Activities For Which Product Management Organizations Are Using GenAI Tools Forrester’s Portfolio Marketing And Product Management Survey, 2025 asked 285 product management decision-makers whose organization has implemented genAI or plans to do so how their organization uses genAI in its product development process. 43% use genAI in product opportunity identification and data analysis; 38% in detailed requirements generation and roadmapping; 37% in competitive intelligence gathering; 33% in user

Answers were: AI-infused applications, 41%; complete customer-facing applications — mobile and/or web, 39%; complete internal-facing applications — mobile and/or web, 38%; administrative apps for gathering, tracking, and reporting on data, 35%; business process/workflow applications, 33%; business analysis and reporting, 33%; core business applications, 30%; web and/or mobile frontends only, 28%; augmented/virtual reality apps, 26%; small automations, 23%; and IoT apps, 23%.

Figure 2 IT Spend: Application Development Versus Application Maintenance And Support This shows the IT spend of app development as well as app maintenance and support for consumer durables, diversified financials, government, insurance, and retailing. On average, ISG’s global benchmarking data respondents said they spent 49.5% of application spend on app development and 50.5% on app maintenance and support. This graphic has an associated spreadsheet that includes all data presented.

Figure 2 IT Spend: Application Development Versus Application Maintenance And Support This shows the IT spend of app development as well as app maintenance and support for consumer durables, diversified financials, government, insurance, and retailing. On average, ISG’s global benchmarking data respondents said they spent 47% of application spend on app development and 53% on app maintenance and support. This graphic has an associated spreadsheet that includes all data presented.

MAD Services Deliver Cool Products While Transforming Your Development Clients want more autonomy to better control their own innovation and development capabilities. Clients want to build modern, up-to-date custom applications leveraging all the latest and greatest technologies, including AI and generative AI (genAI). For a more precise market definition for MAD services, see The Modern Application Development Services Landscape, Q3 2024.

Some even used them to modernize applications from legacy to cloud native (26%) (see Figure 11). While TuringBots are not mature enough to support the full software development lifecycle (SDLC), some are mature enough to help coders deliver their work more effectively and efficiently. When properly handled with sufficient guardrails, TuringBots are valuable tools in a developer’s arsenal.

AI and genAI MAD services present a bigger opportunity to increase cross-departmental collaboration and efficiency as knowledge, processes, and best practices are shared through large language models and genAI architectures. Primary challenge. Providers are challenged to cocreate modern custom apps with clients and enable them to be self-sufficient.

Figure 2 IT Spend: Application Development Versus Application Maintenance And Support This shows the IT spend of app development as well as app maintenance and support for consumer durables, diversified financials, government, insurance, and retailing. On average, ISG’s global benchmarking data respondents said they spend 42% of application spend on app development and 58% on app maintenance and support. This graphic has an associated spreadsheet that includes all data presented.

But industries vary: Enterprises in the healthcare equipment and services industry lead with an average of 32 releases per supported application per year, while those in business services and supplies average 24. Application testing is low — and much lower in Europe than globally. The application testing rate measures the percentage of app development testing and quality assurance that’s performed through automated processes.

which were complete customer-facing applications — mobile and/or web, 36%; complete internal-facing applications — mobile and/or web, 35%; AI-infused applications, 35%; administrative apps for gathering, tracking, and reporting on data, 34%; business process/workflow applications, 31%; business analysis and reporting, 31%; core business applications, 28%; web and/or mobile frontends only, 26%; AR/VR apps, 26%; small automations, 24%; and IoT apps, 19%.