Your Salesforce AI strategy depends on data you can trust

AI is central to Salesforce initiatives such as Agentforce, analytics, automation, reporting, and customer engagement. As evaluate these capabilities, though, strategy discussions are quickly turning to data quality, governance, ownership, and readiness.

Every AI-generated recommendation, workflow action, report, insight, and customer interaction depends on underlying data, and the quality of those outcomes depends on the quality of that data.

Executives are focused on what AI can deliver: faster decisions, better customer experiences, greater efficiency, and clearer visibility across the business. Technology and operations leaders, in turn, must determine whether their data environments can support those outcomes consistently. The shared objective is a foundation for trusted, scalable results.

Data readiness is one of the clearest predictors of AI success. Organizations that strengthen their data foundation now are better positioned to scale automation, improve analytics, and realize more value from AI.

Salesforce is building toward connected intelligence

In this release, Salesforce continues investing in capabilities that bring data, analytics, automation, and AI closer together. Data 360 (formerly Data Cloud), Agentforce, reporting enhancements, and workflow intelligence all reflect a broader strategy centered on connected business.

The vision is compelling: customer information, operational data, service history, workflow activity, and business insights become more accessible across the platform. Moreover, teams gain greater visibility into the relationships between people, processes, and outcomes. In addition, AI experiences become more relevant because they have access to richer context.

The opportunity, thought, extends beyond technology. Connected intelligence creates value when employees can make decisions faster, resolve customer issues more efficiently, identify trends sooner, and act with greater confidence. Those outcomes require data that is accurate, accessible, and consistently understood.

This is why many organizations discover that their AI strategy quickly becomes a conversation about data strategy.

Data challenges become more visible as AI adoption expands

Most organizations have spent years building systems, integrations, reports, and processes designed to support business growth. Those investments create tremendous value, but they also introduce complexity.

• Customer information may exist across multiple systems
• Different teams may maintain their own definitions for key business metrics
• Data ownership may be distributed across departments
• Reporting processes may rely on information from several sources that evolve independently over time.

These conditions exist in organizations of every size and across every industry.

As AI becomes embedded in daily workflows, these issues become harder to ignore. Recommendations, summaries, insights, and automated actions are only as reliable as the data behind them. The more decisions influenced by the platform, the more important consistency becomes.

We often see organizations begin an AI initiative and quickly uncover opportunities to improve customer identity management, data governance, reporting standards, integration architecture, and stewardship practices. Those discoveries are valuable because they strengthen the foundation supporting future innovation.

Customer identity deserves special attention

One of the most common readiness challenges involves customer identity.

Many organizations maintain customer information across CRM platforms, ERP systems, service applications, billing environments, marketing tools, and industry-specific platforms. Each system serves an important purpose, while also having its own processes for creating, updating, and maintaining records.

Over time, those differences create familiar problems: duplicate records, inconsistent customer attributes, extra reconciliation work, and unreliable reporting caused by multiple versions of the same customer across systems.

Salesforce’s continued investment in Data 360 highlights the importance of establishing a more unified view of business data. Connected customer profiles support better analytics, more effective workflows, and more informed AI experiences because they provide a more complete picture.

Organizations preparing for expanded AI adoption need to understand how customer identities are created, maintained, governed, and connected across systems. This assessment often reveals some of the most impactful opportunities for improvement.

Data quality creates confidence across the business

The goal of data quality is confidence. When people trust the information in front of them, they move faster: service teams resolve issues more efficiently, operations leaders make decisions with more certainty, executives spend less time reconciling reports, and analysts focus more on insight than validation.

Confidence develops when data is complete, consistent, timely, and aligned across systems.

A useful exercise is to identify a business-critical workflow and trace the information supporting it. Examine where the data originates, who owns it, how it changes over time, and how it moves between systems. Review the reports, dashboards, and decisions that depend on that information. By doing this, our clients discover opportunities to improve consistency, eliminate manual work, and strengthen governance.

Governance and stewardship turn data into a strategic asset

Trusted data is not achieved through technology alone; it requires equally strong governance and stewardship.

In strong data environments:

• Clear ownership models exist
• Business definitions are documented and understood
• Quality expectations are established
• Teams know who is responsible for maintaining critical information and resolving issues when they arise

Stewardship creates accountability, and governance creates consistency. Together, they provide the structure needed to support analytics, automation, and AI at enterprise scale.

While many organizations already have informal ownership practices in place, Summer ’26 presents an opportunity to formalize those efforts and align them with broader AI and modernization objectives.

Access and security shape data readiness

Data readiness also includes access readiness.

Employees, workflows, integrations, and AI-enabled experiences all depend on appropriate access to information. Permission models, role hierarchies, API controls, and security policies shape how data moves through the organization and how effectively it supports business processes.

As Salesforce continues expanding AI capabilities, access governance becomes increasingly important. Organizations need to understand who can access critical data, how permissions are managed, and whether existing controls align with current business requirements.

Reviewing access models often uncovers opportunities to simplify administration, strengthen governance, and improve consistency across teams.

Focus on use cases that connect data to business outcomes

Successful AI initiatives begin with clear business objectives.

The strongest use cases are tied to operational workflows where teams already understand the problem they need to solve. Customer service, workflow routing, knowledge retrieval, operational reporting, and decision support are strong starting points because outcomes are easier to measure.

Business context is what transforms data into value. Prioritize use cases where trusted information can directly improve a customer interaction, accelerate a process, support a decision, or increase operational efficiency.

This approach helps teams connect AI investments to measurable business results while strengthening the data foundation that supports future expansion.

A Salesforce data readiness checklist

Before expanding Agentforce, Data 360, analytics initiatives, or AI-enabled workflows, consider the following questions:

Identity

• Can customer, prospect, partner, member, or policyholder records be consistently identified across systems?
• Are duplicate management processes documented and monitored?
• Is there a strategy for creating a unified view of key business entities?

Access

• Do permissions align with current business responsibilities?
• Are security controls consistently applied across systems and integrations?
• Is access governance documented and reviewed regularly?

Data quality

• Do business users trust the information supporting key workflows?
• Are critical fields complete, consistent, and reliable?
• Are reporting outputs aligned across teams?

Stewardship

• Is ownership established for critical datasets?
• Are business definitions documented?
• Are quality standards monitored and maintained?

Use cases

• Have priority AI use cases been clearly identified?
• Can success be measured?
• Does trusted data exist to support those workflows?

Building the foundation for what comes next

Salesforce continues investing in a future where data, analytics, automation, and AI work together within everyday business processes. Summer ’26 reinforces that direction through expanded capabilities designed to help organizations activate and apply trusted business information across the platform.

Organizations that strengthen their data foundation now will be better positioned to move faster, operate more efficiently, and adopt new capabilities with confidence. The work starts with understanding the current data environment, identifying governance gaps, and connecting information strategy to measurable business outcomes.

AI readiness ultimately reflects the strength of the underlying data foundation. Organizations that invest in identity, quality, stewardship, access, and governance are building the platform for long-term success across analytics, automation, and AI.

Next steps

At Sikich, we help organizations assess data readiness, modernize data architecture, establish governance frameworks, and build scalable foundations for AI-enabled operations. Evaluate your foundation before scaling AI.

Evaluate your Salesforce data foundation