The Future of Software Development with AI: Beyond Writing Code

AI can create a software prototype in just a few hours, but is it able to make that product secure, scalable, and prepared for 100,000 users?

In this episode of the AI InterConnect podcast with the host Laurent Cochet, Alex Lozitskyi, CEO of CHE IT Group, shares his thoughts on where software development is headed with AI.

He talks about how AI is shifting what clients expect, why companies still rely on software development firms, and what happens when AI-generated code faces real-world problems.

how AI is changing the rules of software development

According to Alex, more clients now approach development companies with part of their work already completed. Using tools such as Lovable, business owners can build prototypes, generate front-end interfaces, and test product ideas without hiring developers from the start.

However, the growing accessibility of AI doesn't necessarily reduce demand for development services.

In fact, Alex has noticed an increase in project-based requests. As businesses gain more tools to experiment with, they also discover more opportunities worth validating. Many eventually need experienced technical partners to connect the pieces and improve the underlying systems.

if AI can code, why hire developers?

If AI can generate code, build interfaces, and create functional applications, why should businesses continue working with software development companies?

Alex addressed this question directly:

"Why should I have Alex? Why should I have Che IT and many other service providers?"

His answer comes down to three areas where technical expertise makes a significant difference: security, scalable architecture, and code quality.

security and data protection

For businesses handling customer data, financial information, or confidential operations, security needs to be considered from the beginning.

Incorrectly configured access permissions could expose personal information. Users might gain access to data that belongs to other customers. Sensitive business processes could become visible to people who should never see them.

Experienced developers know how different parts of an application work together, where security risks can arise, and how to keep sensitive data safe.

scalable software architecture

Alex highlights a common challenge with AI-generated applications: they can work perfectly well during initial testing but struggle when usage increases.

"Building a prototype is one thing. Scaling it from 1,000 to 100,000 users is a completely different challenge."

As a product develops, businesses need to consider database structure, integrations, server capacity, performance, and how different components communicate with each other. An experienced software architect can identify potential issues early and design a system that supports your product's expected growth.

code quality and maintenance

Someone needs to understand what has been created, review its quality, identify potential problems, and make sure future development doesn't become unnecessarily complicated.

This is where experienced technical partners continue to provide value, even as AI makes writing code more accessible.

from AI Prototype to production-ready software

Consider a startup that builds an application using AI tools and successfully tests it with 1,000 users. That's a promising start. However, the next stage could involve 10,000 or even 100,000 users, more integrations, additional features, and increasingly complex data flows. At that point, the original architecture may no longer be sufficient.

The development team needs to evaluate the application from several perspectives:

• Architecture: Can the system handle more users, data, and integrations without performance problems?
• Security: Are customer information, permissions, and sensitive operations properly protected?
• Code quality: Is the generated code consistent, understandable, and maintainable?‍
• Quality assurance: Have key features and possible issues been tested?‍
• Tech debt: Will the current code make future updates harder or more expensive?

Alex believes these requirements will also change the role of developers.

"Developers will increasingly become architects, overseeing AI agents, ensuring code quality, and making sure the architecture is properly designed."

how CEOs can measure ROI from AI automation

Alex identifies two primary ways businesses can generate ROI from AI automation:

"Those two ways that you can get the most ROI, decreasing the cost or increasing the profit."

You can automate repetitive tasks, reduce manual data entry, simplify internal workflows, and free employees from routine work. This allows your team to spend more time on activities that require human judgment and expertise.

AI helps businesses handle more leads, respond to potential customers faster, analyze more data, and improve sales processes. For example, automating lead checks and follow-ups allows your sales team to manage more opportunities.

However, Alex warns against treating AI as a standalone solution.

"If you have a mess in your processes, non-AI or automation can help you out with that."

Introducing AI into poorly structured workflows doesn't automatically make them better. In some cases, it simply creates another layer of complexity that employees need to manage.

why people still matter in the age of AI

Alex believes the most valuable asset in business remains something AI cannot fully reproduce: genuine human connection. He explains this through a simple sales philosophy:

"A good salesperson sells features. A great salesperson sells outcomes. An outstanding salesperson sells emotion."

Features explain what a product or service does, outcomes show customers what they can achieve with it, but emotion is what makes a business relationship memorable and builds trust.

For Alex, this starts with treating every business conversation as a conversation between people, and not simply another sales opportunity.

what's next for software development and AI?

Alex admits that planning eight or twelve months ahead can feel unrealistic when a new AI release can fundamentally change the capabilities of an existing product within days.

Clients are becoming more technically capable themselves: they can build prototypes, test ideas, and handle some development tasks independently.

What they increasingly need from partners is a deeper understanding of technology and the ability to turn business ideas into reliable products.

where should CEOs start with AI implementation?

Before spending money on implementation, CEOs should work with their teams to find what slows the business down and where improvements would make the biggest difference.

A practical starting point involves four steps:

• Find your business problems. Look at the activities that consume the most employee time, create delays, generate errors, or limit the company's ability to serve more clients.
• Structure your existing processes. Document how workflows operate, who is responsible for each step, what information is required, and where tasks are transferred between people or systems.
• Set up measurable business goals. Is the goal to reduce operational costs, increase lead conversion, shorten response times, or improve employee productivity?
• Bring in tech expertise. Once the business problem is clear, involve experienced developers or AI specialists to review your current systems and recommend the right solution.