While some companies are just starting to adopt artificial intelligence (AI), others that have already adopted generative AI assistant tools might be evaluating their next move. These organizations don’t yet employ more advanced agentic AI systems. Agentic AI systems involve multiple AI agents working together across end-to-end workflows, coordinating tasks, decisions, handoffs, and monitoring within defined guardrails. This type of AI deployment overcomes data silos, which persist when the organizations’ AI tools don’t connect. This article will focus on steps leaders can take as they move from planning to execution in the next phase of AI.
When aiming for the appropriate use of AI in their companies, leaders should consider that:
- AI strategy works when it connects business outcomes, data foundations, operating models, and AI governance.
- If the organization is still early in adoption, this can be a time when gaps between ambition and readiness surface.
- A short, step-by-step road map can help reduce “pilot purgatory” and prioritize use cases with measurable return on investment.
Why AI Strategy & AI Maturity Should Advance Together
Technology transformation has emerged as the top strategic priority for U.S. business leaders across industries and company sizes, as organizations shift from strategy design to execution, according to the C-Suite Barometer: Executive Leadership Insights in the US from Forvis Mazars. The research also shows that C-Suite executives are increasingly spending more on AI implementation as they move from experimentation to scale. Nearly seven in 10 U.S. executives say AI is having a major impact on their company, and nine in 10 U.S. companies have restructured teams to implement AI, signaling a shift toward execution and operating model change rather than isolated experimentation.
Technology transformation is firmly in execution mode, with AI being scaled across core functions and teams being restructured to support it.
What Is an AI Strategy?
An AI strategy is a practical road map that connects high‑value AI opportunities to meaningful business outcomes. It clarifies what problems AI should solve, what data and technology are required, and who is accountable for driving results. A strong strategy also defines governance, risk controls, and success measures so organizations can move confidently from experimentation to scaled, responsible use.
Although closely related, a key distinction to note is between AI strategy and AI implementation. While the two are closely connected, they serve different purposes:
- AI strategy outlines what the organization aims to achieve and why. It provides the framework to assess readiness, establish an operating model, set governance expectations, and support alignment with the company’s broader vision.
- AI implementation focuses on how to bring that strategy to life. This includes developing a delivery road map, integrating AI into workflows, managing change, measuring outcomes, and improving solutions over time.
Together, strategy can provide the direction and guardrails, while implementation helps drive execution and adoption.
The Readiness Gap: Why Many Organizations Stall in “Pilot Purgatory”
In the 2026 Financial Executives Priorities Report, developed by Forvis Mazars and the Financial Education & Research Foundation (FERF)—the independent nonprofit research affiliate of Financial Executives International (FEI), findings show that many companies have adopted AI, with 88% of organizations in 2025 reporting they regularly use AI in at least one business function. However, companies often become stuck in the pilot phase. Only 15% of organizations said they were fully prepared to support advanced analytics and AI initiatives, while 51% were either not prepared or only somewhat prepared, often due to foundational data issues and infrastructure gaps.
“You have to start with the foundation. You have to have very good clean data and you need to know where it all is in order to really push forward with a real transformation.”
CFO, exercise equipment and connected fitness company
Here are questions to help preclude pilot purgatory:
- Do you have a prioritized short list of use cases tied to outcomes?
- Do you know what data is required and where it lives?
- Do you have a policy for sensitive data and vendor tools?
- Do you have project success metrics and owners documented?
- Do you have a plan for skills, training, and workflow change?
AI Strategy Framework
These six steps can help your organization adopt a successful strategy.
Step 1: Start With AI Governance & Controls
According to the C‑Suite Barometer, while AI adoption is widespread, seven in 10 U.S. executives continue to express ethical concerns. However, the percentage of respondents reporting “major” concerns fell from 50% in 2024 to 36%, suggesting improving AI governance and risk maturity.
Complementing this view, the Financial Executives Priorities report finds that finance leaders’ top concerns when deploying generative AI or large language models (LLMs) center on hallucinations or inaccuracy and data privacy or leakage.
Step 2: Define Business Outcomes & Value Streams
Prioritize outcomes that leaders care about, such as decision speed and accuracy, efficiency, forecasting, customer experience, and risk reduction. C-Suite leaders cited better decision making and strategic positioning as top reasons for adopting AI.
Step 3: Select Use Cases That Match AI Readiness
According to the Financial Executives Priorities report, finance leaders most often cite AI use cases tied to productivity and decision support, including content drafting and document review, automation of accounts payable (AP) and accounts receivable (AR) processes, basic forecasting and analytics, and the development of internal knowledge bases. The same survey shows that ChatGPT and Microsoft Copilot are the most widely adopted internal AI tools among finance organizations, reflecting an initial focus on practical, near‑term value rather than more advanced deployments.
Step 4: Build the Foundation (People, Process, Technology, Data)
Many organizations are returning to a People-Process-Technology framework to make sure new tools are matched with the right skills, redesigned workflows, and a culture that can embrace change. Companies are increasingly adding Data as a fourth pillar, highlighting its critical role for reliable machine learning and AI output.
Step 5: Define the AI Operating Model (Ownership, Decision Rights, & Delivery)
With AI being scaled across core functions, many organizations are restructuring teams to support it. As AI moves into execution mode, an effective operating model typically brings together a steering committee, business process owners, and IT and security leaders, supported by a clear intake and prioritization process.
Step 6: Implement in Waves, Measure, Then Scale
Key performance indicators (KPIs) for measuring AI success include cost savings or operational efficiency gains, employee productivity or engagement, and measured attention to customer or client satisfaction improvements. Scaling depends on tracking value and updating controls so AI usage can expand.
Finance & AI Strategy
According to the C-Suite Barometer, U.S. executives often cite AI implementation as a top area for increased financial investment. Nearly 75% report using AI for cybersecurity, automation, customer experience, and forecasting. One in five U.S. companies now spends more than 20% of its budget on AI, exceeding the global average of 10%. The heightened AI use has prompted the need for AI strategy consulting, which often includes an AI readiness assessment, use-case prioritization, data and architecture planning, governance design, and an implementation road map.
As AI investment accelerates, business leaders are increasingly focused on how these capabilities translate into operational impact. The Financial Executives Priorities report shows that while revenue growth and cost optimization remain core priorities, technology enablement has surged in importance, with implementing new financial technology ranking among the top priorities for 2026. These priorities reinforce how organizations are scaling AI from isolated efficiency gains into foundational capabilities that support sustained business performance.
How Forvis Mazars Can Help
If your organization needs to create a strong AI strategy, governance framework, or other capabilities critical to the safe and effective deployment of AI, professionals at Forvis Mazars can help. We can assist with road map development aligned to business outcomes and controls; readiness assessments of people, processes, technology, and data; governance design that addresses accuracy, privacy, and cybersecurity considerations; and implementation waves guided by AI accelerators and KPI measurement. Connect with us today to ask your questions or jump-start your next AI project.