Reliable AI requires data prep, governance and training – but where do you begin?

The transformative potential of AI to fundamentally reshape how businesses operate offers unprecedented opportunities to leverage new innovations to enhance efficiency, automation and decision-making and drive growth. 

But achieving this potential demands clarity amidst chaos. Many businesses—especially small to mid-sized businesses (SMBs) — are hindered by fragmented systems, inconsistent data and unclear governance processes.

Research from SMB Group’s Technology Buying Journey highlights that one key benefit of technology is access to better decision-making data. Leveraging this data can unlock benefits beyond AI, such as improved analytics and planning capabilities. Even businesses not yet ready to implement AI will find that cleaner, more accurate data enhances their existing analytics and reporting, helping them make faster, more informed decisions such as areas to invest that will help them grow.

Taking care of data first is essential. AI amplifies the importance of data integrity, hygiene and governance. Reliable AI outcomes depend on high-quality data, and businesses that prioritize these foundational steps set themselves up for success. Your data should drive your AI — and, ultimately, your success.

For SMBs, this foundation must be carefully constructed through data preparation, governance and training practices. But this raises a key question: with the proliferation of data across organizations, where do you start? What does AI readiness look like?

To lay a solid foundation for AI implementation and success, SMBs should take the following four steps to unify, prepare and manage their data.

Step 1 – Unify disparate data

One of the biggest obstacles for SMBs is data silos. When data is scattered across disconnected systems, businesses struggle to view their operations comprehensively. This lack of cohesion limits their ability to make informed decisions and build reliable AI models. The first step in any AI journey is unifying data into a centralized platform. By consolidating information from multiple systems, SMBs can create a single source of truth that enhances visibility and reliability. Cloud-based ERP systems like Acumatica are designed to prioritize customer control and ownership, offering a future-ready foundation where AI yields a tangible return on investment.

For example, Dukathole Group, a growing brick manufacturing company, leveraged cloud ERP technology to unify data across six divisions that previously relied on separate financial systems. With real-time access to comprehensive data, Dukathole successfully integrated and profited from three acquisitions, improved inventory management and streamlined production data to better manage cost centers and boost profitability.

The company also expanded into the construction industry using robust project management functionality. This expansion enabled Dukathole to build houses on time and budget, address local housing shortages and create over 450 jobs. Dukathole’s personalized dashboards now provide flexible reporting and real-time insights across its eight branches, allowing leaders to identify challenges and make informed decisions quickly, setting the stage for scalable growth and operational efficiency. Companies like Dukathole that unify high-quality data lay the groundwork for future AI initiatives that drive predictive insights and automation, further transforming their business.

Step 2 – Cleanse and prepare the data

Even with unified data, AI outcomes can falter if the data is inaccurate or incomplete. The IBM® Data Differentiator reports that as much as 68% of organizational data never gets analyzed, meaning businesses only tap into a small fraction of their data. AI models rely on high-quality data to deliver actionable insights. Using the right ERP system can provide the mechanisms to capture clean data – AI-enabled ERP systems can detect irregularities and help keep data clean. The following three steps will help SMBs ensure their data is high quality and ready to generate results.

1. Eliminate redundancies: Use tools like deduplication software to remove duplicate customer records. For instance, consolidating “Jane Doe” with two different email addresses into a single record ensures consistent reporting and customer communications.
2. Fill in the gaps: Address missing data points with third-party enrichment tools. For instance, if customer profiles are missing contact information, CRM capabilities can identify and help you fill those gaps.
3. Conduct regular audits: Establish quarterly review processes where data quality champions validate datasets, flag discrepancies and clean up inconsistencies. Audits will be further enabled by AI capabilities.

Step 3 – Implement robust data governance

Once you’ve unified and cleaned your data, the next step is to develop a strong governance framework. Governance entails a commitment to transparent, user-friendly AI tools so businesses can maintain complete control of their data while driving powerful results.

But what makes data governance so critical to AI enablement?

• Consistency: Governance processes, such as version control, prevent drift in data quality. For instance, implementing standardized naming conventions and workflows for product stock-keeping units (SKUs) ensures that an AI-driven inventory management system can accurately track and reorder stock across multiple locations without errors.
• Compliance: Automated workflows that validate data regulation requirements help businesses prevent costly missteps. For example, an AI-powered customer insights tool can flag and anonymize sensitive personal data, ensuring compliance with industry regulations while allowing businesses to extract valuable insights from the data.
• Trust: Reliable data builds confidence among business leaders and stakeholders, improving results. An AI-powered demand forecasting tool relies on accurate historical sales data to provide actionable predictions. Foundational to trust in the AI era is a commitment that business data is not given to third parties. When governance ensures the data’s integrity, leaders can trust AI’s output to make important staffing and inventory decisions.

To tackle data governance, SMBs should assign clear ownership for data updates and maintenance and use automated tools to identify errors in real time. Identifying a “data steward” can ensure businesses are accountable for their data governance practices. AI-powered automated tools will help in this area. By establishing governance policies that adapt to changing business needs and incorporating governance early, businesses ensure that their AI initiatives and broader data-driven strategies can scale.

Step 4 – Train AI models with tailored data

Generic AI models often fail to meet a company’s specific needs. To achieve meaningful results, AI must be trained on datasets that reflect the business’s unique challenges and goals. To develop tailored AI training, SMBs should focus on use case-specific datasets to ensure AI learns within the right context. For example, a retail business could train AI models on historical sales patterns, customer demographics and seasonal trends to optimize inventory management and reduce stockouts.

Many companies, particularly small businesses, sometimes lack the resources to manage AI development internally. This is where software vendors like Acumatica can provide industry-specific AI best practices and prebuilt models that can provide ongoing support to reduce the burden on internal teams. These partnerships also help organizations incorporate safeguards to avoid bias in AI models by ensuring diverse datasets are used during training, enabling the technology to deliver equitable and accurate outcomes.

Incorporating real-world scenarios into AI training that mirrors unique business operations and priorities ensures that SMBs maximize their ROI and leverage the tool properly. An example is Acumatica’s anomaly detection capabilities, built on industry-specific datasets, which enable SMBs to automate error detection and identify trends tailored to their unique operations.

The broader value of data foundations

At Acumatica, we approach AI innovation with guiding principles designed to empower SMBs. Our responsible approach to AI, outlined in our Principles of Innovation, emphasizes the practical implementation of AI capabilities to ensure that technology doesn’t just solve abstract problems but aligns with real-world needs.

While AI adoption may be the long-term goal, the foundation built today — unified, clean and well-governed data — ensures your AI investment will pay off tomorrow and into the future.