Forrester’s Responsible AI Solutions Landscape Is Coming!

Forrester’s State Of AI Survey, 2025 reveals a surge in AI deployment across organizations: 78% of AI decision-makers report their organization already has generative or predictive AI in production. Yet this momentum masks deeper strategic gaps. One of the most glaring gaps is poor AI governance and risk management. If unattended, this gap can only grow as new regulations, class‑action activity, and public scrutiny increase.

The good news is that software solutions are available to help technology leaders and their organizations design, execute, and optimize the processes needed to close this gap. The bad news is that the current state of the market for these solutions is becoming quickly crowded with rapidly emerging vendors, with messaging that’s difficult to decipher and many different offerings all labeled as “AI governance.” To help technology leaders and their peers navigate this market and identify the type of capabilities they need in the context of specific AI use cases, Forrester will publish a Landscape report on responsible AI solutions in Q2.

Forrester defines responsible AI (RAI) solutions as software ensuring that organizations’ AI models and systems are explainable, accountable, and trustworthy.

This definition reflects what leading enterprises now recognize as essential for safe, fair, and trustworthy AI. The research will support technology leaders, along with their peers in risk, in two key ways:

1. Redefining RAI Through Three Critical Components

First, we ground responsible AI in three essential pillars:

Explainability. This pillar includes transparency, traceability, observability, and interpretability.
Accountability. Accountability ensures that organizations can identify, manage, and mitigate AI‑related risks, including regulatory risks. It also promotes clear mechanisms to determine who’s responsible for given outcomes.
Trustworthiness. This pillar is rooted in core trustworthy AI principles such as fairness, robustness, and human oversight.

This expanded definition reflects the multidimensional nature of RAI and provides leaders with a more actionable foundation for their strategies.

2. Clarifying An Overcrowded And Fast‑Changing Market

We help potential buyers focus on the capabilities that matter the most for their use cases by:

Providing an overview of critical capabilities. We’ll highlight what leaders need to govern AI across multiple AI models and systems.
Detailing relevant use cases. We’ll help leaders connect capabilities with business needs and the underlying use cases.

The result is a practical guide for identifying the right solutions, avoiding fragmentation, and building a cohesive RAI technology stack.

If you’d like to discuss your RAI strategy or the upcoming research, please get in touch! Clients can reach out or schedule a guidance session with me anytime.