A WORKING SELF-ASSESSMENT / ABOUT FORTY MINUTES

Where you
actually stand.

AI, data and automation: a clear look at how your organization runs, written in your own words. Nothing to buy. Nobody scores your answers but you.

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BEFORE YOU START

Written in your own words.

This is a set of questions that founders, owners and senior leaders are rarely asked in one sitting. Answer truthfully and you will finish with a clearer picture of where your organization runs on data, where it runs on instinct and where the expensive gaps are.

Nobody scores your answers but you. There are no points and no grades.

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A WORKING SELF-ASSESSMENT

Where you
actually stand

AI, data and automation: a clear look at how your organization runs, written in your own words.

Four parts, six pages
Forty minutes, a keyboard
Nothing to buy

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BEFORE YOU START

How to use this workbook

This is not a brochure. There is nothing to buy on these pages, and nobody scores your answers but you.

Work through the four parts in order. Each one builds on the last.
Tick the answer closest to the truth, not the one you would like to be true. If you sit between two options, take the lower one. Optimism is how gaps stay hidden.
Write in the boxes. The written answers are worth more than the ticks.
At the end, a short reading shows you how to interpret your own answers.

If a question does not fit the way your organization works, skip it. A skipped question is information too. “Organization” means your company, firm, department or agency.

PART 1

Where you stand today

Strategy first, technology later. This part is about intent: whether someone in your organization is steering this, or whether it is steering itself. Most leaders who answer truthfully tick more A’s and B’s than they expected. That is normal, and it is fixable.

1.1. Does your organization have a defined plan for AI, data or automation this financial year?

AWe talk about it now and then. Nothing is written down.BWe intend to do something, but there is no plan and no deadline.CA plan exists on paper, but it does not really drive decisions.DA written plan exists, someone owns it and progress gets reviewed.

1.2. Who owns this area day to day?

ANobody. It surfaces only when something forces it.BMe, on top of everything else I already carry.CA senior person holds it alongside their main job.DA named person with the time, the authority and a budget.

1.3. Is there money set aside for it?

ANo.BNo, but we would find money for the right thing.CAn informal amount, decided case by case.DA real budget line, agreed in advance.

Gut check: with no one else in the room, how clear is your organization’s direction on AI and data?

No direction12345Completely clear

1.4. What have you already tried? Tools, pilots, consultants, something built in-house. Tick what happened.

Still in use, and clearly paying for itselfUsed by a few people, quietly fadingTried, then abandonedDiscussed at length, never startedNothing tried yet

1.5. When AI comes up in leadership conversation, it is usually:

AIt has not really come up.BA worry about falling behind, with no next step attached.CIdeas and enthusiasm that do not survive the week.DSpecific problems, with owners and numbers attached.

1.6. The last time this came up seriously: what triggered it, and what happened next?

If your written answer ends with “and then it fizzled out”, hold on to that thought. It will matter in Part 4.

PART 2

How you deal with data

Instinct built your business, and nobody here is grading you against a textbook. The question is narrower: when you want a number, do you get it, is it right and does it arrive fast enough to matter?

2.1. Where does your organization’s data actually live?

AIn people’s heads, inboxes and personal spreadsheets.BIn spreadsheets and a few systems that do not talk to each other.CIn core systems, but combining them into one picture takes real effort.DMostly in one place. Combined views are routine.

2.2. How do significant decisions actually get made?

AExperience and instinct. Data is rarely in the room.BInstinct decides, and numbers arrive later to justify it.CA mix: data when it is easy to get, instinct when it is not.DThe numbers are on the table before we decide.

2.3. It is mid-afternoon and you ask for one number: say, your most profitable customer, project or route last quarter. What happens?

AWe could not produce it with confidence.BDays of manual work, and I would have to push for it.CIt arrives within a day, if I chase the right person.DMinutes. Someone pulls it up, or I do.

2.4. When numbers do arrive, how much do you trust them?

AThey are often wrong, or two sources disagree and nobody knows which is right.BDifferent teams bring different versions of the same figure.CBroadly right, but I still have someone double-check.DTrusted enough to act on without a second look.

2.5. The tools your team works in every day:

AWe fight them. Workarounds everywhere.BWe have outgrown them, but we manage.CAdequate, though we use a fraction of what they can do.DThey fit the way we work.

How confident are you that the numbers reaching you describe reality?

Not at all12345Completely

2.6. Which number do you ask for most often, and how long does it usually take to reach you?

If Part 1 felt uncomfortable and this part felt fine, or the reverse, that gap is worth noticing. The reading below shows you how to interpret it.

PART 3

The dreamstate

Forget budgets, systems and skills for one page. Nothing here has to be feasible. The unedited version of what you want is the most useful thing you will write today, so write it before the practical voice in your head wakes up.

3.1. Every constraint removed: how would a big decision get made in your organization? Who would know what, and when?

3.2. One report, insight or prediction lands on your desk every Monday morning, always complete, always right. What does it tell you?

3.3. Three years from now, everything has gone right. What can your organization do that it cannot do today?

3.4. And the first thing you would stop doing by hand?

PART 4

Where it hurts

Common problems, in plain words, grouped into four areas. Tick every line that sounds like your organization. Do not overthink it: if you winced, tick it.

Predicting and deciding AI and machine learning

We are sitting on years of records across sales, operations and customers, and we have never learned anything from them.We find out about problems after they have cost us money, never before.Forecasting here is one person’s spreadsheet and a good memory.People around us keep talking about what they do with AI, and privately we do not have an answer to it.

Seeing the business clearly Analytics and visualization

Someone spends days every month assembling the same report by hand.Two teams bring two different versions of the same number to one meeting.Whatever I read about the business is weeks old by the time I read it.The data exists, but the one person who can pull it is always busy.

Work that repeats itself Automation

Skilled people spend hours every week moving data between systems by hand.The same information gets typed in more than once, by more than one person.When one particular person goes on leave, a whole process stops.Mistakes from manual steps keep coming back, no matter how careful everyone is.

Machines, vehicles and sites IoT and connected systems

We own equipment, vehicles or sites and cannot see what they are doing right now.We hear about breakdowns from a phone call, after the fact.Data from the field arrives on paper or in photo messages, and someone retypes it.Maintenance runs on a calendar, or on failure. Never on actual condition.

Now look back over your ticks.

4.1. Which single line costs you the most, in money, hours or sleep? Copy it here.

4.2. Roughly what does it cost you in a year? A rough guess is fine: money, hours or missed chances.

4.3. And why has it not been fixed yet?

READING YOUR OWN ANSWERS

Your map.

No points and no grades. A workbook cannot see everything, and a few pages of ticks are a map, not a verdict. But a map you drew yourself is hard to argue with.

First reading: your letters

Second reading: the tilt

Third reading: the cluster

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Where youactually stand.

Where youactually stand

Where you
actually stand.

Where you
actually stand