Table of Contents
We at JetRuby leverage AI task automation, making every stage of product development more efficient. A prototype that used to take a week to create is now delivered to clients much faster, showing how productivity improves with automation.
What is the main benefit of using AI in automating tasks?
The answer is clear: saving time = saving money. Developers can shift their focus from boring routine AI tasks to more creative problem-solving. The main benefit of using AI for task automation is clear: saving time = saving money. Developers can shift their focus from boring tasks to more creative problem-solving.
Market analysts agree on the potential for growth.
McKinsey reports that generative AI could add between $2.6 trillion and $4.4 trillion to the global economy yearly, with automation and productivity as key factors.
Businesses want to use AI because it has great potential. But how to use AI to automate tasks effectively?
Let’s break down the key advantages.
Key Takeaways
- AI Helps Productivity as it automates repetitive tasks and speeds up delivery (for example, prototyping is cut from 1 week to 4 days).
- AI supports human expertise, but it does not replace it. Human validation = quality and security.
- Security Comes First: Strict protocols, like data anonymization and access controls, prevent leaks and breaches.
- Knowing how to use AI to automate tasks is now a key skill. Training helps with effective adoption.
- We invest in custom AI models to fit our company’s standards and workflows.
- Responsible adoption matters more than hype. We focus on measurable improvements (like speed and cost) while managing risks.
What are the main benefits of using AI in automating tasks?
In early 2022, when AI tools started being trendy, we at Jetruby realized this was a major change in how software would be built.
The choice was clear: we could either lead this change or risk falling behind.
This insight turned out to be very accurate.
We adopted a clear and careful strategy for using AI, so we can complete projects faster while keeping high-quality standards.
The secret? A balanced approach that combines:
- Selective automation of routine development tasks
- Enhanced human oversight where it matters most
- Continuous upskilling of engineering teams through personalized development plans
After 10+ years of refinement, we know what success looks like for our teams. Our education system teaches these methods from the beginning, helping new hires build skills that fit our workflows.
For AI tools and new workflows to work well in our production cycle, they need to be based on the same foundation of knowledge and standards we have created.
Without this foundation, we would end up with the same generic outputs as other companies that use only basic AI solutions. Let’s be honest — responses from standard agents miss distinct details and personal experience.
This is why the real change isn’t about the AI itself, but about improving our training programs. We need to teach our teams how to use these tools effectively by setting best practices and consistent methods for implementation.
In the long run, we will create processes where AI tools will be smoothly integrated throughout the entire software development life cycle (SDLC).
As for back-office operations, there’s significant potential for automating business tasks. This includes data classification, entry, compliance checks, and analyzing meeting effectiveness from transcripts. We are focusing our efforts on these areas right now.
When we look at our production cycle, one important focus is security.
AI tools are becoming widespread and are often used carelessly, which leads to serious risks. Issues like using questionable third-party tools or accidentally breaching NDAs can lead to sensitive data being exposed.
Our approach is different from the industry norm.
We aren’t adopting AI to save time. We are building something more valuable: a reputation for using AI responsibly.
- Junior team members cannot use AI tools without supervision. They must first receive proper training on how to use AI to automate tasks and check their results.
- We only use AI for suitable tasks and keep human oversight where needed. We continuously improve our models to ensure high quality.
- We have strict rules to track what client data is shared and which AI systems receive it.
Here’s a key truth: nobody fully understands how today’s large language models work. We can’t ensure that confidential data put into these systems won’t leak to competitors.
That’s why we invest heavily in AI security now, before major breaches occur. While others rush to use tools like Copilot without considering risks, we focus on:
- Custom security protocols
- Model vetting processes
- Data flow monitoring
This careful approach reduces risk and gives us an edge in the market. Enterprise clients value working with a partner that prioritizes data integrity alongside speed.
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How JetRuby Uses AI to Automate Tasks and Boost Productivity
At JetRuby, we integrate AI where it makes sense, always balancing innovation with practicality. We do not mindlessly chase trends, we try to find where AI can actually improve how we work: speeding up development, refining research, or automating repetitive tasks.
The AI Tools We Rely On Daily
We use ChatGPT with strict anonymization for business analysis to quickly parse data and generate insights.
Our developers often work with Cursor IDE and GitHub Copilot, though we encourage them to pick the models they’re most comfortable with. Right now, that usually means:
- OpenAI’s latest models (for general coding and automation)
- Anthropic’s Claude (when we need more nuanced reasoning)
- Interplexity (for digging into client research and market trends)
But we’re not plugging into APIs and calling it a day.
The real magic happens when we tailor these tools to our workflows.
Customizing AI with Model Context Protocol (MCP)
One of our most promising experiments is Model Context Protocol (MCP), a way to run small local servers that feed our own standards, best practices, and project context into AI responses.
Think of it like giving ChatGPT a JetRuby playbook before it answers.
We’re testing MCP in a few key areas:
- Backend: Helping engineers write smarter database queries.
- Frontend: Turning Figma designs into cleaner, more accurate code.
- QA: Automatically generating test cases from JIRA tickets.
- Documentation: Transforming loose requirements into structured specs, much like our deep dive into AI code review enhancements.
Internally, we’re trying two approaches:
- A full AI pipeline — where each step in development flows into the next with AI assistance.
- Surgical AI boosts — focusing only on bottlenecks where AI makes the biggest difference.
The big question — which one gives us the best return on effort?
We at Jetruby strongly believe AI should solve problems, not add complexity, so we’re careful not to overengineer.
AI is not a quick fix for every problem. Today, a skilled developer can often do complex tasks faster than AI. However, AI excels at automating repetitive tasks and speeding up research.
So, AI tools help us, but they don’t replace skill.
Some engineers face a funny problem: they feel less driven to dig deeper when they don’t see big improvements. Most AI models, like OpenAI and Claude, work similarly, so the real difference comes down to how you use AI to automate tasks.
Our view?
Today’s AI is like early Photoshop. It is powerful when used correctly, but it is not a simple shortcut. We encourage our team to develop their skills now because these tools will improve.
Open-Source vs. Paid Models: Where Things Stand
Open-source options like Llama are getting better, but they still can’t match the reliability of paid solutions. We are watching their progress, but for now, fully integrating AI throughout the development process, where business analysts, developers, and quality assurance teams can easily share AI-processed work, is still in progress.
Where AI Already Shines: Automating the Routine
While dev work is still a balancing act, back-office tasks are where AI delivers clear wins today.
We’re already using it for:
- Transcribing meetings (video → audio → searchable text)
- Checking compliance (automated text analysis against standards)
- Generating reports (pulling data into digestible summaries)
These aren’t flashy, but they save real time, exactly the kind of low-effort, high-return use cases we love.
We’re excited about AI, but we’re not handing over the keys. The best results come when smart people use smart tools, not when tools try to replace people. So we’ll keep experimenting, measuring, and refining. Because at the end of the day, AI should work for us, not the other way around.
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AI Task Automation: Implementation Journey
At JetRuby, we’ve taken a deliberate approach: no reckless experimentation, but no hesitation either.
Instead, we’ve integrated AI the same way we handle any major tech shift: through structured discipline, real-world testing, and a focus on measurable impact.
Our engineering teams have always been organized into practice groups, each specializing in a core technology stack.
These groups, led by senior engineers, continuously refine our approaches to problem-solving, tools, and training. We didn’t tear down this structure when AI started gaining traction.
Security was (and still is) a top concern. The tech industry tends to swing between two extremes: some companies dive headfirst into AI with little regard for data risks, while others avoid it entirely out of fear.
We chose a middle path: cautious but proactive adoption. Right now, we’re handling security in two ways:
- Strategic vendor partnerships. We only work with AI providers that meet strict data protection standards, backed by airtight agreements.
- In-house tool development. We’re building our solutions to automate IT tasks in cases where external tools don’t cut it.
We know stricter policies are coming, and we’re already preparing for them. But in the meantime, we do not wait around and implement AI with the right safeguards.
When we first introduced AI tools, reactions were mixed. Some engineers were excited, others were hesitant, preferring tried-and-true manual methods.
We didn’t force the issue. Instead, we showed, rather than told.
We gave developers small, concrete tasks: “Try building this feature with AI assistance.” or “Use this tool to automate part of your workflow.”
The results spoke for themselves. Even the biggest skeptics started seeing the value, not because AI solved everything perfectly, but because it saved real time on repetitive tasks.
That said, we’re also realistic about the future. AI is no longer a nice-to-have — it’s becoming a core skill. Soon, engineers who don’t leverage AI will struggle to keep up with those who do.
That’s why we’re gradually factoring AI proficiency into performance reviews. Yet, it doesn’t mean we force adoption, but rather staying competitive.
With multiple teams working across different tech stacks, consistency is key. To make sure AI adoption wasn’t fragmented, we set up regular discipline lead meetings where teams share:
- What’s working — Which tools deliver the best results for specific tasks
- How to implement them — Best practices for integration
- How to train effectively — Onboarding strategies that actually stick
We also updated our skills matrix to include AI competencies, making it clear that it’s part of how we work now.
Some companies obsess over metrics like “percentage of AI-generated code.” We don’t.
Why?
Because today’s benchmarks might be irrelevant tomorrow. Instead, we focus on real-world improvements.
For example, prototyping used to take a week. Now, with AI-assisted workflows, we’re delivering in 3-4 days. That’s the difference that matters, not abstract numbers, but tangible speed and quality gains.
AI isn’t magic. It’s a tool, one that’s constantly evolving. Our approach reflects that: stay flexible, prioritize real impact over hype, and make sure adoption happens in a way that’s sustainable, secure, and genuinely useful. The tools will keep changing, but our goal won’t: building better software, faster. And right now, AI is helping us do exactly that.
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Measuring Success: Achieving Faster Delivery
So, what tasks can AI perform?
As we at Jetruby started integrating AI into development workflows, we’ve seen measurable performance gains, particularly in prototyping and reducing time-intensive tasks. One standout example comes from a complex eCommerce project with a highly intricate database architecture.
Even senior developers found crafting efficient queries for product reports challenging. After multiple experts spent considerable time optimizing a billing data query, they turned to ChatGPT for assistance. The AI generated a fully functional SQL query in just 30 minutes. Previously, similar tasks would have taken hours or even days.
AI performs well in structured environments like SQL, where requirements are clearly defined.
But the efficiency gains extend beyond database optimization.
In another case, a non-developer on our team created comprehensive time-off dashboards in a single evening using AI. He simply described the data structure and requirements to AI and bypassed the need for deep SQL expertise. As a result, he connected Superset BI tools to the company’s internal ERP system with minimal manual coding.
The productivity improvements have impressed clients, but they’ve also sparked some industry misconceptions.
With AI’s ability to generate functional code snippets, some non-technical stakeholders assume developers are becoming obsolete or that teams not using AI are already at a disadvantage.
We emphasize that this isn’t the case. Current AI tools still heavily require skilled human oversight.
However, when clients see tangible results, they immediately recognize the value. Rapid dashboard development and database reporting have been particularly persuasive, and showcased the main benefit of using AI in automating tasks and how AI can cut costs and maintain quality.
We developed a Farming Management System (FMS) that closely tracks each cow, without manually logging weights and milk quantities.
Small IoT sensors on collars and smart scales in the milking parlor collect data. This data goes into a Spark-powered system, which stores everything in a PostgreSQL database. Then AI models analyze this data.
A simple neural network and a support vector machine (SVM) predict drops in milk production or early signs of illness. A simplified YOLOv4-Tiny model monitors barn videos to spot unusual behavior, like bulls pacing or cows being isolated.
Since everything runs in Docker on Amazon Web Services (AWS), these processes happen in real time. When the AI detects a problem, like a predicted decrease in milk yield or a stressed cow, it sends SMS and email alerts and can adjust feeding plans automatically.
This ongoing cycle of sensing, analyzing, and acting has led to a 28% increase in milk yield and a 35% reduction in veterinary costs, while automating about 95% of routine monitoring tasks.
AI in the Farm Management System (FMS) makes checks quicker and easier using data instead of manual work. This helps farmers take better care of their animals, saves them time, and increases their productivity.
Our decision to integrate AI now was a forward-thinking move, as we recognize that organizations without hands-on experience will struggle to catch up as AI evolves. By embedding AI into workflows today, we are future-proofing our teams’ capabilities, so our company remains competitive when the technology matures.
Pre-sales engagements have seen some of the most dramatic improvements too.
With the help of generative AI, business analysts now rapidly refine client ideas, create prototypes, and clarify requirements much faster. But of course, the human element remains irreplaceable.
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Challenges & Lessons Learned
At JetRuby, we have learned that we get the most value from AI when human experts guide its use. This principle shapes our work in presales and development projects, and it will soon affect our project management systems.
At first, we struggled with inconsistent output quality and a lack of standard processes. These issues stemmed from our company culture and our technology. Although we have made progress, we understand that as we expand AI across the company, we’ll have new challenges.
One major challenge in managing workflows with AI assistants is that human oversight can be unreliable. We have seen this with clients who rely on in-house development teams. Developers, pressed for time or overly trusting, often accept AI-generated code without checking it properly, despite knowing how to use AI to automate tasks.
The consequences are stark:
- Poor Quality Visibility — Flaws become glaringly obvious upon review.
- Unprofessional Output — Sloppy or nonsensical implementations damage credibility.
- Security Risks — The most dangerous pitfall of all.
The risks go beyond just being inefficient. AI models, especially those that generate code, can create false information, like fake package names, APIs, or even entire libraries. This is dangerous because malicious actors can quickly take advantage of these mistakes; that’s exactly why we emphasize how to use AI to automate tasks responsibly.
There are cases where hackers watch AI-generated code, find these made-up dependencies, and then publish harmful packages with the same names. When developers, especially those who skip checking their code, use these infected dependencies, the results can be disastrous:
- Cryptocurrency miners can silently drain resources
- Data breaches expose sensitive information
- Reputational and financial damage far exceeds the cost of the project itself
At JetRuby, we have learned that we need clear guidelines for using AI. While AI can speed up development, it can also pose risks if we don’t have human oversight. As we increase our use of AI, we are focusing on:
- Mandatory review protocols for AI-generated outputs
- Developer training to combat complacency
- Security-first dependency management
The path to effective AI adoption is augmenting human judgment.
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Future Outlook
The way we build software is changing.
At JetRuby, we see AI task automation tools as a helpful partner for developers, not a replacement. This collaboration will reshape our work, favoring teams that use it effectively.
In the coming years, most coding will likely be done with help from AI, and engineers will take on more strategic roles. Two important job types will emerge:
- Code Refiners. These developers focus on details and improve AI-generated code to ensure it meets high performance, security, and maintainability standards.
- System Architects. These planners design how complex systems work together. They create the frameworks that allow AI-generated parts to function smoothly.
Imagine applications that are built from smart, self-adjusting modules. These can be updated using natural language prompts instead of manual coding.
While we aren’t there yet, our current focus is on integrating AI into our existing workflows, from the first idea to the final launch. The main challenge is to ensure the switch between human and machine contributions is smooth across all teams.
We also look at how AI can improve project leadership and team management. AI can help us identify risks sooner, allocate resources more effectively, and keep projects on track — something we’ve already started implementing in our CTO advisory services.
Today’s AI tools can write good code. However, they struggle with context. Even the best models find it hard to understand large, complex projects. The real breakthrough will happen when AI can fully understand:
- A project’s entire codebase (not just snippets)
- Up-to-date business requirements
- The full suite of tests and quality checks
- All the subtle, unwritten rules that make our solutions unique
When AI can reach this level of awareness, developers will focus mostly on time guiding the process, like conductors leading a group of smart tools.
Where JetRuby Is Heading
In the next two to three years, we will focus on two main goals:
Building Our Own AI Advantage
We have a vast collection of high-quality code that we will use to train AI models. These models will think like us and generate code that meets JetRuby’s standards and best practices.
Letting AI Handle the Heavy Lifting
AI will soon help with writing code and quality control. It will automatically check reports, logs, and outputs.
We are already testing AI in training and development, and we have seen some exciting early results:
- Instant training videos. AI helps create polished instructional videos from slide decks in minutes instead of days.
- Smarter onboarding. AI will generate learning materials tailored to our internal processes.
- AI presenters. This allows experts to focus on their knowledge while AI handles the presentation.
The key idea?
Humans focus on the “what” and “why”. AI handles the “how.”
The AI field changes daily, and we’re ready to adapt. One thing will always stay the same: JetRuby’s commitment to leading the way.
Our CTO as a Service is about empowering your team with the right leadership, structure, and vision.
If you are new to AI or want to improve your existing solutions, our experienced team is here to help you as trusted partners.
We take the time to understand your unique challenges, then roll up our sleeves to:
Simplify the Complex
We assess your current workflows, team dynamics, and tech stack, cutting through the noise to focus on what matters.
Build with Confidence
From strategy to execution, we help implement AI initiatives smoothly and ensure they align with your business goals.
Grow with You
We don’t set things up and leave. We stay to refine, adapt, and future-proof your team.
Think of us as your tech leaders who are ready to help. We combine AI’s accuracy with the knowledge of skilled professionals.
That’s a future we can create, where you can seamlessly automate business tasks and leverage AI for everything from presales to project delivery.
Please don’t hesitate to contact us if you need professional advice.
