Sample Workforce AI Impact Snapshot
Preview the sample below. Select Read Full Report to see the entire Snapshot here. Product details follow the sample.
Synthetic example using simulated participant data. Actual Snapshots use input from the organization’s participating employees.
Workforce AI Impact Snapshot
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AI Task Exposure
Elevated task exposure; 4 Moderate and 2 Lower.
Human Strengths
Very Strong or Strong; 5 Moderate and 1 Developing.
Adaptability
Very High or High; 4 Moderate and 4 Building.
Technology Readiness
Building; 4 Moderate and 2 High.
What This Snapshot Gives You
This report provides:
- An aggregate picture of your workforce across four dimensions: AI Task Exposure, Human Strengths, Adaptability, and Technology Readiness
- An unattributed summary of participant comments, when comments were provided
- Intersection analysis showing important relationships between selected dimensions in your workforce
- Practical interpretation of what the findings may mean for your organization
- Questions worth exploring with your leadership team and employees
- Common focus areas that may help identify where further attention could be useful
- Practical guidance for sharing the findings with employees and considering what to do next
See Using Your Snapshot: What Comes Next near the end of this report for guidance on employee engagement and using the findings to help determine your next steps.
How This Snapshot Works
The Snapshot begins with information provided by your employees about the work they actually do. Rather than relying primarily on job titles or general predictions about occupations, the assessment gathers structured information about tasks, human strengths, adaptability and workplace technology experience.
A defined methodology analyzes that information across four dimensions: AI Task Exposure, Human Strengths, Adaptability and Technology Readiness. It also examines selected relationships between dimensions to help identify patterns that may be useful to understand.
AI supports much of this analysis. Carefully developed analytical instructions and rules are used to support consistent calculations, identify patterns and help interpret what the findings may mean for the organization.
The methodology itself has been developed and independently challenged using multiple leading AI models as complementary development and review systems. They have been used to question assumptions, test consistency, identify potential problems and provide independent second opinions during development.
Human judgment remains central. The methodology, safeguards, interpretation and reporting approach were developed under the direction of Donald DeGagne, drawing on 32 years of local-government management experience. AI supports the analytical process; management experience, critical thinking and judgment guide the methodology and final interpretation.
The result is not simply a questionnaire submitted to an AI system to generate a report. It is a structured assessment process combining employee evidence, defined analytical rules, modern AI capabilities and experienced human oversight.
The detailed analytical methodology and instructions are proprietary. The purpose of this section is not to explain those internal mechanics, but to make clear what sits between the employee questionnaire and the finished Snapshot.
Your employees provide the information. The system analyzes the patterns. Leadership receives an independent organizational picture to help decide what deserves attention next.
Understanding Your Snapshot Results
This is an organizational Snapshot, not an assessment of individual employees for management.
Leadership receives aggregate findings across the four dimensions and, when comments were provided, an unattributed summary of participant comments. The Snapshot does not identify which employees, positions or identifiable groups have particular levels of AI Task Exposure, Human Strengths, Adaptability or Technology Readiness. Individual scored responses, linked response records and personal reports remain private.
The Snapshot can therefore show you what patterns exist across your organization, but not who sits behind them or everything that explains why they exist.
This is intentional. It protects employee privacy and is also why employee involvement matters. Employees can help connect the aggregate findings to the work itself and identify what may be worth exploring further.
Your Workforce Snapshot
The organization-level findings from 12 participants
The Pattern at a Glance
Among 12 simulated participants, six reported Elevated AI Task Exposure, four Moderate and two Lower. The Team Average Task Exposure range is 40–50%. This indicates a mix of task characteristics with potential for AI assistance; it does not predict changes to jobs or staffing.
Human Strengths were Very Strong or Strong for six participants, Moderate for five and Developing for one. Adaptability was Very High or High for four, Moderate for four and Building for four. These separate results suggest capacity is uneven across the simulated assessment group and should be understood through discussion of the work.
Overall Technology Readiness was Building for six, Moderate for four and High for two. The Technology Foundation average of 2.9 / 5 exceeds the AI Experience average of 2.5 / 5. Among the six with Elevated Exposure, readiness was evenly split across High, Moderate and Building; this difference may matter when considering how to involve employees in further exploration.
AI Task Exposure
Tasks with potential for AI assistance
Seven task-characteristic inputs, reported in aggregate
Six of 12 participants fell in Elevated Exposure, four in Moderate and two in Lower. The calculated central estimate is 45%, giving a Team Average Task Exposure range of 40–50%; participant exposure midpoints span 18–60%.
The distribution shows varying levels of the seven reported task characteristics. Exposure describes potential for AI assistance in tasks, not job elimination or a basis for staffing decisions. The aggregate results do not identify which jobs or people account for the pattern.
Human Strengths
Responsibilities and workplace capabilities
Human-centred responsibilities, workplace strengths and adaptability
Two participants were Very Strong, four Strong, five Moderate and one Developing on the combined responsibilities and workplace-strength inputs. Half were in the two higher categories, while half were Moderate or Developing.
This spread points to varied reported responsibilities and capabilities across the participating group. The Developing result is an aggregate classification, not a judgment about an employee. Human Strengths and Task Exposure measure different things; the former does not reduce the latter.
Adaptability
Reported capacity to adjust to workplace change
Ability to adjust, readiness to adjust and confidence adapting
One participant was Very High, three High, four Moderate and four Building. Thus reported capacity to adjust to workplace change is distributed across all four presentation categories.
The four Building responses may warrant attention to how change is discussed and supported, while the four higher responses indicate a different reported experience. These answers measure perceived capacity to adjust, not enthusiasm for a particular change.
Technology Readiness
Technology foundation and reported AI experience
Overall Technology Readiness, with two subordinate components
Average of technology comfort, confidence learning new technology and the reported organizational AI environment.
Average of reported workplace and personal experience using AI-assisted tools.
Overall Technology Readiness was High for two participants, Moderate for four and Building for six. The supporting organization averages were 2.9 / 5 for Technology Foundation and 2.5 / 5 for AI Experience.
The modest difference suggests reported technology comfort, learning confidence and local AI environment are somewhat ahead of actual AI experience. These are self-reports: they do not establish proficiency, approved use or resistance, and the organizational environment input reflects participants’ own work areas.
Participant Comments
What This Tells You
- Half of participants reported Elevated Task Exposure, while the other half were Moderate or Lower; this calls for a closer look at tasks rather than assumptions about roles.
- Human Strengths are spread across categories, including six Very Strong or Strong and six Moderate or Developing; these results should be read separately from Exposure.
- Adaptability ranges from Very High to Building, indicating different reported capacities to adjust to workplace change.
- Technology Readiness is Building for half of participants, and reported AI Experience averages below Technology Foundation.
- Among participants with Elevated Exposure, Technology Readiness is split evenly across High, Moderate and Building.
Opening Questions for Leadership
- Which of the task characteristics behind the six Elevated Exposure responses merit a closer conversation with employees?
- What might explain the spread in reported Adaptability and the six Building Technology Readiness results?
- How should concerns about learning time, job security and safeguards shape the discussion of these findings?
Practical Considerations
What the findings may mean when read together
Reading the Full Picture
The simulated results point to a workforce picture with both opportunities for further inquiry and meaningful variation in reported capacity. Half of participants have Elevated Task Exposure, but Human Strengths and Adaptability span several categories. The aggregate data identifies patterns; employees can help explain what the underlying tasks involve.
Technology Readiness is Building for half the group, while AI Experience averages below Technology Foundation. Even within Elevated Exposure, readiness is evenly divided across High, Moderate and Building. A single approach to any future exploration may therefore miss differences in experience and support needs.
The written comments add concerns about job security, time to learn, privacy safeguards and clear procedures. Those issues may affect how leadership shares and investigates the results. They do not themselves establish what technology, if any, should be adopted.
Intersection Analysis
Elevated Exposure × Technology Readiness
Of the six participants with Elevated Exposure, two had High, two Moderate and two Building Overall Technology Readiness. Exposure to potentially assistable tasks therefore coexists with quite different reported technology conditions and experience.
Elevated Exposure × Human Strengths
Of the six with Elevated Exposure, one had Very Strong, two Strong, two Moderate and one Developing Human Strengths. Elevated task exposure is present across the strength categories and does not itself imply workforce weakness.
Questions Worth Exploring
- What do employees see behind the six Elevated Task Exposure responses, and which tasks involve exceptions or judgment?
- How should leadership interpret the 40–50% team exposure range alongside the 18–60% participant range?
- What might explain why six Human Strengths results are Moderate or Developing while six are Strong or Very Strong?
- What support or working conditions might matter to the four participants reporting Building Adaptability?
- What accounts for AI Experience averaging 2.5 / 5 while Technology Foundation averages 2.9 / 5?
- What would help make sense of the even High, Moderate and Building readiness split among the six with Elevated Exposure?
- How should concerns about job security and possible duty changes be addressed when these aggregate findings are shared?
- What privacy safeguards, written procedures and learning time do participants say they need before considering AI use?
Common Focus Areas
Understanding exposed tasks
Six of 12 participants reported Elevated Task Exposure. Examining the underlying work with employees may clarify where assistance is plausible and where human judgment or service needs shape the work.
Different capacity for change
Adaptability spans all four presentation categories, including four Building results. This may warrant discussion of how changes are introduced and supported without presuming individual attitudes.
Technology experience and support
Six Technology Readiness results are Building, and AI Experience averages 2.5 / 5 compared with 2.9 / 5 for Technology Foundation. Employees’ comments also request practical learning time and relevant examples.
Trust, privacy and clear guidance
Written feedback raises job security, communication, confidential information, safeguards and outdated procedures. These concerns may deserve attention alongside any exploration of AI-assisted tasks.
Using Your Snapshot: What Comes Next
The Snapshot provides an organizational picture based on information provided by your employees. It identifies patterns, relationships, questions and areas that may warrant further attention.
It cannot, and is not intended to, explain everything behind those findings. Your employees understand the work, processes, service demands, frustrations, exceptions and practical realities behind the aggregate results.
The following is a practical sequence for using the Snapshot. It is guidance, not a prescribed implementation process. Every organization will need to decide what is appropriate for its circumstances.
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Start Before the Assessment
Before employees complete the assessment, communicate with the whole organization.
Explain:
- Why the organization is undertaking the Snapshot
- What it is intended to help the organization understand
- Who is being invited to participate and why
- That the purpose is to understand the work and organizational impact of AI, not to evaluate individual employees
- That individual scored responses, linked response records and personal reports remain private
- That leadership receives aggregate organizational findings and an unattributed summary of participant comments, not individual employee results or attributed comments
- That employees will have an opportunity to help discuss and understand the findings afterward
Participation may focus on employees whose work includes administrative, information-handling, communication, coordination, analytical, documentation, supervisory or other technology-supported activities.
Job titles do not always reveal what people actually do. If an employee who has not been invited to participate believes the nature of their work makes the assessment relevant to them, provide a way for that employee to raise the issue and be considered for participation.
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Conduct the Assessment
Conduct the assessment with the relevant employees, consistent with the purpose and privacy protections explained to the organization at the outset.
Once completed, leadership receives the aggregate organizational Snapshot, including an unattributed summary of participant comments when comments were provided. Participating employees receive their own private personal reports.
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Discuss the Findings With Employees
After leadership has reviewed the Snapshot, bring the aggregate findings and participant comment summary back to participating employees.
The purpose is to help understand what may be behind the organizational results. Employees should be approached as people with expertise in their own work, not as subjects being evaluated.
Questions could include:
- What findings seem most important or surprising?
- What might help explain those findings?
- Where are repetitive, administrative or information-heavy tasks consuming significant time?
- Where might AI assistance be worth exploring?
- Where are human judgment, relationships, communication, experience or local knowledge particularly important?
- What concerns or risks should we keep in mind?
- Where might additional technology knowledge, training or support be useful?
- What is one manageable area that may be worth investigating further?
These questions are intended to support conversation and learning, not to produce an immediate AI implementation plan.
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Explore Where It Is Useful
Some findings may deserve further investigation before decisions are made.
Where useful, employees or smaller work groups can look more closely at particular:
- Processes
- Tasks
- Opportunities
- Concerns
- Service implications
- Areas where additional knowledge is needed
Employees closest to the work may be able to identify:
- Why a particular problem exists
- Where time is being consumed
- What has already been tried
- Where AI or other technology might add value
- Where technology may not be the answer
- What practical risks or service considerations leadership should understand
Not every finding requires immediate action. Give people reasonable space to investigate and learn where further exploration would be useful.
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Bring the Learning Together and Consider What Comes Next
Bring employee learning together with the Snapshot findings.
The objective is not to adopt AI everywhere or produce a long list of initiatives. It is to determine what appears to deserve attention and what, if anything, the organization should consider doing next.
Depending on the findings, that might include:
- Investigating a particular process or workflow
- Continuing employee discussion
- Establishing or reviewing appropriate boundaries for AI use
- Learning from peers or other organizations
- Considering training or technology support
- Seeking governance, legal, privacy, technology, organizational-change or other professional expertise
- Deciding that no immediate action is required in a particular area
Start with what your own information and employees indicate deserves attention. Then determine what expertise, resources or action may be appropriate.
The Snapshot helps identify where to look next and which questions are worth carrying forward. It does not prescribe which technology to buy, which consultant to hire or which solution to implement.
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Close the Loop With the Organization
Once leadership has considered the Snapshot findings, employee input and any additional investigation, communicate the resulting direction to the whole organization.
Employees should understand:
- What the organization learned
- What it intends to explore or do next
- What remains under consideration
- Where no immediate action is planned
Closing the loop demonstrates that employee participation contributed to organizational learning and was not simply a data-collection exercise.
The Snapshot is a starting point. What follows should be shaped by the organization’s own findings, its employees, its circumstances and the decisions leadership ultimately makes.
Understand first. Then decide what to do.
Evidence to support your next conversation.
The Snapshot brings employee-reported work evidence together into four distinct dimensions. They are interpreted separately and never combined into one overall score.
AI Task Exposure
Where current AI capabilities may intersect with the tasks people perform.
Human Strengths
Human-centred responsibilities and workplace strengths that remain important as work changes.
Adaptability
How readily people appear able to adjust to changing responsibilities and ways of working.
Technology Readiness
Technology comfort, organizational support and experience with AI tools.
Aggregate findings, interpretation and practical considerations help you understand the patterns and decide what deserves further attention.
Evidence-supported questions provide a starting point for discussion with employees. When participants provide comments, the report includes an unattributed summary of their material themes and concerns.
How the Snapshot works.
Choose who will participate
Confirm who will take part and the timing. Participation can focus on employees whose work involves administrative, information, analytical, communication or similar tasks.
Provide private input
Participating employees complete a private online assessment about their work and experience with change and technology, designed to take less than 10 minutes.
Receive your reports
Leadership receives the organizational Snapshot. Participants receive their own private personal reports.
Useful input depends on trust.
Leadership receives aggregate findings and, when comments are provided, an unattributed summary. Personal reports and individual responses are not shared with leadership.
Findings are reported for the organization as a whole, without identifying individuals or breaking results down by position, department or group. Identifying details are removed from the comments summary.
Explore the methodology and privacy approach →Practitioner-developed.
AI-supported. Human-directed.
Developed by Donald DeGagne, drawing on 32 years of local-government management experience and post-retirement work in small-business development and marketing.
AI supports the analysis, with human judgment under Donald’s direction remaining central.
The Snapshot may identify areas worth exploring. It does not promote a specific tool, training program, consultant or Realistik service.
$20 per participant.
For example, 10 participants cost $200. The price includes your organizational Snapshot and participants’ private personal reports.
Generally delivered within 24–48 hours after all participant assessments are completed.
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Several comments sought practical training tied to daily work, enough time to learn, and opportunities to learn during work hours. Written feedback also stressed that people who learn at different speeds should not be left behind, while expressing interest in learning for future development.
Some comments raised job security and called for honest communication about possible changes to duties before assumptions are made about automation. Other comments sought clear privacy rules, safeguards and specific guidance before AI is used with confidential information. The feedback also described uncertainty about a forthcoming AI tool and noted that existing written procedures need updating.