For small and midsize businesses (SMBs), AI is no longer optional. But for many leaders, the real challenge is making those investments pay off.
That’s where things often break down.
Too many AI initiatives start with tools instead of outcomes. They stay stuck in pilot mode. Or they deliver insights that never translate into action. In fact, according to the report, AI for SMBs: Closing the Readiness–Reality Gap, 70% of AI initiatives remain in pilot mode.
This is further proof that AI only creates value when it improves decisions that impact the business.
This guide outlines five drivers of AI value – business strategy, technology and data, AI experience, organization and culture, and governance – and explains how to apply them to achieve measurable results.
1. Business strategy: Start with outcomes
AI should support the outcomes your business cares most about. Whether that’s profitability, customer retention or faster product delivery, the goal is to align with outcomes that matter to your bottom line.
What to focus on:
- Define your top business priorities for the next 12 months.
- Identify where AI can help you move faster or smarter toward those goals.
- Align AI efforts to those decisions, not standalone use cases.
- Establish metrics or KPIs to assess the ROI of AI initiatives.
Without that alignment, even strong AI ambitions struggle to deliver impact.
Real-world example:
Reynolds Community College used SAS to align analytics with enrollment and retention goals. The result was its highest enrollment in six years and more than $1 million in savings in six months.
2. Technology and data: Build a usable foundation
AI depends on data, but not just any data. It needs data that is accessible, consistent and trusted across the organization.
Many SMBs still operate with siloed systems and fragmented data flows. That makes it difficult for AI to deliver reliable outputs at scale.
What to focus on:
- Improve data quality and consistency at the source.
- Ensure data is accessible across teams that need it.
- Invest in platforms that can scale with your business.
Real-world example:
The Sax Institute uses the SAS platform to protect sensitive data, keep it secure and well governed, while still enabling easy access for health analysts, epidemiologists and researchers. This trusted foundation allows teams to apply AI to real-world public health challenges with confidence and speed.
3. AI strategy and experience: Build capability with the right support
AI is not just a technology investment. It is a capability investment. Organizations must build internal expertise and, when needed, the right partners and support in place.
This includes software vendors who understand your business and offer direct support, as well as implementation partners to guide deployment and accelerate adoption.
What to focus on:
- Identify internal champions who can connect business needs with AI use cases.
- Invest in training and change management.
- Work with partners who can support implementation and long-term success.
The goal is to make AI usable – not just available.
Real-world example:
Brooks Rehabilitation used SAS to support its multimodal pain team. The result was a 50% to 70% reduction in opioid prescriptions, driven by data-informed care pathways and clinical dashboards. The organization shifted toward more personalized, data-driven patient care.
4. Organization and culture: Lead from the top
AI adoption doesn’t happen through technology alone. It requires leadership, alignment and a willingness to change how decisions are made.
If AI isn’t embedded into how teams operate, it remains underused, regardless of its potential.
What to focus on:
- Integrate AI into strategic planning and operational reviews.
- Incentivize innovation and data-driven decisions.
- Create space for experimentation and continuous learning.
Real-world example:
Seacoast Bank increased risk-adjusted revenue per customer by 30% by integrating SAS analytics into its customer engagement strategy. The bank’s leadership prioritized data-driven decision-making and saw immediate returns in marketing performance and customer value.
5. AI governance: Build trust and transparency
Trust is essential. AI must be secure, ethical and explainable. Without governance, AI can introduce risk rather than reduce it. Organizations need frameworks that ensure accountability and compliance.
What to focus on:
- Establish policies for data privacy and model transparency.
- Use platforms that support responsible AI by design.
- Monitor AI performance and retrain models as needed.
Real-world example:
Interamerican Insurance, the largest private insurer in Greece, used SAS to achieve GDPR compliance and build a trusted analytics ecosystem. The result was improved performance, reduced operating costs, and stronger customer trust across its life, health, and property insurance lines.
AI is not a one-size-fits-all solution
Really, it's not. But when AI aligns with business priorities and is supported by the right data, capabilities and governance, it becomes a meaningful driver of growth and efficiency.
The organizations seeing the most value from AI are not just adopting it – they are applying it to the decisions that matter most.
If you're ready to move beyond experimentation and build an AI strategy that delivers measurable results, our solutions are purpose-built for small to mid-sized businesses, backed by decades of experience and designed to scale with your business.
