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TL;DR: Evaluate workplace AI against a specific operational problem, reliable information, privacy requirements, and measurable outcomes. Start with a focused, lower-risk pilot, keep people accountable for high-impact decisions, and maintain clear handoffs when AI cannot safely or accurately help.
AI is becoming part of workplace operations, but adding an assistant to an existing process does not automatically make that process better. Workplace, facilities, people, and operations leaders need to evaluate AI against practical questions: Which work should it support? What information can it use? Where must a person remain accountable? How will the organization measure whether it helps?
A useful evaluation focuses less on novelty and more on operational fit. The goal is not to automate every interaction. It is to make workplace services easier to access, improve response quality, and help teams make better decisions while protecting privacy, accuracy, and employee trust.
Begin by documenting the problem before considering an AI solution. Common use cases include answering recurring workplace questions, directing employees to the right request workflow, summarizing operational information, identifying patterns in utilization data, and helping workplace teams find relevant information faster.
Describe the current process in enough detail to expose its weaknesses:
This prevents a common mistake: applying AI to a vague goal such as “improve the employee experience.” A specific goal, such as helping employees find the correct workplace request category, is easier to test and govern.
Not every workplace task carries the same risk. Classify potential use cases before selecting a tool or designing a workflow.
AI may be well suited to routine, reversible tasks based on approved information. Examples include explaining booking rules, locating a workplace policy, summarizing a request, or suggesting the next step in a standard process.
Greater caution is required when an output could affect access, safety, employment, privacy, or resource allocation. Examples include deciding who receives a particular accommodation, inferring individual attendance patterns, approving a security-sensitive visitor request, or recommending changes based on data that may be incomplete.
For higher-risk work, AI should support analysis rather than make the final decision. Define who reviews the output, what evidence they need, and how an employee can request correction or escalation.
AI is only as reliable as the information available to it. Create an inventory of the sources that could inform the experience, such as booking rules, room and desk records, service catalogs, workplace policies, building information, and request history.
For each source, document:
Use the minimum information needed for the task. An assistant that answers a general question about room availability does not necessarily need access to an employee’s broader workplace history. Limiting data improves privacy and reduces the chance that irrelevant or outdated information influences an answer.
A confident-sounding response can still be wrong. Build a test set from real operational questions, but remove unnecessary personal information. Include straightforward questions, ambiguous wording, exceptions, outdated policy references, and requests that should be escalated.
Review each response for:
Test with the people who understand the process, not only with technical stakeholders. A facilities coordinator may identify an exception that is invisible in a sample dataset. An employee experience leader may spot language that creates confusion or implies a policy that does not exist.
AI should not become a dead end. Every supported workflow needs a clear path to a person or formal request process when the system lacks confidence, the issue is sensitive, or the employee asks for help.
A good handoff should preserve useful context, such as the employee’s question, selected location, relevant booking details, or request category. It should also explain what happens next, who owns the follow-up, and whether additional information is needed.
Set escalation triggers in advance. These may include safety concerns, accessibility or accommodation requests, suspected security issues, disputes about access, conflicting records, or questions involving confidential information. The system should not pressure employees to disclose more information than the responsible team needs.
Workplace systems can contain information about attendance, location, visitors, requests, and resource use. Treat this information according to its sensitivity and intended purpose.
Before launch, confirm:
Avoid using workplace data for a new purpose simply because it is available. If the organization wants to analyze utilization, explain the purpose, use aggregated information where appropriate, and avoid presenting operational data as a measure of individual performance unless there is a legitimate, documented reason.
A pilot should be narrow enough to evaluate and useful enough to reveal real operational conditions. Select one workflow, audience, or location rather than launching across every workplace process at once.
Define the baseline before the pilot starts. Depending on the use case, this could include time spent answering routine questions, the number of misrouted requests, completion time for a standard workflow, or employee effort required to find information. Do not assume that faster interactions are better if they create more errors or follow-up work.
During the pilot, review both successful and unsuccessful interactions. Look for recurring misunderstandings, missing source information, unexpected privacy concerns, and cases where employees bypass the tool because the handoff is unclear. A limited pilot is also an opportunity to test internal ownership, not just the technology.
AI-enabled workplace operations still need accountable people. Assign ownership for the content, workflow, data, access controls, and performance review. These responsibilities may sit with different teams, but they must be visible.
Create a maintenance routine that includes:
For an AI assistant such as Tessa, this operational discipline is as important as the initial configuration. The value comes from connecting assistance to reliable workplace information and established workflows, then improving those connections over time.
Evaluate the experience from both the organization’s and the employee’s perspective. Useful measures may include successful self-service completion, request routing accuracy, time to resolution, escalation quality, and the rate of questions that require correction. Pair these with qualitative feedback about clarity, confidence, and ease of use.
Do not optimize for deflection alone. Preventing an employee from reaching a person is not a success if the issue remains unresolved. A strong result means the employee reaches the right answer or owner with less unnecessary effort, while workplace teams retain appropriate control.
Once a pilot demonstrates value, expand by workflow rather than by enthusiasm. Prioritize use cases with reliable information, clear ownership, manageable risk, and a measurable improvement opportunity.
Document what the AI feature can and cannot do. Communicate that boundary to employees, provide an easy correction path, and review performance after changes to policies, spaces, or service processes. Responsible adoption is not a one-time approval. It is an ongoing operating practice.
For workplace leaders, the central question is simple: does AI help people navigate the workplace while improving the team’s ability to operate it? When the answer is supported by accurate information, human accountability, privacy safeguards, and measured results, AI can become a practical layer in a modern workplace operation rather than another disconnected tool.
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