In the report, “Frontier firms” are organizations that are furthest along in their AI maturity and are treating AI as a core, enterprise-wide capability rather than a set of isolated experiments. IDC classifies companies into three groups—Frontier firms, neutral, and laggards—based on several factors: - How widely they use generative AI (GenAI) - How widely they use agentic AI - How many parts of the business are meaningfully impacted by AI (for example, products, customer engagement, operations) - Whether they are already monetizing GenAI - How advanced they are in responsible AI (RAI) practices What sets Frontier firms apart: - They integrate human expertise, data, technology, and governance into a coherent AI strategy. - They adopt AI-first strategies and redesign business processes to align with business goals and new business models. - They move beyond basic productivity use cases into functional and industry-specific applications. - They are early adopters of agentic AI in areas like product development, customer support, and customer engagement. A few data points from the study: - 98% of Frontier firms already use GenAI, compared with just 31% of laggards. - 84% of Frontier firms feel well prepared for AI, versus only 19% of laggards. - Frontier firms use AI broadly across IT (78%), product development (75%), cybersecurity (75%), and customer service (74%), while laggards report single‑digit adoption in most functions. - Only 22% of organizations worldwide qualify as Frontier firms, which shows there is still a large opportunity for others to catch up. In short, Frontier firms are not just using AI tools; they are reimagining how their business works with AI at the center.

The research shows that both generative AI (GenAI) and agentic AI are moving from experimentation into broader, more strategic use. GenAI usage and trends: - 68% of all respondents already use GenAI. - Another 26% are not using it yet but plan to within 12 months. - Organizations started with task automation and individual productivity, but these are now seen as table stakes. - The focus is shifting toward functional and industry-specific use cases that can reshape products, services, and business models. Where GenAI is used across the business: - Customer service, marketing, and IT are currently the leading functions, with over 40% of organizations using GenAI there. - Among Frontier firms, over 70% use GenAI in customer service, marketing, IT, product development, and cybersecurity. - 97% of Frontier firms use GenAI in two or more business functions, compared with only 18% of laggards. - On average, Frontier firms use GenAI in seven business areas. Types of GenAI use cases: - Productivity use cases: Helping individual employees work faster (for example, drafting content, summarizing information, or analyzing documents). - Functional use cases: Embedding GenAI into functions like marketing, sales, IT, and supply chain. - Industry use cases: Creating new business models, products, or services tailored to specific industries such as retail, manufacturing, or healthcare. Agentic AI usage and trends: - 37% of respondents currently use agentic AI. - Another 25% are experimenting with it, and 24% plan to use it in the next 24 months. - Today, use is modest across functions, but nearly three times as many organizations plan to use agentic AI in key business lines over the next two years. - Frontier firms are ahead of the curve, using agentic AI more actively in customer service, cybersecurity, product development, and sales. Overall direction: - Organizations are expanding from prebuilt GenAI applications (40% today) toward customized or custom-built GenAI solutions (expected to reach 70% in the next 24 months). - Business functions are gaining more influence over AI budgets, with about 44% having complete or shared ownership. - There is a clear shift from using AI just to “do the same work faster” to using AI to rethink how functions operate and how revenue is generated.

The study indicates that most organizations are already seeing tangible value from GenAI and early value from agentic AI, but they are also running into governance and risk challenges as they scale. Business value and ROI from GenAI: - 68% of respondents use GenAI, and most report positive returns. - Average ROI for GenAI users is 2.8x, with payback in about 15 months. - Frontier firms see even stronger performance, with an overall AI return of 2.84x compared with 0.84x for laggards. - Organizations are not only looking at financial ROI. They also report: - 51%: improved accuracy and consistency - 45%: time savings - 40%: enhanced customer experience Business value and ROI from agentic AI: - 37% of respondents currently use agentic AI, with many more piloting or planning adoption. - Agentic AI users report an average ROI of 2.3x, with payback in about 13 months. - Even in early pilot phases, agentic AI is already reshaping how industries operate, especially in areas like product development, customer support, and engagement. - The report expects organizations to see more value from agentic AI as adoption and maturity grow. Top-line and bottom-line impact: - Organizations are using GenAI to both grow revenue and reduce costs. - 44% of surveyed organizations plan to monetize industry-specific GenAI use cases within 24 months, signaling a move toward more differentiated, domain-specific applications. - Among Frontier firms, 67% are already monetizing or using GenAI to boost revenue. - AI leaders report benefits beyond productivity, including brand differentiation, cost efficiency, top-line growth, and better customer experience. Key challenges and risks: - Nearly 30% of organizations cite security, privacy, governance, and compliance as the top barriers to scaling AI. - Over 75% rate transparency as very or extremely important, highlighting the need for explainability and human oversight. - As GenAI deployments mature, ROI can plateau because projects become more complex and expectations rise, making governance, data quality, and responsible AI practices more critical. In practice, this means that to capture sustained value, organizations need to: - Invest in robust security, privacy, and compliance frameworks. - Put responsible AI governance in place, with clear policies and human oversight. - Prioritize transparency and explainability, especially as they adopt more advanced agentic AI. - Shift spending toward tailored, higher-impact use cases rather than only generic productivity tools.