Automation and AI applied to work
Don't automate work that shouldn't exist.
We identify tasks and processes where automation, integrations or artificial intelligence can reduce manual work, errors, waiting time and operational dependency.
But before adding technology, we ask a more basic question: Does this task really need to keep existing?
Having a task you can automate doesn't mean it's worth automating.
Many initiatives start with the tool:
“We want to adopt AI.”
“We need a bot.”
“We want to automate this process.”
“We need to build a system.”
But technology can run a badly designed process perfectly.
Before automating, we ask
- 01Why does this task exist?
- 02What result does it produce?
- 03Who uses that result?
- 04Why does it need these steps?
- 05Can it be eliminated?
- 06Can it be simplified?
- 07And only then: can it be automated?
Technology should solve a need. Not add another layer of complexity.
What we can automate
We look for repetitive, predictable, low-human-value work.
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01
Data entry and transfer
Information that is copied by hand today between spreadsheets, forms, systems or platforms.
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02
Document generation
Reports, summaries, documents and communications built over and over from available information.
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03
Repetitive questions
Internal or external questions whose answer depends on structured, available information.
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04
Follow-ups
Reminders, statuses, deadlines, requests and tasks that currently depend on manual follow-up.
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05
Classification and processing
Emails, requests, documents, cases or forms that need to be identified and routed.
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06
Integrations
Manual processes that exist because two tools don't share information.
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07
AI-assisted analysis
Large volumes of information that need to be summarized, compared, analyzed or prepared for a human decision.
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08
Workflows
Sequences of tasks, notifications, validations and actions that can run on their own under defined rules.
What we shouldn't automate
Some tasks need to stay human.
We don't try to replace human involvement across the board. In some activities, the value lies precisely in:
- Judgment
- Negotiation
- Relationships with other people
- Creativity
- Understanding context
- Accountability for a decision
- Handling complex exceptions
In those cases, technology can reduce the surrounding work so the person can spend more capacity where they truly add value.
Automating the work around a decision can be worth more than automating the decision.
How we decide
An automation opportunity has to justify its existence.
We don't automate just because it's technically possible. We assess each opportunity by:
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01
Frequency
How often is it done?
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02
Time
How much capacity does it use?
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03
Repetitiveness
How much does it vary from one time to the next?
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04
Rules
Can it be described with clear enough criteria?
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05
Errors
How much rework does it create today?
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06
Volume
How many people, cases or transactions does it affect?
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07
Risk
What happens if the automation fails or makes a wrong decision?
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08
Investment
How much does it cost to implement and maintain?
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09
Return
How much capacity, time or cost could it recover?
Being able to automate something doesn't mean you should.
Not everything needs software development
First we look for the simplest solution that solves the problem.
An opportunity can be solved by:
- 01 Configuring a tool the company already uses more effectively.
- 02 Connecting existing tools.
- 03 Automating part of the workflow.
- 04 Adopting a solution available on the market.
- 05 Using AI on a specific task.
- 06 Building software when no existing alternative properly meets the need.
Building software is an option, not DeepWork's product.
AI
AI where it makes sense. Not wherever it fits.
Artificial intelligence greatly expands the tasks that can be assisted or automated. It can help to:
- Classify information
- Extract data
- Summarize documents
- Analyze large volumes of text
- Draft content
- Detect patterns
- Answer questions from available information
- Prepare information for decisions
- Coordinate actions across tools
But adopting AI also adds requirements: information quality, oversight, security, costs, exceptions and accountability.
That is why DeepWork looks at AI as part of the system of work, not as an isolated tool.
Human + AI
It isn't only about what AI can do. It is about how work should change when AI exists.
The biggest impact doesn't always come when a machine replaces a whole task. It can come when we redistribute the work.
Before
After
Automation reduces work. Redesign decides which work remains.
An example
From a manual task to a simpler workflow.
Before · Weekly report
- 5 people send information
- 1 person consolidates spreadsheets
- Fixes formats
- Looks for missing data
- Builds the report
- Sends it by email
4 hours a week
First DeepWork question
Is the full report still needed?
Data nobody uses is removed.
After
- Existing systems
- Data consolidated automatically
- AI prepares a summary and flags variations
- The owner reviews exceptions
- Report available
40 minutes a week
Illustrative example. The opportunity and impact are measured in each organization.
What matters isn't the hours recovered. It is that we first removed unnecessary information and then automated what was left.
Economic impact
An automation should be able to explain what it improves.
Before implementing, we set a baseline. Depending on the case, we can measure:
- Monthly hours used
- Estimated process cost
- Number of manual interventions
- Cycle time
- Errors and rework
- Volume processed
- Response time
- Capacity freed up
After implementing, we measure again. The final question isn’t “does the automation work?”.
Did the way of working really improve?
Implementation
From opportunity to a working solution.
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01
Analyze
We understand the task and document the current situation.
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02
Simplify
We remove unnecessary steps and requirements before designing the solution.
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03
Design
We define what technology does, what a person keeps doing and how exceptions are handled.
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04
Implement
We configure, integrate, automate or build according to the agreed solution.
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05
Validate
We test how it works in real conditions.
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06
Measure
We compare results with the starting point.
Technology without unnecessary dependency
We use technology as a means, not a destination.
Depending on the problem, a solution may use existing tools, APIs, integrations, automations, artificial intelligence or specific development. The choice depends on the context, the systems already in place, cost, maintenance and the expected return.
We don't propose building something new when an existing solution solves the problem well.
There may be an opportunity if…
- “We have people copying information between systems.”
- “Everything depends on a spreadsheet.”
- “We produce the same reports every week.”
- “We get a lot of repetitive questions.”
- “Some processes need too much follow-up.”
- “We want to use AI, but we don’t know where it would add value.”
- “We’re thinking about building a system.”
- “We have tools that don’t talk to each other.”
- “We hire people to absorb admin tasks.”
- “We already automated, but there is still too much manual work.”
“We’re about to buy technology and want to know whether it will really solve the problem.”
Frequently asked questions
About automation and AI.
What kind of tasks does DeepWork automate?
Repetitive, predictable, low-human-value work: data entry and transfer, recurring documents and reports, repetitive questions, follow-ups, request classification, integrations between tools and AI-assisted analysis. Before automating, we check whether the task should exist or whether it can be simplified.
Does DeepWork build software?
When it's justified, yes. But first we look for the simplest solution: configuring a tool you already use better, connecting existing systems, automating part of the workflow or adopting a market solution. Building software is an option, not our product.
When don't you recommend automating?
When the task shouldn't exist, when it can be simplified first, when it varies too much to be described with clear rules, when the risk of an error is high or when the investment isn't justified by the capacity it would recover.
How do you know an automation was worth it?
Before implementing, we measure the starting point (hours, manual interventions, errors, times) and then we measure again. The question isn't whether the automation works, but whether the way of working improved.
We're about to buy a tool. Can you help us decide?
Yes. We can analyze the work that tool is supposed to handle and check whether it really does, whether the process should be simplified first or whether there is a simpler alternative.
Automation and AI
Before automating,
make sure you're solving
the right problem.
We analyze the work, estimate the impact and define what should be eliminated, simplified or automated.