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G'day and welcome to Amazing Apps.

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I'm your host, Microsoft MVP, Neil Benson.

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I'm on a mission to help you master Agile

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practices and build amazing apps on the

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Microsoft Power Platform and Dynamics 365.

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Amazing Apps is the result of my

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curiosity and experiments with new

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ways of building amazing business

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apps and high performing teams.

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It's full of advice from my guests

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and examples from some of my work over

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the last few years leading business

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applications, teams, and practices.

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If you enjoy this episode, head

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over to https://amazingapps.Show

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for additional resources.

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You'll find more episodes of Amazing Apps

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as well as my videos, free workshops,

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ebooks, and my online training courses.

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In this episode, I'm going to try

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and persuade you to continue learning

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through experimentation as we

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embark on our AI adoption journey.

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We're going to be talking

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a lot about experiments.

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But let's start with smallpox.

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When's the last time you or

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someone you know well had smallpox?

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I bet it's never, at

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least I hope it's never.

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According to the World Health

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Organization, people have been trying to

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inoculate themselves against smallpox by

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exposing themselves to the virus since the

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15th century, maybe as early as 200 BC.

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In 1721, Lady Mary Wortley Montagu

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brought smallpox inoculation to Europe

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by asking that her two daughters

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be inoculated against smallpox.

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That was a practice she

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had observed in Turkey.

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By all accounts, she was quite

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the adventuress of her day.

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Fifty years later, in 1774, Benjamin

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Jesty makes another breakthrough.

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Testing his hypothesis, that

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infection with cowpox, a bovine

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virus which can spread to humans,

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could protect a person from smallpox.

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He and his family were spared from the

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smallpox infection that swept through

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the southwest of England in 1774.

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Jesty was one of several people

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thought to have practiced inoculation

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around this time, but the credit for

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inventing vaccination is generally

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given to our next character.

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Twenty years later, in 1796, a

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British doctor, Edward Jenner,

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conducted one of the bravest

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experiments I've ever heard of.

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He swabbed the ugly cowpox lesion

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of a milkmaid and used it to infect

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an 8 year old boy, James Phipps.

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If any of you know any 8 year old boys,

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this isn't a practice I would recommend.

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Phipps was unwell and suffered a local

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reaction, but he made a full recovery.

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So what did Jenner do?

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Two months later, in July 1776, he tested

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Phipps resistance by infecting him with

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matter from a human smallpox lesion.

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Cowpox in humans results in ugly

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lesions, often on the hands and arms and

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face, but it's mild and rarely deadly.

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Smallpox, however, is far more

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infectious and in 1776 it often

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resulted in a slow, painful death.

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It's reported to have been responsible

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for between 10 percent and 20 percent

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of all deaths in the 18th century.

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Whatever happened to 8 year old Phipps?

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Well, he remained in good health

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despite the smallpox exposure.

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He's considered to be the first

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person vaccinated against smallpox.

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And did you know, the word vaccination

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is derived from vacca, Latin for cow.

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This is a painting of

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Benjamin Jesty's cow, Blossom.

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The most famous cow in the

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world, at least in 1774.

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Other experiments since then by

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scientists have led to vaccinations

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against over 20 human diseases.

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Receiving vaccines has become routine

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for many of us, especially since 2020.

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Many of us wouldn't be here if our

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antecedents hadn't been vaccinated

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against smallpox and other deadly viruses.

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In 1996, I was studying biochemistry

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at the University of Edinburgh

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where I spliced the gene from green

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fluorescent protein, which is found

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in the jellyfish, Aquorea victoria,

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through a bacterial vector into

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yeast, saccharomyces cerevisiae.

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According to my professor, our

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experiments were related to gene

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targeting and cancer research.

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Through ultraviolet microscopy, we

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could see exactly where inside the

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yeast cell DNA was being expressed and

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proteins were subsequently located.

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My goal was just to make

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glow in the dark beer.

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Can you imagine  traffic

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cop with a UV torch?

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Honestly, officer, I

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haven't been drinking.

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But I found the conversational

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skills of baker's yeast to be pretty

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poor compared to C# developers.

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So I ended up pursuing a career

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with the IT crowd instead.

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But my passion for running

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experiments hasn't abated.

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Today, I'm the co founder of SuperWire.

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ai, a Microsoft partner and independent

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software vendor building engagement

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applications for superannuation funds on

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Power Platform, Dynamics 365, and Azure.

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I'm also the founder of Customery, an

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online training provider helping Microsoft

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teams adopt and master Agile practices.

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In both businesses, we love

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learning through experimentation.

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We start with a hypothesis, run a short

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experiment to test the hypothesis,

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review the results, and reassess our

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hypothesis to improve our knowledge.

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Instead of learning through experiments,

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lots of development teams attempt to

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design everything up front, in the

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belief that if we could just understand

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enough at the analysis and design

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phase, that everything will be alright.

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If you are analysing your users

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requirements up front, and designing

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your solution in advance, you're

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doing it at the point of peak

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ignorance, also known as Mount Stupid.

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At the start of your project,

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your team knows least about

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the users and their needs.

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And your users know least about

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the application you're building.

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Instead, if you can defer the requirements

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analysis until the last possible moment

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before you need to start developing

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the feature, you'll have learned a lot

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more about the requirements by then.

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Don't spend months analyzing

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requirements before development starts.

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Instead, work in short bursts.

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Keep the users involved in planning your

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experiments and reviewing the results.

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Learning through experimentation,

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working in short increments.

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Emergent analysis and design.

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Collaborating with users

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while building the app.

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We've got a label for working like this.

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It's called Agile Software Development.

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Especially the Scrum framework, which

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is founded on empiricism, which is

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the theory that we learn from the

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experience derived from our senses.

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That is, complex solutions

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can't be designed up front.

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We need to learn through experimentation.

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Let me give you an example

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of how we experiment while

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building Microsoft business apps.

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One of my teams is currently working

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for a Queensland government department.

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They register and monitor the

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training contracts for Queensland's

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trainees and apprentices.

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Every year, they process 90,000

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expense claims submitted by trainees

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who have attended an approved

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training class away from home.

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63,000 of these claims are PDF forms

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that are emailed to the department,

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and 17, 000 are submitted online via

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a webpage developed 12 years ago.

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A 12-year-old .NET web app is

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considered pretty modern by

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this department's standards.

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How could we improve the trainees

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expense claim experience and the

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department's processing efficiency?

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The first idea we had was a new

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mobile-optimized Power Pages site that

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would connect directly to Dataverse

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where the trainee data is already stored.

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We would automatically calculate

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the distance from the trainee's

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home to the training location.

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And we already provide a portal for

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the training provider to confirm

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the trainee attended the training.

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And then we would send the

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payment to SAP for processing.

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But the department can't force trainees to

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use a webpage, and many of them are handed

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PDF forms by the tutor at the end of the

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training course, and it's easy for them

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to get the form approved there and then.

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Instead, we're going to experiment

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with the Power Platform's AI builder

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by training a form processing model to

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read the PDF expense claim documents,

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turn them into a digital expense claim

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record in Dataverse so that we can

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process most of them automatically.

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We call this type of work a

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spike in our product backlog.

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Like a rock clamors spike.

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Our spikes allow us to safely explore

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a new rock face and discover if

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there is a path towards progress.

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At the same time, our risk of falling and

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dying is reduced because we time box the

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spike and contain it into a fixed amount

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of effort within our two-week sprint.

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During the sprint review, we'll report

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the results of our spike back to our

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stakeholders and invite their feedback

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about whether or not to pursue that

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solution or try another experiment.

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I remember Frieda, our CRM

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product owner at the University

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of New South Wales, wasn't happy

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that all our spikes went well.

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If every experiment succeeds and proves

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your hypothesis, said Frieda, then it's

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because your experiments were too safe.

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It's only when half of your spikes

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fail do you know that you're

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being bold enough and building an

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amazing new business application.

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Our government department is also

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considering implementing a new business

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rules engine to replace the 20 year

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old rules engine that supports the

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legacy PowerBuilder application

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we're replacing with Power Apps.

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When a new training contract is

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submitted to the department, they need

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to validate the trainee's details,

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the employer's details, the workplace

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location, the contract dates, the training

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organization, the training course.

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There are hundreds of validations

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to perform on each contract, and

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thousands of rules in the rules engine.

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Instead of a business rules engine with

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a fixed set of deterministic rules, could

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we use AI to validate training contracts?

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Could we build a model of valid training

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contracts, then train a co pilot to

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spot invalid training contracts, and

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ask it to validate all the new training

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contracts coming into the department?

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Arguably, this approach is not actually

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artificial intelligence, it's machine

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learning, because the system will be

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identifying patterns in the contracts

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provided to it And improving its

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decision making capability based on

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our feedback about new contracts.

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Whatever we call it, I think it's

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an interesting hypothesis to test.

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What's the smallest, useful experiment

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we could conduct to help us advance our

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knowledge about whether AI, really it's

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ML, could validate training contracts

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without a hard coded rules engine?

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Well, we start Sprint 1 on Monday.

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If you follow me on LinkedIn or

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subscribe to my podcast, Amazing

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Apps, I'll let you know the results.

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I love building in public.

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Until then, experiment.

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Find a hypothesis, run a test,

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learn from the results, share the

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outcomes with your stakeholders, or

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better yet, share them in public.

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But, please don't experiment on 8

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year old boys or infect anyone with

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a deadly disease in your attempts

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to harness artificial intelligence.

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Thanks for listening

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or thanks for watching.

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I hope you enjoyed this Amazing

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Apps episode and found it useful.

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If you want to accelerate your

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career by building amazing Power

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Platform and Dynamics 365 apps your

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stakeholders love, then join me

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in my free interactive workshop.

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Inside, I share the three secrets

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to successfully using Scrum to build

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agile apps so that you can deliver

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projects faster, under budget,

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have more fun, and get promoted.

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Register today at

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00:12:00,316 --> 00:12:03,336
https://customery.com/3secrets.

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You'll also find that link in the episode

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description, in your podcast player,

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or in the YouTube video description.

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Until next time, keep experimenting.