative
Project: ODIN — Organizational Effectiveness Agentic AI
IT leader: Nate Melby, VP and CIO
Dairyland Power Cooperative had amassed a large collection of field observations, incident reports, near-misses, safety rules, and work methods that could yield insights into processes and practices that could help protect its workers.
But the insights were essentially out of reach, trapped in siloes.
Dairyland’s organizational effectiveness team turned to CIO Nate Melby for help unlocking those insights. Melby then turned to agentic AI, recognizing that the technology could address the team’s need to make better use of its data.
“This was about finding insights on how to work more safely,” Melby says. “It’s about preventing incidents.”
The collaboration between the two teams created ODIN, the first agentic AI implementation of its kind in the electric utility industry.
Focused on worker safety, ODIN autonomously connects the collective safety knowledge of the organization and delivers actionable insights directly to field crews at the moment work is planned.
ODIN was developed through a hybrid approach that combined an agentic AI platform and Dairyland’s internal private generative AI platform called VoltWrite. ODIN leverages LLMs, retrieval-augmented generation, and a coordinated swarm of autonomous agents.
Agents work together to analyze internal safety data, performance history, work practices, and safety rules and then synthesize the information into clear guidance on the safest way to perform specific tasks.
ODIN has produced results, including a reduction in OSHA recordable injuries and improvements in the quality and consistency of pre-job safety briefings.
ODIN was deployed in early 2025 for use by Dairyland’s workers in transmission construction and electrical maintenance, which are the highest-risk work areas. Dairyland is looking to expand ODIN’s use to other teams.
Dow’s digital sustainability ledger drives low-carbon sales
Organization: Dow
Project: Carbon Footprint Ledger
IT leader: Deb Bauler, Chief Information and Digital Officer
Executives at Dow consider the Carbon Footprint Ledger (CFL) as more than a technology or innovative carbon accounting methodology. According to Senior Global IT Director Jeremy Preston, CFL is “a digital business capability that enables Dow to translate sustainability investments into customer value.”
CFL transformed how Dow uses greenhouse gas emissions data. It combines a methodology aligned to international standards with an enterprise-scale digital platform. It also integrates manufacturing, supply chain, commercial, and sustainability data to generate product carbon footprints under enterprise-level governance and management at scale.
In doing so, Preston says it creates “a trusted, traceable link between low-carbon processes and raw materials implemented across its manufacturing network and the lower-carbon products customers seek.”
The technology team worked closely with sustainability and business teams, collaboratively developing the capabilities needed to reconstruct product genealogy, maintain end-to-end data lineage, track low-carbon attributes across interconnected manufacturing processes, and generate product carbon footprints that can support customer offerings and commercial transactions.
CFL was built on Dow’s Integrated Data Hub and in partnership with Boston Consulting Group and Databricks.
The core CFL platform is fully deployed and supports commercial transactions today.
Preston says CFL “enables Dow to turn sustainability investments into customer value, commercial differentiation, and new growth opportunities.” Dow reports that it has driven hundreds of millions of dollars in low-carbon product sales in 2025 and 2026.
The company is now expanding its use. “We are extending adoption across additional products, manufacturing networks, business segments, and customer use cases while continuing to enhance automation, analytics, and integration with commercial processes,” Preston says.
J&J transforms quality management with AI
Organization: Johnson & Johnson
Project: Q&C Strategy
IT leader: Michael Comprelli, Vice President, Head of Technology, Technical Operations, and Risk; Joel O’Connor, Head of Technology, Medtech Quality, and Compliance
Johnson & Johnson is using AI to transform quality management through its Q&C Strategy.
QuIn is an AI-powered digital assistant that fuses human expertise with machine learning, automation, and data-driven insights to boost efficiency, reliability, and worker impact. By embedding gen AI into core quality management systems processes, QuIn proactively gathers actionable insights, increases operational efficiency, and allows teams to focus on high-value, patient-centric work.
Cora is an innovative generative AI platform that provides regulatory intelligence monitoring, impact analysis, and augmented content revision. Cora assists with document analysis, compliance comparison, stakeholder analysis, policy/standard creation, procedural/document updates, and document comparison. Cora is purpose-built for regulated environments, validating outputs against source material and offering a user experience that instills trust in the outcome.
QuIn and Cora, which automate time-intensive tasks and democratize information access, are on track to deliver significant value, with J&J reporting more than $62 million in documented true cost savings by 2028 from QuIn alone. Cora delivered $2 million in cost efficiency in 2025 and will deliver a documented cost savings of $25 million by 2028.
“Our teams proved responsible AI can be applied meaningfully in a highly regulated environment without compromising the rigor, accountability, or human judgment that quality requires,” says Michael Comprelli, vice president, head of technology, technical operations, and risk.
He continues, saying that J&J “moved these ideas beyond experimentation and into products that employees use in their daily work. We did that by bringing together Quality expertise, product management, data engineering, architecture, cybersecurity, user-experience design and AI engineering around a common purpose.”
JLL brings intelligent automation to business services
Organization: JLL
Project: Business Service Digitization
IT leader: Pinak Dash, Global Head of JLL Business Services and Legal Technologies
JLL launched its digitization initiative to drive process redesign as well as systematic AI and RPA deployment across JLL Business Services (JBS).
The initiative was designed to address inefficiencies that hampered scalability and competitive positioning. It was also designed to eliminate manual processes that consumed thousands of hours across finance, HR, legal, procurement, marketing, research, IT, and lease administration.
Pinak Dash, global head of JBS and legal technologies, says the digitization initiative had a dual-strategy combining traditional digitization with generative AI innovation to hundreds of processes.
JLL lists three innovations critical to the program’s success.
First is a hybrid platform that integrates RPA with JLL’s proprietary AI platform called Falcon, which created intelligent automation that adapts and learns. It enables real-time process automation, intelligent document processing with automated extraction/validation, and smart decision-making for continuously optimizing workflows.
The second innovation is its use of ProHance for real-time process monitoring and enabling of data-driven optimization. Sensors capture granular productivity metrics, identify bottlenecks, and provide actionable insights for continuous improvement across automated and manual processes.
Third is its custom AI assistants and transaction agents. Falcon-powered assistants provide intelligent knowledge search while specialized agents execute complex transactions across enterprise SaaS platforms. These handle multisystem workflows, reducing human touchpoints while maintaining accuracy and compliance.
Dash says the initiative has delivered quantifiable benefits through improved efficiency, accuracy, and quality of services provided to clients.
“The initiative delivers on our business goals, makes us more efficient, provides customers better service, and it opens up the capabilities and bandwidth of our people to do what they like to do and to find innovative ways to serve our business,” he adds.
Nationwide partnership platform delivers efficiencies, business growth
Organization: Nationwide
Project: Enterprise Digital Platform (EDP)
IT leader: Michael Carrel, EVP and CTO
Nationwide’s new Enterprise Digital Platform (EDP) gives the company “a scalable way to connect with external partners quickly, securely, and consistently across all areas of our business,” says company EVP and CTO Michael Carrel.
He explains that “instead of treating every integration as a custom effort, EDP creates a common front door for digital products, documentation, onboarding and governance.”
That innovation has produced better experiences for the company’s partners. It saves time for Nationwide teams, partners, and customers. And it supports faster launch times for new products and enables growth across the business.
“EDP changed the model from fragmented, point-to-point integrations into an enterprise platform built around reusable digital products. That shift lets us support a range of integration options in one governed environment, meet partners at different stages of technical maturity, and add new capabilities over time without redesigning every relationship from scratch,” Carrel explains.
EDP uses cloud-native microservices, role-based access control, and advanced analytics. Nationwide IT created modular microservices to make EDP more scalable, resilient, and adaptable. And IT decoupled it from infrastructure-specific dependencies so that it would be a platform-agnostic developer portal. That, Carrel says, reduced operational constraints across environments.
Additionally, IT shifted from a user-specific model to role-based access, which improved security, simplified administration, and better served the needs of different audiences.
Meanwhile, robust analytics delivers visibility into platform usage and performance, which Carrel says helps ensure Nationwide continuously evolves the platform based on measurable outcomes.
The core platform is fully deployed, with Nationwide planning to expand it.
“Our Enterprise Digital Platform is more than a piece of technology,” Carrel notes, “it represents a strategic enabler to support growth objectives across Nationwide’s businesses.”
PITT Ohio fast-tracks shipment requests with AI assist
Organization: PITT Ohio
Project: No Touch Email (N@TE AI)
IT leader: Scott Sullivan, President and CEO (formerly CIO)
As PITT Ohio started its AI journey in 2024, the mandate was clear: Use the technology to solve “real problems,” says Ryan Carner, director of enterprise IT solutions.
“We wanted to hit the ground running and find a problem that was solvable,” Carner says, noting that the company also wanted to use the experience to build in-house AI skills. “The idea was to find a business case for AI that would be our first but not the only one.”
PITT Ohio leaders decided to tackle what Carner describes as a “mundane but very important task for how our business operates”: handling emails to the customer service team.
The need was significant. Customer service representatives were manually processing hundreds of pickup request emails daily, each requiring five to 15 minutes to interpret and re-enter shipment details into the company’s transportation management system (TMS). The emails were complicated, containing a lot of information submitted in nonstandardized ways and varying formats. This repetitive task consumed valuable time, introduced errors, and delayed customer response.
N@TE uses generative AI and natural language processing to transform unstructured email content into structured pickup orders automatically and in real-time. N@TE scans incoming emails, extracts key shipment data, and creates orders directly in the TMS via API integration. It operates seamlessly within existing workflows, requiring no change in customer behavior or retraining of staff.
PITT Ohio deployed N@TE in 2025, and the company also secured a patent for the product that year. N@TE has produced a 30-60X increase in processing speed, 99% accuracy in extracting and populating order data, and a 70% reduction in handling costs per pickup order.
SMU builds AI adoption through grassroots ambassador program
Organization: Southern Methodist University
Project: Scaling AI Without Scaling AI: Organizational AI Scaling Through Willingness
IT leader: Jason Warner, Associate CIO
Like executives in most organizations, leaders at Southern Methodist University encountered mixed attitudes about AI. Some workers had little interest in using the tech, others were afraid it would take jobs, still others were curious about what it could do.
Associate CIO Jason Warner and other leaders decided to leverage that last group, believing the best way to get SMU faculty and staff to embrace AI was to use enthusiasts to help smooth the way.
So, instead of treating AI as a conventional technology rollout, Warner and his colleagues built opt-in communities of practice known as the AI Coalition of the Willing and Operation Copilot.
The goal, Warner says, was to build institutional capability, reduce risk, and generate momentum.
“We knew the fastest way to scale AI was to scale the willingness of people to use the technology, and not talking to people about cost savings and the like,” Warner says, adding that willing users as great ambassadors and evangelists who showcase in formal and informal ways the technology’s potential for hesitant or skeptical colleagues.
Participating faculty members have access to a licensed ChatGPT account as long as they use it. Staff members have access to Copilot accounts after taking a self-paced training course and likewise must use it to keep that access.
Warner says these willing workers are demonstrating the benefits of AI (significant time reclamation, reduced cognitive load, improved quality of outputs, expanded professional capacity).
SMU is now moving to a single solution and scaling AI, confident that its use will deliver returns following in the footsteps of the early adopters.
Interested in meeting and learning from all CIO 100 winners? Join us next week at CIO 100 Awards & Conference in Frisco, TX. Limited seats remain! Register here