Will AI Replace Developers? What Software Development Managers Really Need to Know

Will AI Replace Developers? What Software Development Managers Really Need to Know

AI, DevOps, Software Development, Technologies

By Rod Aburto
Business leader holding AI hologram in hands, symbolizing the future of developers.
The conversation used to be about offshore vs nearshore. About Agile vs Waterfall. About backend vs frontend. But lately, Software Development Managers everywhere are asking a very different kind of question:
Will AI replace my developers?

It’s a question that comes with real anxiety. Tools like GitHub Copilot, ChatGPT, and other generative AI platforms are writing code faster than ever before. Code review, documentation, even whole applications—now seemingly “automated” in ways that were unthinkable five years ago.

So, should we be worried?

In this post, I want to unpack that fear—and offer a framework for thinking clearly about what’s changing, what’s not, and how Software Development Managers (SDMs) can lead through this pivotal moment in tech.

A Short History of Developer Disruption

If you’ve been in tech long enough, you know this isn’t the first time developers have faced “extinction.”

  • In the early 2000s, people said offshoring would eliminate the need for in-house engineers.
  • In the 2010s, we heard “No-code/low-code” would replace dev teams entirely.
  • In the DevOps boom, sysadmins were supposedly doomed by automation pipelines.
  • Even tools like Stack Overflow were feared as “crutches” that would deskill engineers.

But here we are. Still hiring. Still coding. Still solving complex problems.
History shows us a pattern: new tools don’t eliminate developers—they change the shape of what developers do. And AI is shaping up to be the biggest transformation yet.

Business leader holding an AI hologram, representing the future of developers in Dallas and Austin
Tech leaders in Dallas and Austin are evaluating how AI may reshape developer roles—not eliminate them.

What Software Development Managers Are Feeling Right Now

From my conversations with SDMs in the US, Mexico, and Latin America, a few recurring AI-related concerns keep popping up. They’re worth naming:

  • Many managers are already seeing LLMs generate CRUD operations, unit tests, and even frontend code at speed. That’s been the domain of junior engineers. If AI does it faster, what’s left?

  • If developers are just there to prompt, correct, and verify AI-generated code, what happens to craftsmanship, creativity, and code ownership?

  • When AI writes 70% of a pull request, how do you review code? How do you ensure quality? More importantly—how do you retain accountability?

  • There’s a fear that management may see AI as a reason to reduce headcount. “Why hire three engineers when one can prompt Copilot and ship features?”

These are real, strategic concerns—not just philosophical ones. As SDMs, we’re responsible for both delivering value and protecting the long-term health of our teams. AI puts those priorities in tension.

What AI Can—and Can’t—Do in 2025

Let’s talk capabilities.

AI in Software Development: What It Does Well vs. Where It Struggles

Generate boilerplate code (CRUD, API wrappers, HTML layouts)
Accelerates repetitive scaffolding so engineers focus on business logic and integration quality.
Summarize documentation
Condenses long specs/READMEs; great for onboarding and quick impact assessments.
Convert code from one language to another
Helps migrate modules or prototypes across stacks; still requires human review for idioms/perf.
Write tests (with good hints)
Boosts coverage quickly; engineers refine edge cases and contract boundaries.
Offer autocomplete that feels like magic
Context-aware completions reduce keystrokes and mental load during implementation.
Refactor existing code (with clear patterns)
Supports safe, pattern-based refactors; humans validate architecture and boundaries.

In short: AI is brilliant at local optimizations, terrible at global understanding.

Think of it this way: AI is a tireless intern—super productive with guidance, but not ready to lead, innovate, or take the wheel on its own.

The Human Edge in Software Development

Let’s get philosophical for a second.

The heart of good software is not just in writing code—it’s in deciding what code to write, and why. That’s still a deeply human process, built on:

  • Team discussion
  • Customer empathy
  • Cross-functional negotiation
  • Prioritization and iteration
  • Navigating constraints

No model—no matter how large—has the intuition, values, or sense of ownership that human developers bring to a team.
In fact, the more generative tools we introduce, the more valuable roles like tech leads, architects, product engineers, and domain experts become.

Laptop with AI and people icons symbolizing AI-assisted software development collaboration in Texas
Software Development Managers are raising concerns about AI’s impact on junior roles, creativity, and code ownership.

What the Future of Dev Teams Could Look Like

So let’s get real. Will AI shrink development teams?

Probably. But not in the way you think.

We won’t lose developers—we’ll lose certain types of developer work. Here’s how that might look:

Comparison: Today vs Tomorrow with AI-assisted development
Today
Tomorrow
Manual UI implementation Auto-generated layouts with human tweaks
Writing tests by hand AI writes tests, devs refine edge cases
Reading long docs AI summarizes, humans decide relevance
Debugging via trial and error AI suggests fixes, humans validate impact
Sprint planning as checklisting Shift toward outcome-oriented problem solving

In this future, the bar for what it means to be a «productive» developer will rise. Engineers will need better product understanding, system thinking, and communication skills.

And yes—there will be fewer junior-only roles. But there will also be more hybrid, strategic, and creative roles.

How SDMs Can Adapt—and Lead

So, what do you do about all this? Here’s a roadmap for Software Development Managers navigating this shift.

1. Embrace AI as a Tool, Not a Threat

Your devs are already using Copilot. Don’t ban it—standardize it. Share best practices, do paired prompting sessions, encourage responsible experimentation.

2. Train Your Developers to Prompt Like Pros

Prompt engineering is quickly becoming a core skill. Support your team with resources, workshops, and internal documentation on how to get the most out of AI tools.

3. Redefine Code Review

Focus less on syntax, more on logic, clarity, and business alignment. Encourage devs to annotate AI-generated code so it’s reviewable.

4. Shift Your Hiring Strategy

Look for:

  • Developers with product mindset
  • Engineers who can guide AI, not just code
  • Communicators who can explain tradeoffs
  • Generalists who can move up and down the stack

You’ll get more value from adaptive thinkers than from “pure coders.”

5. Educate Leadership

Your executives may see AI as a silver bullet. Help them understand:

  • Where it adds value
  • Where human oversight is critical
  • Why teams need time to evolve, not just “automate”

Being a trusted advisor internally is your new superpower.

Chapter 7: Ethical and Strategic Pitfalls to Watch For

Adopting AI tools blindly comes with risks you can’t afford to ignore.

Hallucinated code

AI sometimes generates plausible-looking but incorrect or insecure code. Don’t trust, verify.

IP leakage

Tools like Copilot might include code patterns from public repositories. Be clear on your org’s compliance standards.

Skill erosion

If juniors rely too heavily on AI, they may never build foundational skills. Introduce “manual coding days” or “promptless challenges” as part of dev growth plans.

Team morale

Some devs may feel threatened by AI adoption. Create psychological safety to express doubts and provide mentorship toward evolving roles.

Business professional holding AI balance icon, symbolizing tradeoffs in future software development teams
The future isn’t about losing developers—it’s about reshaping the kind of work software engineers will do with AI.

So… Will AI Replace Developers?

The short answer: No. But it will replace how we develop software.

The real danger isn’t AI—it’s companies and teams that fail to adapt.

The best teams will treat AI not as a shortcut, but as an amplifier:

  • Of creativity
  • Of speed
  • Of code quality
  • Of collaboration

And the best SDMs will guide their teams through that transition with clarity, empathy, and a vision for what comes next.

Final Thoughts: AI Will Change Us—But It Won’t Replace Us

The age of generative development is here. But it’s not the end of software teams—it’s the beginning of a new kind.

Your job isn’t to resist the future. Your job is to shape it.

By embracing AI thoughtfully, upskilling your team strategically, and focusing on what humans do best—we can build better, faster, and more meaningful software than ever before.

Want to future-proof your team?

At Scio Consulting, we work with companies building resilient, forward-thinking nearshore teams—engineers who thrive in human+AI workflows and understand how to bring value, not just velocity.

Let’s talk about how we can help you stay ahead—without leaving your team behind.

Rod Aburto

Rod Aburto

Nearshore Staffing Expert

If Your Tech Team Can’t Talk to Users, AI Will Take Their Jobs (and You’ll Be an Accomplice)

If Your Tech Team Can’t Talk to Users, AI Will Take Their Jobs (and You’ll Be an Accomplice)

AI, DevOps, Outsourced Engineering Team

By Guillermo Tena
Conceptual illustration of a human and an AI figure facing each other, symbolizing the relationship between technology and humanity, with "AI" at the center.

Why User Conversations Are Your Most Underused Engineering Tool

Not long ago, after one of those painfully failed product validations, I found myself wondering: how many key decisions have I made without truly understanding who I’m trying to help? I’ll admit—it hurt to realize the answer.

As a Founder / Product Owner / Business Developer, I’ve had the privilege of working with brilliant technical minds. People who write code like poetry—masters of distributed systems, CI/CD, pipelines—the whole stack. But when it comes to having a genuine conversation with a user, many freeze up. Not because they don’t care, but because no one ever taught them the art of asking the right questions.

If you’re a CTO or COO leading a software team—especially in growth-stage companies from Austin to Dallas—here’s your wake-up call:

If your engineers can’t talk to users, you’re not just building in the dark. You’re handing the job to AI, one disconnected sprint at a time.

What Happens When Dev Teams Work Without User Signals

Without user context, your team may:

  • Ship features instead of solving real problems.
  • Use deadlines as the only motivator—eroding product purpose.
  • Iterate fast, but in circles.
  • Turn your backlog into a graveyard of half-guessed ideas.
  • Miss out on disruptive innovation. Real innovation comes from human empathy, not just roadmaps.

I once led a team where the technical challenge wasn’t particularly complex. A CTO told me building the KHERO app didn’t feel “intellectually interesting.” Later, I realized my mistake: I hadn’t explained the impact of what we were building. If I had conveyed that his work would help thousands of people feel like heroes and change the lives of hundreds of breast cancer survivors, I’m sure his perspective would’ve shifted.

When your developers fall in love with the problem, not just the tech, you’ve got an unstoppable team—even when the intellectual challenge isn’t the biggest.

The Mom Test: Why It Should Be Required Reading for Tech Leads

Based on The Mom Test by Rob Fitzpatrick, here’s what we train our developers to do:

  • Don’t pitch—listen.

    • Wrong: “Would you use this?”
    Right: “How did you solve this last time?”

  • Ignore compliments.

    • “Sounds good” ≠ commitment. Real signals come from past actions, not vague future promises.

  • Ask about reality, not hypotheticals.

    • “Would you walk to fundraise?” → 100% yes.
    “Do you walk or run? When was the last time?” → 20% follow-through. Reality > good intentions.

We seek validation, but what we really need is truth. And truth doesn’t emerge when you talk—it shows up when you listen.

Using this shift in approach, we fine-tuned our segment and doubled download and usage rates for the KHERO app.

Developer participating in remote customer call to strengthen nearshore team collaboration

Want to Build a Better Team Culture? Start with This Ritual

We implement a simple practice called Coffee with Customers for our engineering teams (in Mexico, Colombia, and with partners in Texas):

  • Prep (15 min): Devs create one hypothesis and write 3 user-safe questions.
  • Live Call (20 min): A real user call—no selling, just learning.
  • Post-Mortem: We analyze what we learned, share it, and use it to shape the backlog.

The result?
Devs stop building because someone said so. They start building because someone needs it.

For CTOs, COOs & Product Leaders: This Is About More Than Research—It’s About Leadership

A tech lead who can’t explain the “why” behind a sprint is managing, not leading.
Great leaders:

  • Create space for devs to hear users.
  • Reward curiosity over code volume.
  • Coach their teams to spot truths hiding in plain sight.

Why This Matters in Nearshore Teams

With distributed teams across LATAM, communication gaps can multiply. But when nearshore engineers—like those we place from Morelia to Medellín—talk to end users in real time, everything changes:

  • Higher alignment
  • Better backlog quality
  • Shorter cycles
  • Stronger culture
  • Lower churn

Person using a laptop and holding a coffee cup while reviewing code and remote collaboration tools—symbolizing the flexibility of modern tech work.

Final Thoughts (and a Gift)

I’ve made all the mistakes—mistaking interest for intent, validating products with my own pitch, skipping user contact. But I’ve learned. And I’m still learning.

If you want a practical, one-page cheat sheet based on The Mom Test—something you can use in your next team meeting—just reach out to me at linkedin.com/in/guillermotp

Remember:
Don’t try to be interesting. Stay interested.

Guillermo Tena

Guillermo Tena

Head of Growth
Founder @ KHERO (clients: Continental, AMEX GBT, etc.) Head of Growth @ SCIO Consultant & Lecturer in Growth and Consumer Behavior

From SEO to AI: How Blog Content Needs to Evolve for Generative Search – and What It Means for Nearshore Partners 

From SEO to AI: How Blog Content Needs to Evolve for Generative Search – and What It Means for Nearshore Partners 

Agile Methodology, AI, Customer Experience

By Rod Aburto — Nearshore Staffing Expert at Scio Consulting
Person interacting with AI-powered search interface on a laptop, symbolizing the shift from traditional SEO to generative search content strategies.

While attending SaaStr 2025 this past May in San Mateo, California, I noticed a subtle but powerful shift in how tech leaders are thinking about content strategy. A recurring theme throughout the sessions and conversations was the rising influence of Generative AI platforms like ChatGPT, Claude, and Perplexity, as the new front door to online discovery.

This trend made me reflect on how we, at Scio Consulting, share our experience and insights through our blog. Traditionally, we’ve followed SEO best practices to ensure our content gets found. But the game has changed.

Now, your audience might not be typing keywords into Google. They’re asking AI tools natural-language questions—and expecting nuanced, trustworthy answers. That shift changes everything.

Person typing on a computer with a digital interface overlay, representing the shift from traditional keyword search to AI-powered question-based discovery.

From “Googling” to “Asking”

In the old model, keywords, backlinks, and structured metadata were enough to give your blog post a fighting chance at visibility. But today, users searching for insights about nearshore software development, remote engineering teams, or Latin America tech talent are using AI platforms that respond with curated, synthesized summaries.

Instead of reading ten blog posts, people ask:

  • “What’s the best nearshore partner for Agile delivery in Mexico?”
  • “How can I build a scalable development team in Latin America?”
  • “Who offers flexible staff augmentation models for software outsourcing?”

If your content isn’t well-structured, specific, and authoritative, it simply won’t be included in the AI’s answer set.

How Generative AI is Changing Content Discovery

At its core, Generative AI rewards content that is:

  • Expert-led, not generic
  • Conversational, not keyword-stuffed
  • Structured, using clear subheadings and semantic flow
  • Helpful, addressing real questions from real users

That’s a big deal for nearshore partners like Scio. We’re not just writing for a search algorithm—we’re writing to be understood and surfaced by AI.

This means our posts on staff augmentation, agile delivery, and software outsourcing need to clearly explain what we do, how we do it, and why it matters—with a level of transparency and authority that resonates with both humans and machines.

How Scio is Adapting

At Scio Consulting, we’re evolving our content strategy to reflect this shift. We’re aligning our blog posts with the way AI platforms index and summarize information, while staying true to our core voice and expertise.

That includes:

  • Highlighting our experience with nearshoring to Mexico/LATAM and service delivery management
  • Showcasing our ability to scale remote engineering teams for long-term impact
  • Sharing real lessons learned from building scalable development teams across borders
  • Addressing questions we know tech leaders are asking AI tools today

Our goal is to meet CTOs and Software Development Managers exactly where they are—whether they’re browsing a blog or chatting with an AI assistant.

Person typing on laptop with AI assistant icons floating above, symbolizing how generative search is changing access to expert content and thought leadership.

The Future of Thought Leadership

If you’re a tech leader navigating software outsourcing or exploring nearshore options in Latin America, know this: The content you find today may not come from traditional search engines. It may come from a well-trained AI that understands your question—and knows where to look.

We believe nearshore providers like Scio have a responsibility to make our knowledge accessible in this new format. Because if you’re trusting AI to guide your decisions, you should be confident that the right voices—voices grounded in experience, transparency, and delivery excellence—are part of the answer.

Let’s talk about how Scio’s nearshore model and flexible team structures can help you move faster, scale smarter, and deliver better. Visit https://sciodev.com or reach out directly—AI may be the new search engine, but real conversations still matter most

Rod Aburto

Rod Aburto

Nearshore Staffing Expert

Why People Don’t Choose You (Yet): The Psychology of Perceived Risk in Uncertain Times

Why People Don’t Choose You (Yet): The Psychology of Perceived Risk in Uncertain Times

AI, Software Development

By Guillermo Tena

Why People Don’t Choose You (Yet): The Psychology of Perceived Risk in Uncertain Times

I’ve seen it happen, again and again.
You build a great product. It solves a real problem. It looks sharp. You’ve done your homework. And still… silence.

No traction. No signups. No movement. Just you, a whiteboard full of ideas, and a growing sense of “what am I missing?”

I’ve been in those moments myself. And I’ve worked alongside teams launching projects under high uncertainty—some with global ambition, some with no clear roadmap to follow.

Over the last four years, I’ve taught Behavioral Economics and Consumer Behavior at Universidad Panamericana. And one of the core truths I’ve shared with every student, every semester, is this:

People do not act on reality. They act on perception.

That sentence tends to land hard because it explains so much of what we get wrong in product, marketing, and growth.
And I’ve lived it in the field.

When we launched Buffon Academy, which now operates in 20+ cities across multiple continents, organizing information clearly was the only way to make the model scalable. If local teams couldn’t understand the promise, process, and positioning—we were done before kickoff.

And when we set out to create Calaverandia, the world’s first Día de Muertos theme park, the real challenge wasn’t logistics or tech. It was perception.

People couldn’t “see” what we were building. They worried it was a haunted house… a cultural misstep… or just a scam.

We had no past references, no physical previews, no price anchors. The only way we earned trust was by carefully crafting how people would interpret what we stood for, long before opening day.

So believe me when I say this: Perception isn’t just a feeling—it’s a process.
It moves through three stages: Selection, Organization, and Interpretation of what our five senses present to us. It’s how our brains decide what’s safe, what’s valuable, and what’s worth our time (or not).

And in uncertain times like these, this process becomes even more defensive, more selective, and more biased by risk.
Let’s break it down.

The Real Risk Isn’t Always the Real Risk

The Real Risk Isn’t Always the Real Risk

There are six types of perceived risk that shape every buying decision,especially in the digital product space:

  • Functional Risk – “Will this even work the way it promises?”
  • Physical Risk – Rare for SaaS, but relevant in sectors like healthtech or cybersecurity. “Could this cause harm?”
  • Financial Risk – “What if I waste my money?” The higher the price, the greater the perceived risk.
  • Psychological Risk – “What if I choose wrong and look stupid?” This hits the ego of your buyer.
  • Social Risk – “What will my team/peers/LinkedIn think if this flops?” Careers can be on the line.
  • Time Risk – “What if we waste weeks onboarding and it sucks?” Time is an expensive currency.

For product teams, these aren’t fluffy consumer fears. These are conversion killers.
Your pricing, UX, onboarding, positioning-all of it either increases or reduces perceived risk.

So, How Do Humans Lower Perceived Risk?

Here’s where things get juicy. When uncertainty rises, people lean on mental shortcuts to protect themselves:
They double down on research. Endless tab-switching, deep dives into 2017 Reddit threads. Result? Paralysis by analysis.

They stay loyal to what they know. Even if it’s not great. It’s familiar. “Nobody got fired for buying IBM.”

They judge you by how you look. AI has leveled the content game. If your brand doesn’t look the part, you’re not even in the running.

They seek proof. Case studies, testimonials, and logo farms matter. It’s not as painful to fail if others have failed with you.

They anchor on price. Often, buyers choose the more expensive option just to feel safer. “If it costs more, it must be better…”

The Psychology Behind the Freeze: CEO Confidence Drops

The Psychology Behind the Freeze: CEO Confidence Drops

This isn’t just theory—it’s playing out in real time. According to the Q1 2025 Vistage CEO Confidence Index, CEO confidence fell sharply to 78.5, down 22.1 points from the previous quarter. Over 42% of middle-market CEOs now expect economic conditions to worsen, a huge spike from 13% just months ago.

Why? Policy shifts, election-year volatility, and tariff uncertainty have created a planning nightmare. And when business leaders feel uncertain, they delay decisions or stick to the familiar—even if it’s suboptimal.

👉 This climate magnifies perceived risk and makes life harder for new players.

Click here to read the full research

So, how do you stand out when no one wants to take risks?

You need to understand one thing deeply: trust transfers. This is where the Halo Effect becomes your ally. If your company is new, unknown, or doesn’t have an extensive track record, that doesn’t mean you’re out of the game. But you do have to borrow credibility from places that feel earned.

Worked at Google? People assume you know what you’re doing.
Have a respected advisor in your niche? Their reputation reflects on you.
Brand design that screams «premium»? That’s a silent signal of trust.

The key is not faking it but intentionally designing how trust gets built. Build strategic halos with people, design, and client stories that feel authentic and deserved. That’s how perception starts working in your favor, and you can increase your performance.

TL;DR: Perception Is Your Funnel

In uncertain times, people don’t buy features or services. They buy LOW RISK.

That’s why if you’re building or selling digital products, you’re not just managing features, you’re managing perception. Especially when selling to experienced C-levels. They’ve seen enough Saas/Product hype to be skeptical by default.
Some high-impact actions you can take today:

  • Celebrate your current clients and wins: everyone wants to be on the winning team.
  • Publish content with C-level credibility, not just SEO fluff.
  • Use pricing anchors strategically to shape perceived value.
  • Design is not decoration, it’s a trust signal. Make every pixel earn its place.
  • Build a trust system, not just a website. (Think of it like a journey: Website → LinkedIn profiles → Blogs → Conversations)

👉 If you treat perception as a process, you’ll be able to design strategies that reduce risk in every stage of the customer journey and get a better performance for your company.

TL;DR: Perception Is Your Funnel

Final Thoughts: I Know How Frustrating This Can Be

If you’re reading this, chances are you’re building something meaningful or something you’re proud of.

But if people aren’t choosing you (yet), it’s probably not because your product lacks value. It’s because the value isn’t being perceived clearly or confidently enough.

And that’s not on you. It’s just how our brains are wired,especially in times of uncertainty.
I’ve worked with enough teams to know that this gap between what we build and what people see can feel exhausting. But here’s the truth:

You don’t need to scream louder. You need to be understood faster.
Perception is your real growth funnel.

When you treat it like a process; something you can design, test, and improve. It stops being this mysterious blocker and starts becoming your quiet advantage.

You don’t need to be perfect. You just need to feel like less of a risk to the right people.
And that’s something you can absolutely build. Be patient

Guillermo Tena

Guillermo Tena

Head of Growth
Founder @ KHERO (clients: Continental, AMEX GBT, etc.) Head of Growth @ SCIO Consultant & Lecturer in Growth and Consumer Behavior
Is AI Replacing Developers?

Is AI Replacing Developers?

AI, Blog, Software Development

Is AI replacing developers?

Artificial Intelligence (AI) is transforming industries across the board, and the tech industry is no exception. The question on many minds is: will AI replace software developers? While the answer is complex, it’s important to understand the potential impact AI can have on productivity and the future of software development. 

The Impact of AI on Productivity in Software Development

AI has the potential to significantly enhance productivity in software development. By automating repetitive tasks, AI allows developers to focus on more complex and creative aspects of their work. Tasks such as code generation, bug fixing, and even some aspects of software design can be streamlined with AI, leading to faster development cycles and higher-quality outputs.

Is AI replacing developers?

How AI Will Transform Software Development

AI will not replace software developers; instead, it will transform their roles. Here are some key areas where AI is making an impact: 

  1. Automated Code Generation: AI tools can generate boilerplate code, reducing the time developers spend on routine coding tasks.

     

  2. Intelligent Debugging: AI can identify and fix bugs more efficiently, improving code quality and reducing the time spent on manual debugging.

     

  3. Enhanced Testing: AI-driven testing tools can run extensive test cases, identify edge cases, and ensure robust software performance.

     

  4. Project Management: AI can assist in project management by predicting timelines, identifying potential bottlenecks, and optimizing resource allocation.

     

  5. Learning and Adaptation: AI systems can learn from past projects, continuously improving their performance and providing developers with valuable insights.

The Evolution of Developer Roles

The automation of manufacturing processes in the past provides a valuable reflection on what we might expect in software development. Just as automation in manufacturing led to the evolution of certain roles, AI in software development will likely evolve the roles of developers. 

Senior developers and specialists are more likely to be affected by AI advancements than junior developers. While junior developers will continue to perform essential tasks, senior developers will need to adapt to new roles that leverage AI tools to enhance productivity and drive innovation.

Addressing Challenges and Ethical Considerations

At Scio, we are aware of the challenges and ethical considerations that come with integrating AI into software development. 

Challenges: 

  • Data Privacy: Ensuring that AI systems handle data responsibly and comply with privacy regulations. 
  • Bias mitigation: in AI algorithms to ensure fair and equitable outcomes. 
  • Job Displacement: Addressing the potential displacement of jobs and providing opportunities for upskilling and reskilling. 

Ethical Considerations: 

  • Transparency: Maintaining transparency in how AI systems make decisions and handle data. 
  • Accountability: Ensuring that there is accountability for AI-driven decisions and their impacts. 
  • Bias and Fairness: Actively working to reduce biases in AI systems and promoting fairness. 

Is AI replacing developers?

Embracing AI as a Tool

At Scio, we are starting to use like ChatGPT as part of our programming and languages toolkit. By leveraging AI for clear prompts, token management, and feedback loops, we enhance our development processes and ensure that our teams remain at the forefront of technological advancements. 
 

AI is not here to replace software developers but to empower them. By embracing AI, developers can enhance their productivity, focus on more meaningful tasks, and drive the future of software development. At Scio, we are committed to navigating the challenges and ethical considerations of AI integration while leveraging its potential to benefit our clients and teams. 

Robotic Process Automation and the future of intelligent machine work

Robotic Process Automation and the future of intelligent machine work

AI, Technologies

Curated by: Sergio A. Martínez

The reason why humans build machines is that they want to make work easier and faster. That always has been true; machines help us accomplish tasks that would otherwise take a long time with just human labor alone, or even be impossible for a human to do in the first place. They also help us save space, energy, and time — after all, resources are precious commodities, so if we can utilize them more efficiently through machines, why wouldn’t we? And more importantly, machines also increase our industrial production rate, more so than what could be achieved without the use of machines. Humans often look to make activities effortless, and advances in technology give us the capability to automate tasks.

Robotic-Process-Automation-icono

And of course, this process of automating tasks and processes is pretty important in every industry imaginable. Let’s look, for example, at software development: A solution already in popular use is Robotic Process Automation (RPA), a way to automate specific tasks within a process, so people don’t have to do them manually. The main advantage of RPA is that it can save time and be more accurate than humans because it’s not necessary to have someone actively monitoring how the task is performed, and ultimately means that businesses can get more done faster and with fewer resources. This allows developers to focus on more complex projects while reducing the time spent performing mundane tasks.

By its very nature, RPA works well with larger applications due to its ability to organize data into streamlined processes, reducing the overall development time and cost, reducing development hours, and making sure everything runs smoothly. Robots make this easy as they don’t need the same amount of troubleshooting, testing, and debugging time as we humans do. In other words, the reason why RPA has become an increasingly popular tool in the software industry is because of its ability to speed up development for faster technology deployment. As stated by IBM:

“[RPA] combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.”

However, with more and more businesses migrating to digital tools and platforms, and software development continues rapidly expanding with no signs of slowing down, the demand for innovative technology solutions also grows. It’s no wonder the development of automatic tools is booming to keep up, helping to optimize tasks during a software project in a way that was unthinkable barely a decade ago. There is no bigger leap forward in automation technology than Artificial Intelligence, which promises to change the field in ways that we maybe cannot grasp yet.

Automatic Intelligence

Empathy Design Disorder 1

The use of AI technology is certainly booming at the industrial scale and with good reason. By deploying these kinds of applications, businesses can automate many mundane, time-consuming tasks that would otherwise require a lot of manual labor, while reducing wasted resources and increasing efficiency in the production process. With AI driving efficiency gains, businesses benefit from reduced labor costs and improved production times, making it a no-brainer as far as implementation is concerned. 

It’s no surprise, then, that use of AI technology is booming. This capability has generated enthusiasm from those who understand its vast capabilities, leading to an explosion of use at an industrial scale. And as AI continues to expand, it may become a fundamental component of modern business operations around the world. However, is the implementation of AI tools and an automation process the same thing? Or do these ideas refer to fundamentally different concepts with distinct goals and desired outcomes?

“AI is not the same as automation. Automation is a machine executing a series of instructions exclusively set by humans. If an action isn’t explicitly described in the instructions, the machine can’t do it. With AI, however, the machine can take broad rules outlined by humans, and determine its own pathways to success”, explains the Artificial Intelligence Institute. “Automation can be used in tandem with AI such as machine learning and deep learning to produce even better results in a process we might call AI automation [which] allows us to reap both the business process benefits of automation — increased speed, efficiency, time-savings, and ability to scale — with the insights, flexibility, and processing power of AI technology.”

That way, AI is revolutionizing the robotic automation process and has opened up virtually infinite possibilities for all sorts of industries, enabling robots to react faster and make more accurately timed decisions without direct human input. AI can even give robots the ability to learn from their mistakes, so they don’t repeat them and cause unnecessary delays in production or other processes. All of these advantages offered by AI give RPA tools a new lease of life, making them even better players in today’s automated world. And this can only get better, right?

The “artificial” in Artificial Intelligence

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It might seem cut-and-dry to think that AI is an overall net positive on automation processes, but companies should approach AI with caution instead of putting too much trust in it, outright replacing manual decision-making processes without due consideration, because there are often large discrepancies between initial expectations and actual outcomes when working with AI. In other words, while these new tools may promise optimal performance, they don’t always live up to expectations, so any organization interested in these kinds of automation tools needs to bear the limitations of AI in mind at all times.

“When companies place too much confidence in AI, they may miss key opportunities to inject creativity or human judgment into decision-making processes which can lead to misguided actions with unintended consequences”, says Adolfo Cruz, PMO Director, and Partner at SCIO. “For example, AI tools are limited when it comes to making decisions; they can only provide insights based on data and algorithms, and do not possess the same level of judgment as a human. Additionally, these tools lack intuition and creativity and may not be able to think outside the box or come up with creative solutions to unique problems”. 

That is to say, AI has come a long way in developing industrial advancements, yet there are still certain tasks that should be left off limits. AI should not be involved in any decision-making processes due to their lack of understanding of the potential implications of their actions. Allowing the robots to take over tasks such as operating complicated machinery and making decisions over them could do more harm than good when it comes to safety measures for both the workers and the products being created. Even with the best technology and programming, mistakes can still be made due to inevitable flaws in their programming. These risks outweigh any saving benefits that AI machines may provide, therefore we must prevent them from causing any further damage by restricting them in what they can do within an industrial context.

In short, automation and AI represent a powerful combination of resources with exciting potential. With no tedious tasks to weigh them down, people can focus their full power on the challenge or problem at hand and work in tandem with AI automation to create dynamic systems that save time, energy, and money. This combination is already being used across industries to great effect — streamlining production processes that were once complex and solving problems more quickly than was ever thought possible. All of this leads us toward an exciting future where these amazing technologies will continue to do even more positive things for both businesses and consumers. All in all, it’s truly amazing how much these two forces are capable of when we use them together.

The Key Takeaways

Robotic Process Automation
  • The point of building machines is to reduce the amount of work a person needs to do to produce something, and in software development, this is no different.
  • It should be clear that AI and Automation tools do not refer to the same concept, exactly, but should be combined to get the most out of them.
  • The main advantage of AI is that it can make its own decision and correct courses, which can be powerful when used with RPA.
  • However, this AI application should be careful and considerate, or any organization runs the risk of over-rely on this technology, which can have unintended consequences.

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