Organizations getting real value from AI are not simply investing more. They’re building stronger foundations across strategy, data, governance, adoption and measurement. Here’s what successful AI leaders do differently.  

Most SMBs are investing in AI, running pilots and experimenting with new use cases. Yet only 9% have fully embedded AI into strategy, operations and decision making. While 70% remain in early stages of AI adoption and maturity, the challenge is rarely a lack of technology. For growing organizations, the lack is accountability, coordination and execution. The ones pulling ahead are not simply adopting more AI. They are building the leadership, governance and foundations needed to turn AI activity into measurable business impact.

Make someone accountable for AI outcomes 

Many leadership teams assume AI is simply an IT initiative. While it’s true that IT and the business are more connected than ever before, SAS and IDC research conclude that AI readiness is a shared business responsibility requiring alignment across:

• Strategy.
• Data.
• Governance.
• Business processes.
• Workforce capabilities.
• Measurement and ROI.

The accountable leader will vary, especially across industries, but the mandate should not. AI leaders' mandate should focus on strategy, implementation and transformation, including:

• Setting the business outcomes that AI should support.
• Prioritizing and funding the right use cases.
• Establishing expectations for data, governance and risk.
• Coordinating adoption across business and technology teams.
• Measuring results and deciding what to scale.

AI does not need to be the AI leader’s responsibility, but accountability for AI outcomes must be an explicit part of the role. Having someone in place who owns the journey from experimentation to business impact is key.

Focus AI on business outcomes that matter most 

According to research, nearly 45% of SMBs have no structured view of AI use cases. Without a structured view of AI activity, initiatives compete for the same resources, successful pilots stay isolated, and leaders cannot compare results. The organization becomes busier with AI without becoming better at it.

For SAS Partner, DJ Penix, President and CEO of Pinnacle Solutions, the leadership challenge is moving beyond experimentation to create an integration foundation for repeatable, measurable value.

This research illuminates the reality many SMBs face: moving from AI pilots to real impact requires moving beyond experimentation and building an integrated foundation that supports your data and business needs. This is what delivers repeatable, measurable value. DJ Penix President & CEO Pinnacle Solutions 

Build trusted data and governance foundations

The business impact grows with maturity. SMBs with fully integrated AI strategies and governance are twice as likely as less mature organizations to report improvements of more than 40%, reflecting the cumulative value of stronger foundations and more disciplined execution.

AI leaders not only help identify high-value use cases but also build supporting capabilities that enable those use cases to be repeated, expanded and embedded across the organization. The behaviors that separate leaders from the rest are consistent across the data and cluster across four dimensions:

Planning: connect investments to business value

• Identify the business problem.
• Define the strategy and decision criteria.
• Fund AI as a capability, not just a pilot.
• Building: create trusted foundations.
• Get your data house in order.
• Establish governance and clear ownership.
• Build integrated environments that grow with the business.

Enabling: build confidence and adoption

• Develop internal skills across business and technology teams.
• Provide clear guidelines for responsible use.
• Use partners to fill skills gaps.

Executing: measure impact and scale what works

• Manage use cases as a portfolio.
• Embedded successful AI into business processes.
• Measure impact and use results to guide investment.

The leadership mandate becomes practical when it is translated into five connected decisions: strengthen data foundations, define business outcomes, establish the strategy, enable adoption, and measure value. These decisions form the basis of the geode, 5 steps to turn AI into impact.

Choose platforms and partners that accelerate execution

SMBs cite governance, data quality, integration, execution, and ROI tracking as top AI readiness challenges. With such a tall order, AI leaders can’t solve these readiness gaps alone. The most successful leaders,

Look for a platform that can:

• Connect trusted data, analytics and AI.
• Apply governance consistently across data and AI.
• Provide end-to-end traceability, auditability and control.
• Integrates with existing systems.
• Support AI from experiment through operational use.

Look for partners that can:

• Align AI initiatives to business priorities.
• Accelerate implementation and time to value.
• Build internal skills and support adoption
• Embed AI into everyday business processes.
• Help measure and demonstrate business impact.

This is where SAS and our Partners can help. SAS brings data management, analytics, AI, governance and decision support together so organizations can create a trusted path from data to insight and from decision to measurable outcome. Technology and implementation partners can complement our platform with industry expertise, deployment support, services, change management and skills development.

Define success before deciding what to scale

AI success is not determined by how many pilots you launch. It’s determined by how effectively you align data, strategy, adoption and measurement to the outcomes that matter the most. The leaders pulling ahead are building the foundations, accountability and measurement practices that support this.