Market research analysts and marketing specialists: AI and the work ahead
AI assistance can speed information gathering, coding and report production. The difficult parts remain framing a useful question, obtaining a credible sample and deciding what the evidence actually supports.
Survey research, competitive intelligence and marketing analytics have different data and validation needs. A summary of public commentary cannot substitute for a representative customer sample or a well-designed test.
Compare the duties below with a normal week in your own role. Time
spent, responsibility, employer tools and access to reliable data can
change the picture substantially. Use the personal task audit to
identify those differences.
Task-by-task exposure
We selected 8 of 11 O*NET core
tasks by importance. Importance is an O*NET rating on a 1–5 scale; it
is not the percentage of time spent. The category and explanation are
our interpretation, not an O*NET assessment of AI.
0 routine automation 6 ai-assisted 2 human-led
Reading uncertainty: changing one task by one
category step would put this index at approximately 31–44/100. This is a
sensitivity example, not a statistical confidence interval. Small
score differences are not a sound reason to change careers.
Selected core tasks and our interpretation
Task and source detail
Assessment and reason
Prepare reports of findings, illustrating data graphically and translating complex findings into written text.
O*NET task 5434 · Importance 4.06/5
Task source: 08/2026 · Rating: 08/2026
AI-assisted
Charts and draft narratives are assistable once data is available. The researcher must verify denominators, uncertainty and whether the narrative says more than the study can support.
Seek and provide information to help companies determine their position in the marketplace.
O*NET task 5437 · Importance 4.06/5
Task source: 08/2026 · Rating: 08/2026
AI-assisted
Existing feedback can help describe customer perceptions. Positioning advice requires a relevant comparison set and a clear distinction between evidence and commercial assumptions.
Conduct research on consumer opinions and marketing strategies, collaborating with marketing professionals, statisticians, pollsters, and other professionals.
O*NET task 5439 · Importance 3.94/5
Task source: 08/2026 · Rating: 08/2026
Human-led
Study execution includes coordinating people and making choices about whose views are represented. Text analysis helps with a subset, but does not establish a valid research design.
Collect and analyze data on customer demographics, preferences, needs, and buying habits to identify potential markets and factors affecting product demand.
O*NET task 5433 · Importance 3.88/5
Task source: 08/2026 · Rating: 08/2026
AI-assisted
Cleaning and exploring supplied data can be assisted. Collection quality, representativeness and the leap from correlation to market demand prevent classifying the entire duty as automated.
Devise and evaluate methods and procedures for collecting data, such as surveys, opinion polls, or questionnaires, or arrange to obtain existing data.
O*NET task 5443 · Importance 3.85/5
Task source: 08/2026 · Rating: 08/2026
Human-led
Sampling and measurement choices determine whether the evidence answers the business question. Draft survey wording can help, but accountable design and validation remain central.
Gather data on competitors and analyze their prices, sales, and method of marketing and distribution.
O*NET task 5441 · Importance 3.65/5
Task source: 08/2026 · Rating: 08/2026
AI-assisted
Software can organize available competitor observations. Missing private data, inconsistent units and the meaning of a competitor's behavior still require assessment.
Measure and assess customer and employee satisfaction.
O*NET task 5435 · Importance 3.62/5
Task source: 08/2026 · Rating: 08/2026
AI-assisted
Open-text themes can assist satisfaction analysis. Survey coverage, response bias and the relationship between comments and the wider population require independent checks.
Monitor industry statistics and follow trends in trade literature.
O*NET task 5442 · Importance 3.53/5
Task source: 08/2026 · Rating: 08/2026
AI-assisted
Summaries can organize supplied industry reports. Monitoring still requires checking publication dates, source definitions and whether an apparent trend is comparable over time.
Routine automation includes conventional configured software; it does
not imply autonomous AI or adoption by every employer. Human-led
allows supporting tools. Vendor links document a capability, not a
validated rating of this complete task. Older task dates are shown
even though the database release is August 2026.
Task wording and importance: O*NET occupation 13-1161.00,
O*NET 31.0 Database
, U.S. Department of Labor, Employment and Training Administration, CC BY 4.0.
Selected and adapted by Career Risk Score; USDOL/ETA has not approved,
endorsed or tested these changes.
What assistance looks like in practice
Hypothetical workflow to illustrate the boundary; not a reported case
study.
A company wants to understand why renewals fell. A tool groups comments into themes and drafts a summary. The analyst compares those themes with who responded, which accounts left and whether the questions encouraged particular answers.
What must be checked
Inspect the source comments behind each theme, assess nonresponse and segment differences, and separate correlation from explanations requiring further research. Report what the available sample cannot establish.
Practical skills to strengthen
Publish a portfolio study with its questionnaire, sampling limits and reproducible analysis.
Practice checking AI-coded comments against an independently reviewed subset.
Present a recommendation alongside the alternative explanation that would change it.
Changes worth watching
Measure whether automated summaries reduce preparation time without hiding contradictory evidence.
Watch whether employers expect experiment design, data quality and business interpretation alongside reporting.
There is no supported date when this occupation becomes “safe” or
disappears. Revisit these observations as your tasks and tools change.
Tools behind these capability examples
These are relevant documented capabilities, not endorsements,
adoption statistics or hands-on product reviews. Features depend on
the product, plan and employer configuration. References checked
September 2026.
Copilot in ExcelSpreadsheet assistance. A plausible formula is not evidence that its assumptions fit the decision.
Qualtrics Insights ExplorerOpen-text feedback analysis, subject to permissions and product access. Outputs require review.
BLS reports median annual pay of $78,760
in 2025, with 82,000 projected
openings per year on average during 2025–35. Openings include
replacement needs as well as growth; they are not a count of currently
advertised vacancies.
The projected employment change is + 7%
over ten years. Employment growth can coexist with automation of particular tasks. It does not make every worker or location equally likely to benefit.
Typical entry education
Bachelor's degree
Related work experience
None
Typical on-the-job training
None
Data science adds programming and statistical modeling requirements beyond report preparation. Management analysis adds implementation and stakeholder work. Choose the bridge that matches work you can demonstrate, rather than the destination's median pay.
BLS categories describe typical entry, not a complete qualification
checklist. “None” does not mean no learning is needed. National
medians are not starting salaries; location, sector, experience, hours
and benefits matter. Wage data exclude self-employed earnings. Consult
local job descriptions and relevant credential authorities before
paying for training.
These links identify transferable work and a concrete gap to explore.
They are not guaranteed pathways, “AI-proof” jobs or recommendations
based on a small score difference.
Reproduce an analysis in code, document leakage checks and evaluate it on genuinely held-out data.
BLS typical entry: Bachelor's degree; related experience: None.
10 shared skills among each role's top
12
Reading Comprehension, Active Listening, Writing, Critical Thinking, Speaking, Active Learning, Complex Problem Solving, Judgment and Decision Making, Mathematics, Monitoring.
These broad O*NET skills are selected by importance, with
ties broken by skill ID. Overlap does not measure
proficiency or hiring readiness.
Turn one research finding into an operational recommendation with costs, owners and a way to assess results.
BLS typical entry: Bachelor's degree; related experience: Less than 5 years.
9 shared skills among each role's top
12
Reading Comprehension, Active Listening, Writing, Critical Thinking, Speaking, Complex Problem Solving, Judgment and Decision Making, Monitoring, Social Perceptiveness.
These broad O*NET skills are selected by importance, with
ties broken by skill ID. Overlap does not measure
proficiency or hiring readiness.
Use audience research to develop a message plan and a defensible evaluation approach.
BLS typical entry: Bachelor's degree; related experience: None.
10 shared skills among each role's top
12
Reading Comprehension, Active Listening, Writing, Critical Thinking, Speaking, Active Learning, Complex Problem Solving, Judgment and Decision Making, Social Perceptiveness, Persuasion.
These broad O*NET skills are selected by importance, with
ties broken by skill ID. Overlap does not measure
proficiency or hiring readiness.
Use your own plausible pay figures to explore a move. Both pay fields start
at this occupation's national median so the calculator does not imply a
salary increase. A destination median describes existing workers, not your
likely starting offer.
A fixed-pay illustration in nominal dollars. Unpaid months occur at the
start of preparation; costs are charged at the start. Transition must fit
within the comparison period. Excludes taxes, benefits, raises, inflation,
investment returns and the chance of getting a job. A negative result is
possible. No inputs are saved or sent by this calculator.
Calculation and a worked example
Stay = current pay × years. Move = preparation pay × (preparation years −
unpaid months ÷ 12) + new pay × (years − preparation years) − one-time
cost.
For $60,000 current pay, $70,000 new pay, $50,000 preparation pay, two
years preparing, three unpaid months, $8,000 costs and a ten-year
comparison: stay = $600,000; move = $639,500. The $39,500 difference is a
scenario result, not a forecast. Break-even checks cumulative totals each
month under these same assumptions.
Check the evidence and limits
The index weights these sampled tasks equally. It does not measure
time, adoption, cost savings, unemployment or individual ability. The
examples and transition exercises are editorial inferences. Product
documentation supports the narrower capability described, not the
claim that a whole job can be replaced.
