A team identifies a process that eats up hours and decides to automate it. Right away, every requirement shows up: cover every type of operation, connect every source and handle every exception. The list grows. The first result drifts further away. Minimum viable automation offers a different path.<\/p>\n
To get started, it helps to think in terms of an MVA: a Minimum Viable Automation. It is the smallest piece of an automation that can already be put to use and handle real work.<\/p>\n
The idea takes the logic of the MVP and applies it to processes: build a useful first version, check that it works and expand it with what you learn.<\/p>\n
The question is simple: which part can we automate first so that someone stops doing that work by hand?<\/p>\n
On paper, automating an entire process looks like the most efficient option. In practice, tackling everything at the same time tends to bring three problems.<\/p>\n
Value takes a long time to show up.<\/strong> While the full flow is being built, the team keeps working as before. Effort has been invested, but nobody feels the difference yet and the finish line looks far away.<\/p>\n Problems get tangled.<\/strong> A report changes format, a permission is missing and an exception nobody had mentioned appears. With so many moving pieces, it is hard to tell what is failing and how to fix it.<\/p>\n The project loses priority.<\/strong> When results don’t arrive, confidence weakens. Day-to-day urgencies take their place back and the automation is left pending.<\/p>\n An MVA shortens the distance between effort and results. It lets you start using what you built, be happy, get results and correct course before investing too much.<\/p>\n A minimum viable automation must meet four conditions:<\/p>\n “Minimum” describes the scope. The part that goes into use has to work with the reliability the task requires.<\/p>\n There are three ways to slice a process to reach an MVA.<\/p>\n If the process involves different clients, operations, countries or types of request, you can start with one.<\/p>\n It’s best to choose a frequent case, with relatively clear rules and enough volume for automating it to make a difference. That first version lets you test the approach and build a foundation that can later be adapted to other cases.<\/p>\n Sometimes the first win is in a preliminary task: gathering files, organizing information or preparing data for a review.<\/p>\n Even if the rest remains manual, that stage can already save work. Consolidating several reports into a single spreadsheet, for example, can be an MVA if it eliminates a task someone repeats every week.<\/p>\n A first version can handle the usual cases and leave the exceptional ones for human review. It must be clear what it can process, what it should route elsewhere and who takes care of what’s pending.<\/p>\n The more complex parts can be added later. Critical dependencies are reviewed at the start: if everything depends on accessing a system or having certain data, you need to confirm it’s available before moving forward.<\/p>\n At one of our clients in the agricultural export industry, a person on the financial control team spent about eight hours a month validating revenue recognition.<\/p>\n The work consisted of cross-checking several reports and validating the month’s sales by type of transport. It had to be completed between the first and second business day after closing: a short window for a task that demanded precision.<\/p>\n That was the process they decided to automate in the Booster we ran together. The person who carried it out every month built the automation with the support of our mentor. Their knowledge of the work made it possible to identify the rules, recognize the exceptions and decide where to start. Mentor support is our way of teaching people how to think and work with AI.<\/p>\n The MVA logic made it possible to find the first point in that journey that could already be used and reduce manual work. From there, each expansion would have a concrete foundation to build on.<\/p>\n The solution was built with Copilot Studio and Excel, tools the company already used.<\/p>\n The automation moved forward and, more importantly, the person learned to translate their knowledge of the work into rules, tests and decisions, and to adopt AI in their day-to-day work.<\/p>\n Defining an MVA requires judgment: understanding which result matters, recognizing dependencies and deciding what can wait.<\/p>\n That judgment develops by working on real problems. In Booster AI, people build solutions for tasks in their own role, guided by experts. Each step forward improves a process and builds the capacity to take on the next one.<\/p>\n MVA stands for Minimum Viable Automation. It is the smallest piece of an automation that can be put to use, handle real work and be validated.<\/p>\n The MVP applies to product development and seeks to validate a proposition with a first version. The MVA applies that logic to process automation: it narrows the scope to test its usefulness in real work and expands it based on experience.<\/p>\n With a frequent task, available information, reasonably clear rules and a result that’s easy to check. It can be one type of case or one stage of the process.<\/p>\n Yes. It can automate part of the journey and keep reviews or manual tasks in the rest. What matters is that the boundaries are clear and that the automated part already adds value.<\/p>\n It depends on the scope, the data and access to the tools. Narrowing the problem makes it possible to aim for a useful first delivery in a few sessions, although the timeline should be assessed case by case.<\/p>\n The person who usually performs the process, supported by someone with automation experience. Together they can assess which part adds the most value, what it needs to work and how to check the result.<\/p>\n Automating a large process starts with choosing one part that delivers practical value on its own. A Minimum Viable Automation (MVA) lets you put an initial solution to work, validate its impact, and expand it based on what you learn. Learn how to define your MVA and why this approach delivers results sooner while building your team\u2019s capabilities.<\/p>\n","protected":false},"author":15,"featured_media":715,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[23],"tags":[],"class_list":["post-718","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en"],"yoast_head":"\nWhat a minimum viable automation needs<\/h3>\n
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How to find your first minimum viable automation<\/h3>\n
1. Pick a single type of case<\/h4>\n
2. Solve one stage of the journey<\/h4>\n
3. Set the exceptions aside<\/h4>\n
A case: sales validation at an agricultural exporter<\/h3>\n
What did they do together?<\/h4>\n
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What changes when you work with an MVA<\/h3>\n
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A way of thinking you learn by doing<\/h3>\n
Frequently asked questions<\/h3>\n
What is an MVA?<\/h4>\n
What’s the difference between an MVA and an MVP?<\/h4>\n
Where should you start?<\/h4>\n
Can an MVA include manual steps?<\/h4>\n
How long does it take to be ready?<\/h4>\n
Who should define the MVA?<\/h4>\n
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