MT304 Unit 7 Technology and Decisions Memo Example

Course	MT304 Leading the 21st Century Organization
Unit	Unit 7
Paper type	Technology and decisions memo
Length	About 740 words, 5 pages double-spaced
Format	APA 7 student paper
School	Purdue University Global
Program	BS in Business Administration
Updated	October 2026

Free sample paper for MT304 Unit 7

Two Problems the Dashboard Created: An MT304 Unit 7 Technology and Decisions Memo on Automation Bias and Upward Drift

[Student Name]

Purdue University Global

MT304: Leading the 21st Century Organization

Unit 7 Technology and Decisions Memo

[Instructor Name]

[Date]

The insurer, its staff, software and figures are composite; the research and Colorado law cited are real.

What this part is doingSeparating the two problems in the lead signals that each will get its own fix.

Memo

To: Vice President, Claims From: Desk appraiser, hail team Subject: Decision rights for software estimates and the shared dashboard

Recommendation

1. Appraisers keep authority to settle within a band of 15 percent around the software's figure without further review.

2. A human estimate is required when the software's confidence score is below 0.80 or the figure is above $9,000.

3. Executive dashboard access shows aggregates only, by team and week, not individual claims.

Problem 1: Accepting the Software's Figure

Appraisers accept the software's estimate unchanged on 96 percent of hail claims. In August, I asked two veteran appraisers to recheck 200 accepted files blind, without seeing the software's figure. They found damage the tool had missed on 23, mostly hail on dark roofs photographed at dusk, where the dents barely show. The average missed amount was about $1,140.

Parasuraman and Manzey (2010) describe automation bias as the tendency to over-rely on automated aids, producing two kinds of error: omission, failing to notice a problem the aid did not flag, and commission, following an aid's wrong recommendation. Our 23 files are omission errors. Skitka et al. (1999) found that people working with an automated aid made more omission errors than people doing the same task without one, and that the effect was hard to remove by warning people about it. A reminder to "check carefully" will not fix this; a rule about when a human must estimate independently will.

Ninety-six percent agreement with the software is not evidence that the software is right; it may be evidence that nobody is checking.

What this part is doingReading the high acceptance rate as a warning rather than a success shows the memo understood automation bias.

Problem 2: Decisions Drifting Upward

Since the shared dashboard went live in May, every claim's status, value and age is visible to everyone up to the vice president. Team leads report that they now wait for a comment from you before settling claims over $5,000, because you have occasionally posted questions on individual claims. Settlement time on claims over $5,000 has risen from six days to nine. This is not automation bias; it is a failure of decision rights. When everyone can see a decision, nobody is sure whose it is.

Why the Two Problems Need Different Fixes

It would be tempting to treat both problems as a single issue of trust in technology. They are not. Automation bias is a problem of attention at the desk: appraisers stop looking for what the tool misses. The fix is a rule that tells them when they must look. Upward drift is a problem of authority: team leads stop deciding because they are unsure whether a more senior person wants to. The fix is a written statement of who decides what. Applying one fix to both would leave one problem untouched.

Decision Table

Each recurring decision has one owner and one trigger for escalation.

Accept software estimate within band. Owner: appraiser. Escalate: confidence below 0.80 or value over $9,000.

Independent human estimate. Owner: appraiser. Escalate: difference over 25 percent from the software.

Settle claim up to $15,000. Owner: team lead. Escalate: coverage dispute.

Total-loss valuation. Owner: senior appraiser. Escalate: policyholder disputes value.

Reinspection request. Owner: appraiser. Escalate: none; always permitted.

Settle claim over $15,000. Owner: claims manager. Escalate: litigation threatened.

Change software thresholds. Owner: vice president. Escalate: annual review.

Safeguards

Monthly blind sample. Fifty accepted files a month are rechecked blind by a rotating veteran. If missed damage exceeds 5 percent, the confidence threshold rises.

Dusk and dark-roof rule. Photos taken after 6:30 p.m. or of dark roofs trigger a human estimate regardless of confidence.

Dashboard view. Executives see team-level aggregates. Comments on individual claims go through the claims manager.

Colorado Regulation

Colorado's 2021 law on insurers' use of external consumer data and algorithms requires insurers to show that such systems do not unfairly discriminate and gives the Division of Insurance authority to set rules for testing them (Colorado General Assembly, 2021). Its rules so far focus on life insurance underwriting, but they signal that the state expects insurers to govern their algorithms. A written decision table and a monthly blind sample would put us ahead of any rule that extends to claims software.

What this part is doingPlacing the safeguards in the context of state regulation gives the vice president a second reason to act.

References

Colorado General Assembly. (2021). Restrict insurers' use of external consumer data (S.B. 21-169). https://leg.colorado.gov/bills/sb21-169

Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381-410. https://doi.org/10.1177/0018720810376055

Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991-1006. https://doi.org/10.1006/ijhc.1999.0252

The Purdue Global MT304 Unit 7 instructions

In Unit 7 students write a memo on how technology changes who makes decisions. The memo should open with a short recommendation. Instructors expect problems to be defined precisely and, where there are two, kept apart because they need different fixes. Evidence from the organization, such as acceptance rates or a recheck, makes the problem concrete. Research should name the mechanism, for example automation bias or unclear decision rights. A table assigning recurring decisions to owners, with triggers for escalation, is a strong way to answer drift. Safeguards should be specific and measurable. Relevant regulation should be mentioned where it applies. The memo should be written for a senior reader who will act on page one and expects the reasoning behind each recommendation.

How this MT304 Unit 7 example is built

The sample's recommendation sets a settlement band, triggers for a human estimate and aggregate-only dashboards. The first problem presents the 96 percent acceptance rate and the 23 missed files, named as omission errors through Parasuraman and Manzey and Skitka and colleagues, under the highlighted line that high agreement may mean nobody is checking. The second problem shows settlement time on larger claims rising from six to nine days as team leads wait. A section explains why the two problems need different fixes. A decision table assigns seven recurring decisions. Safeguards include a monthly blind sample and a dusk rule. Colorado's 2021 algorithm law closes the memo.

Where the points sit in the MT304 Unit 7 rubric

Technology memos in MT304 are usually assessed on precise problem definition, use of organizational evidence, accurate application of research and the practicality of recommendations. Graders reward memos that distinguish problems needing different fixes. Evidence should be specific to the organization. Research should explain the mechanism, not decorate the memo. A decision table or similar device should give each decision one owner. Safeguards should be measurable and tied to the evidence. Regulation should be described accurately and only where relevant. Professional memo format with the recommendation first suits the reader. Research and statutes are cited in APA 7. Memos that recommend more training to fix automation bias, without changing decision rules, usually lose points.

MT304 Unit 7 help: mistakes that cost points

Technology memos often treat every problem with software as a matter of training or trust. Another common weakness is describing automation bias in general without evidence from the workplace. Some students propose giving all decisions back to humans or all to software, ignoring the middle ground. Decision rights are frequently left vague. Regulation may be cited loosely or not at all. Before drafting, gather one figure showing how often staff accept the software's output and, if possible, recheck a small sample blind. Then list the decisions that have drifted up or down since the new tool arrived. Those two pieces of evidence define the memo.

Get an MT304 Unit 7 example written to your instructions

What tool now shapes decisions in your organization, and who can see its output? Give whatever numbers you have on it, add the Unit 7 prompt and rubric, and a memo arrives within 24-48h that assigns each decision an owner and names the failure it guards against. First samples cost nothing; estimates stay bracketed. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.

More MT304 papers and BS in Business Administration samples

MT304 Unit 7 questions, answered

Does the tool have to use artificial intelligence?

No. The unit usually concerns any technology that changes who knows what and who decides, and a shared dashboard, a scheduling system or a scoring rule can do that without machine learning. Describe what the tool recommends or displays, who sees it and what people now do differently. The leadership question is the same whether or not the tool learns.

How can I show automation bias without company data?

Describe the pattern you observe and label it as observation: how often people accept the tool's output, whether anyone checks it independently, what happens when it errs. Research on automation bias supplies the mechanism. If you can run even a small check, such as ten cases reviewed before looking at the tool's answer, report it with its limits.

Should the memo recommend limiting who sees the data?

Sometimes, and it has to say why. Wider visibility can improve decisions or pull them away from the people closest to the work, depending on what viewers do with it. The strongest memos separate seeing from deciding: many people may see a figure while one role owns the decision it informs, with a stated trigger for escalation.

Where can I find a free MT304 Unit 7 sample paper?

The complete MT304 Unit 7 technology and decisions memo is on this page, addressing automation bias and decisions drifting upward at a claims office.

What safeguards does the MT304 Unit 7 sample propose?

A monthly blind recheck of accepted files, a rule requiring human estimates for dusk and dark-roof photos and aggregate-only executive dashboards.