This downloadable PowerPoint template of Forrester’s Business Innovation Canvas Framework provides technology and business leaders with a structured approach to identifying new technology-enabled digital experiences for customers and employees, products and services, and business strategies.

Early adopters of adaptive process orchestration (APO) are looking for the best buying strategies, given their available budget, scaling appetite, and other relevant criteria. In our ongoing coverage of the APO market, we’ve seen a trend toward progressive pricing models that address buyers’ preferences. To help buyers, this report describes vendors’ pricing approaches and model types, considers deal sizes, and offers practical budgeting and vendor selection guidance.

You can use adaptive process orchestration (APO) software to streamline business operations across the organization and consolidate heterogenous and overlapping automation tools. But to realize these benefits, you’ll first have to select from a diverse set of vendors that vary by size, type of offering, geography, and use case differentiation. Technology leaders should use this report to understand the value they can expect from an APO vendor, learn how vendors differ, and investigate options based on size and market focus.

Adaptive process orchestration is a step forward in the modern automation journey, fueled by AI and leaders’ appetite to automate more of their processes. The challenges for EA leaders? Building the tech stack necessary for successful process orchestration — with strong foundations, a unified design environment, multiple technologies, an orchestration engine, and myriad endpoints — while ensuring adaptability and resilience. This report helps EA leaders understand the fundamentals of adaptive process orchestration for complex business operations.

Adopting generative AI to become more productive and more innovative won’t work if you only focus on identifying use cases and how you can apply AI to them. As with any other technology, adopting AI to drive better business outcomes requires mastering five competencies. In this report, we apply Forrester’s innovation framework to the challenges of AI adoption to help tech leaders innovate — and show that an AI strategy, a supportive culture, the right organizational structure, and ecosystem support are just as important as the technology.

G‑Research began its AI empowerment journey 18 months ago and has widely embedded genAI tools in its workflows. Survey data and interviews show that agentic AI in the software development lifecycle (SDLC) boosts efficiency, productivity, and job satisfaction. But AI delivers limited benefits in large, complex domains. The case study explores how G‑Research balances productivity gains with human oversight, experimentation, and governance. Technology leaders can learn from G‑Research’s efforts and transition from AI‑driven efficiency to greater effectiveness and innovation by using the right AI for the right use cases.

The convergence and overlap of software development platforms have blurred traditional boundaries, making it difficult for firms to distinguish between tools once meant for specific roles or tasks. While this overlap gives buyers power and flexibility, it also creates confusion, as different platforms can support creating general business apps and process orchestration. Understanding the five primary types of software development platforms — agentic software development, app generation (AppGen), digital process automation (DPA), adaptive process orchestration (APO), and AI — helps enterprise leaders align tools with their software development goals.

Predictive AI has been integral to innovation management for a long time in different areas, particularly trend and pattern recognition. Adding other types of AI to the mix creates new opportunities for innovation leaders. GenAI’s creative and agentic orchestration capabilities suggest a smarter, faster, and largely automated innovation lifecycle — one that is moving from promise to reality. While high-innovation, low-regulation industries lead, AI-powered innovation is also being used for highly complex and sensitive use cases. We show how AI reshapes the innovation process, with examples of how companies use it to bring new products to market more quickly and efficiently.

Companies continue to struggle to build sustainable innovation programs. While they may have an innovation center, dedicated managers, or even a chief AI officer, many find it difficult to realize real business value from their innovation efforts. This report outlines why and how the role of the chief innovation officer can help tech executives ensure that their innovation investments pay off.

Members of Generation Z (Gen Z) are now entering the workforce with new desires and expectations. In this report, we uncover how Gen Z’s specific attitudes and preferences affect their contributions to ideation and innovation. Tech leaders and people managers should use our recommendations to ensure they leverage Gen Z’s huge innovation potential.