How should operations leaders evaluate AI in Salesforce workflows?

Salesforce Summer ’26 continues expanding AI across workflows, service operations, analytics, automation, and decision support experiences. As these capabilities become more visible across the platform, many organizations are shifting their focus from possibility to practicality. The conversation is increasingly centered on where AI can contribute meaningful business value and how leaders can determine whether a workflow is actually ready for AI-enabled support.

For operations leaders, this evaluation starts with a different set of questions than those typically asked in technology discussions. The most valuable insights rarely come from feature comparisons or product demonstrations, but rather from understanding the answers to the following questions.

• How does work move through the business?
• Where do employees encounter friction?
• Where do customers experience delays?
• Where does operational complexity consume time and resources?

Salesforce’s continued investment in Agentforce, Flow, Data 360, analytics, and workflow intelligence reflects a broader shift toward more connected and intelligent business processes. The organizations that create the most value from these capabilities will be the ones that understand their workflows deeply enough to identify where AI can support meaningful outcomes.

Workflow visibility creates the foundation for AI readiness

Many organizations begin evaluating AI before they have a complete picture of how work moves through their business. This is understandable as new capabilities generate excitement, business leaders want to improve productivity, and teams are eager to modernize operations.

In practice, workflow visibility often creates some of the most immediate value available to operations leaders.

One observation we see repeatedly is that workflow documentation frequently reflects how a process was originally designed rather than how it operates today. Over time, organizations add approval steps, introduce new teams, implement workarounds, connect additional systems, and create exceptions to support changing business needs. The process evolves while documentation remains static.

When leaders map a workflow from beginning to end, they often discover that employees are performing tasks, making decisions, and transferring information in ways that are not fully reflected in process diagrams or operating procedures.

This visibility is critical because AI-enabled workflows inherit the strengths and weaknesses of the process they support.

Questions to ask:

• Can we clearly describe how this workflow operates today?
• Have there been significant process changes in the last 12 to 24 months?
• Are there workarounds employees use regularly?
• Do different teams describe the workflow differently?

The answers often reveal opportunities for improvement long before technology changes are introduced.

Follow the friction before following the technology

Operations leaders already know where many workflow challenges exist: employees discuss them in meetings, managers encounter them in reporting reviews, and customers experience them during service interactions. These are the moments of friction that often provide the clearest roadmap for evaluating AI opportunities.

Consider a common service escalation process. A customer issue enters through one channel, moves to a service team, requires operational review, involves information from another system, and eventually reaches a manager for resolution. Each step may function independently and the overall experience can still involve delays, repeated information requests, and uncertainty about ownership.

In many cases, teams spend more time gathering context than addressing the underlying issue.

This is an important observation because it highlights where AI-enabled support may contribute value. Information retrieval, context summarization, routing recommendations, and workflow visibility often emerge as opportunities when organizations understand where friction occurs.

Questions to ask:

• Which workflows generate the most employee frustration?
• Where do customers experience delays?
• Which activities require excessive manual effort?
• Where do employees spend time gathering information before acting?

Strong answers to these questions create a practical starting point for evaluating readiness.

Handoffs deserve more attention than most organizations give them

Many operational challenges occur between teams rather than within them.

A workflow may function efficiently inside a department while becoming significantly more complex as work changes ownership. Information must travel with the request, context must remain intact, accountability must remain clear, and employees need visibility into status and next steps.

Salesforce environments often reflect this reality. A workflow may span multiple Flows, queues, integrations, approval processes, dashboards, and business units. While each component serves a purpose, the complexity emerges from the interactions between them.

One pattern we frequently observe is that employees become the integration layer between systems. They manually collect information, reconcile discrepancies, send status updates, and provide context that technology environments have not yet connected effectively.

This creates an opportunity for workflow modernization because it highlights where information access and process visibility can improve.

Questions to ask:

• Where does ownership change during the workflow?
• Which handoffs create the longest delays?
• How often do employees need to gather information from multiple systems?
• How easily can teams understand the current status of work?

Organizations that understand these transitions are better positioned to evaluate where AI-enabled workflow support can contribute value.

Approval processes often reveal hidden complexity

Approval workflows deserve special attention because they frequently influence operational speed across the organization.

Most approval structures begin with a clear purpose, but additional review requirements accumulate over time as organizations grow, regulations evolve, and business units establish their own operating practices. These additions often make sense individually. Collectively, however, they can create complexity that affects throughput, responsiveness, and visibility.

When operations leaders examine approval workflows closely, they often discover that delays originate from information gaps rather than approval volume. Reviewers spend time locating documents, gathering context, validating details, or confirming ownership before making decisions. This shifts the conversation toward process design and information availability.

AI-enabled workflow support can help surface relevant context, summarize information, and improve visibility into decision-making processes. The greatest value emerges when organizations first understand where complexity exists and why it occurs.

Questions to ask:

• Which approvals create the longest cycle times?
• What information do reviewers consistently request?
• How often do requests return for additional clarification?
• Are approval criteria applied consistently across teams?

These assessments frequently uncover opportunities that improve performance while strengthening governance.

Exception handling reveals the truth about operations

Many process reviews focus on the standard path through a workflow, but daily operations are often shaped by what happens outside that path.

Exceptions create complexity because they introduce variability. A customer record may be incomplete, an order may contain conflicting information, a service request may require escalation, or a workflow may encounter a condition that requires manual review.

Employees develop expertise around these situations because they encounter them regularly. They know which teams to contact, which information to verify, and which actions move work forward.

Exception analysis provides valuable insight because it reveals where operational effort is concentrated.

One of the most effective readiness exercises is identifying the exceptions that consume the most time and evaluating how they are handled today. These situations often provide a realistic view of workflow performance and create valuable opportunities for improvement.

Questions to ask:

• Which exceptions occur most frequently?
• Which issues require the greatest amount of manual effort?
• What causes workflows to stall?
• Which scenarios generate the highest escalation volume?

Organizations frequently discover meaningful improvement opportunities through exception analysis alone.

Measurement creates clarity

Successful workflow initiatives share one characteristic: they connect improvements to measurable outcomes.

Operations leaders need to establish performance baselines before evaluating AI opportunities. Baseline metrics create a framework for decision-making and help teams understand whether changes are producing meaningful results.

The most useful measures are tied directly to business performance. Examples include cycle time, resolution time, throughput, escalation volume, first-contact resolution, rework rates, customer satisfaction, and employee productivity.

Measurement also helps organizations prioritize. Some workflows may appear attractive from a technology perspective while offering limited business impact. Others may present significant opportunities because they influence customer experience, revenue generation, operational capacity, or service quality.

Clear metrics create alignment around where attention should be focused.

Data and governance shape every workflow outcome

Salesforce continues investing in Agentforce, Data 360, reporting, analytics, and connected business context because trusted information plays a central role in workflow performance.

Workflow readiness depends heavily on the quality of the information supporting decisions. Employees need confidence in the data available to them, teams need consistent definitions, and leaders need visibility into performance. Governance provides the structure that supports these outcomes.

We often find that workflow assessments uncover data and ownership questions as quickly as process questions.

• Are teams defining metrics differently?
• Is information originating from multiple systems?
• Is accountability distributed across departments?

 These discoveries are valuable because they identify opportunities to strengthen the operational foundation supporting future AI initiatives.

Organizations that invest in workflow governance, data trust, and operational visibility are creating conditions that support long-term scalability.

A practical readiness exercise for operations leaders

Select three workflows that are important to your organization and evaluate them through four lenses:

Process

• Is the workflow documented?
• Are roles and responsibilities clearly defined?
• Is ownership established?

Friction

• Where do delays occur?
• Which activities require significant manual effort?
• Where does rework happen?

Data

• Is the information trusted?
• Is context readily available?
• Are reporting outputs consistent?

Outcomes

• How is success measured?
• Do baseline metrics exist?
• Can improvements be quantified?

This exercise frequently uncovers opportunities to improve operational performance, strengthen governance, and create greater visibility into how work moves through the business.

Operational readiness drives sustainable AI adoption

Salesforce Summer ’26 reinforces a broader trend across the platform. AI is becoming more integrated into everyday workflows, decisions, and business processes. Organizations have an opportunity to create meaningful value through these capabilities when they are connected to well-understood operational goals.

Operations leaders play a critical role in this journey because they understand how the work gets done. They see where customers encounter friction, where employees spend time navigating complexity, and where process improvements can create measurable impact.

The strongest AI opportunities emerge from workflows with clear ownership, trusted data, measurable outcomes, and established governance. Organizations that invest in understanding those workflows now are building a foundation that supports future innovation across Salesforce.

At Sikich, we help organizations evaluate workflow readiness, improve operational design, strengthen governance, and build scalable foundations for AI-enabled business processes.

Contact our team for guidance in identifying and assessing your workflows for readiness before expanding AI initiatives.