Last Updated: 2/17/2026
As artificial intelligence (AI) and automation continue to reshape business operations, organizations are exploring how to adopt these technologies in a responsible and strategic manner. A recent webinar hosted by professionals at Forvis Mazars provided practical insights into evaluating AI readiness, applying recommended practices, and navigating the full AI life cycle—from ideation to production—with an emphasis on ethical stewardship. These concepts are critical as organizations continue to implement AI.
AI readiness begins well before formal governance structures or risk controls are put in place. It starts with aligning business strategy, data capabilities, operating models, and organizational skills so AI initiatives can move from experimentation to value quickly. Governance, risk management, and compliance become increasingly important as AI scales, but these are most effective when built on a strong readiness foundation established early in the AI life cycle.
Laying the Foundation for AI
A solid foundation begins by forming a cross-functional steering committee that brings together professionals from IT, business operations, legal, and compliance. Then, data readiness and AI governance are additional layers to consider, as AI systems are designed to operate using structured and unstructured data.
The foundational component of data readiness operates as a two‑way street. AI initiatives place new demands on data quality, accessibility, and governance, while existing data limitations often shape what AI can realistically deliver. Organizations that address data management and AI readiness together are likely to be better positioned to scale initiatives responsibly.
Next, introduce a governance framework that can help guide your organization’s AI strategy and integration. Risk management and compliance frameworks are evolving to address AI-specific risks such as hallucinations, bias, and data leakage. Regulatory standards such as HIPAA, the New York Department of Financial Services (NYDFS), and the Federal Financial Institutions Examination Council (FFIEC) remain applicable regardless of whether data is processed through AI or traditional systems.
The NIST AI Risk Management Framework was referenced as a practical tool for mapping, measuring, and managing AI-related risks. It is intended to assist organizations in identifying legal obligations, evaluating third-party vendor risks, and applying controls that promote transparency, accountability, and fairness.
Understanding the Full AI Life Cycle
The AI life cycle spans ideation, development, evaluation, deployment, and ongoing oversight. AI readiness is most critical in the early stages of this cycle, when organizations are identifying high‑value use cases, assessing data and technology capabilities, aligning stakeholders, and establishing operating models. As AI solutions move closer to production, governance, risk management, and monitoring practices become essential to sustaining trust and performance over time.
Agentic AI (which refers to systems that can plan and execute multistep tasks with contextual awareness) is a key goal for many organizations. Examples include chatbots, scheduling tools, and more complex agents. These agents can be developed using platforms like Microsoft Copilot Studio, Workato, and N8N, demonstrating that technical expertise is not essential. Achieving effective outcomes relies on collaboration between domain experts and technologists, thoughtful prompt engineering, and iterative testing.
Sustaining AI Through Recommended Practices
To sustain AI solutions and maintain long-term AI readiness, organizations are encouraged to focus on continuous monitoring, employee training, and ethical oversight. Periodic assessments help detect bias and validate outcomes. Maintaining detailed records of AI models and data sources supports transparency, while training programs assist employees in understanding both technical risks and behavioral considerations.
The rise of shadow IT (unauthorized use of AI tools such as ChatGPT or Sora) highlights the importance of clear policies and proactive governance. Educational initiatives should highlight the risks of using public AI platforms for sensitive data and encourage responsible usage among teams.
How Forvis Mazars Can Help With AI Adoption
To position your organization as a responsible steward of AI, begin with data readiness as an initial step. Next, leverage frameworks like the NIST AI RMF and engage stakeholders across departments to promote responsible usage among teams. Remember, human oversight is essential even as automation expands. Organizations are encouraged to adopt AI purposefully and strategically, fostering a culture of ethical innovation and continuous improvement.
Our teams at Forvis Mazars help organizations assess where they are in their AI journey, identify practical use cases, evaluate data and technology readiness, and design operating models that support responsible AI adoption from the start. Learn more about our AI Strategy & Integration services and how we help your organization move from AI readiness to responsible, scalable adoption. Connect with us today to ask your questions and get started on your next project.