AI in TCM Bootcamp · Pre-Course Survey

AACE ConEX 2026

AI in Total Cost Management
Bootcamp

Pre-Course Survey — Day 1 Opening · Estimated time: 5–7 minutes

H. Lance Stephenson · AECOM Jareth Reeves · Kaleido

Before we begin, we’d like to understand where you’re starting from. This is not a test — there are no marks and no one sees your individual answers except you. Answer honestly; the only person you’re calibrating for is yourself.

Your answers form a baseline you’ll revisit at the end of Day 2 — so answer honestly, as you genuinely feel right now.

Section A About You

A1 — Primary Role

What best describes your primary professional role?

Cost Engineer / Estimator

Project Controls Manager / Analyst

Scheduler / Planning Engineer

Risk Manager / Analyst

Project Manager

Data Analyst / Business Intelligence

Other

A2 — Experience

How many years of experience do you have in project controls or cost engineering?

Less than 3 years

3–7 years

8–15 years

More than 15 years

A3 — Industry Sector

What is your primary industry sector?

Oil & Gas / Energy

Infrastructure / Transportation

Defence / Government

Construction / EPC

Mining / Resources

Other

Participant Code

Create a memorable 4-digit code — for example, the last 4 digits of your mobile number. Write it down. You will need it again at the end of Day 2 to match your answers.

A1, A2 & A3 required to continue

Section B Self-Assessment

The five stages below come from the bootcamp’s AI adoption maturity framework. Read them carefully before answering.

1

Exploratory

No data analytics expertise or limited skills — just beginning to explore what’s possible

2

Investment

Aware of the need; beginning to commit to adoption through tools, training, or hiring

3

Experience

Usable data exists; some hands-on experience with analytics tools and techniques

4

Experimental

AI/ML systems in place; actively driving data-led decisions and learning from outcomes

5

Leading

AI/ML is central to how your business operates; you drive revenue-based learning technology

B1 — Personal Level

Where would you place yourself personally on this scale right now?

1Exploratory

2Investment

3Experience

4Experimental

5Leading

B2 — Organisation Level

Where would you place your organisation on this scale right now?

1Exploratory

2Investment

3Experience

4Experimental

5Leading

B3 — Confidence in Those Ratings

How confident are you in those self-assessments?

Very confident — I know exactly where I stand

Fairly confident — good estimate, could be off by one level

Not very confident — I am guessing

I don’t have enough information to answer

Section C Knowledge Check

For each question, choose the best answer and rate how confident you are. Both parts matter. There is no penalty for guessing.

Question 1 of 5

Artificial intelligence, machine learning, and deep learning are the same thing.

True

False

Not sure

Confidence in your answer

Very confident

Somewhat confident

Just guessing

Question 2 of 5

A regression model is used to predict:

A category (e.g., will this project overrun? Yes or No)

A continuous value (e.g., what will the final cost be?)

Either, depending on the data

I’m not sure

Confidence in your answer

Very confident

Somewhat confident

Just guessing

Question 3 of 5

You are building a model to predict a project’s Estimate at Completion (EAC) at 30% complete. Which of the following would be the target variable — the thing you want the model to predict?

The project’s original budget

The CPI at 30% complete

The actual final cost at project completion

The control budget at the time of prediction

I’m not sure

Confidence in your answer

Very confident

Somewhat confident

Just guessing

Question 4 of 5

Unsupervised learning algorithms require labelled training data — examples where the correct answer is already known.

True

False

Not sure

Confidence in your answer

Very confident

Somewhat confident

Just guessing

Question 5 of 5

In the context of AI and machine learning, “overfitting” means:

The model is too accurate on training data and performs poorly on new, unseen data

The model has been trained on too many features

The training dataset is too large for the algorithm to process efficiently

The model’s predictions are consistently too high

I’m not sure

Confidence in your answer

Very confident

Somewhat confident

Just guessing

Answer all 5 questions to continue

Section D Experience Inventory

D1 — AI Tool Usage

In the past 12 months, have you used AI or machine learning tools in your professional work?

Yes — regularly (weekly or more)

Yes — occasionally (a few times)

No, but I have experimented personally

No

D2 — Tool or Technique (optional)

Briefly describe the AI/ML tool or technique you used. Leave blank if not applicable.

D3 — Predictive Modelling

Have you ever built or contributed to a predictive model of any kind — in Excel, Python, a BI tool, or purpose-built software?

Yes — I built one myself

Yes — I contributed to one built by others

No, but I have used outputs from one

No

Section E Expectation Setting

E1 — Your Goal

In one sentence, what is the single most important thing you want to leave this bootcamp able to do?

E2 — Biggest Concern (optional)

What is your biggest concern or uncertainty about AI/ML in your work context?

E1 required to submit

Survey Complete

Your responses have been saved. At the end of Day 2 you will be shown your answers and asked to re-evaluate them with what you know then.

Keep a note of your participant code below — you will need it on Day 2.

Your Participant Code

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