Key Takeaways:
- AI integration in custom software development speeds up processes but introduces new complexities that require careful management and oversight.
- Agencies must focus on strategy and human expertise, using AI as a tool to solve the right problems, not just to speed up development.
- Effective AI adoption requires continuous training, experimentation, and critical evaluation to avoid costly mistakes and ensure long-term success.
Nearly half of technology leaders in PwC’s October 2024 Pulse Survey reported that AI is now “fully integrated” into their companies’ core business strategy.
That integration is reshaping entire industries, and custom software development is right in the middle of the disruption.
For software development agencies, this is a turning point. The rise of AI is changing how software is designed, built, and maintained.
Speed is up. Costs are down. But the risks are growing, too.
The question isn't just how software is made — it's who still needs to make it.
In our interview, Scott Jackson, CEO and founder of Essential Designs, shares how AI is transforming the development process from the inside out, and what agencies like his are doing to adapt, stay relevant, and keep delivering real value.
Who is Scott Jackson?
Scott Jackson has been in the software industry for nearly 25 years and has led Essential Designs for the past 17. As CEO and founder, he oversees software consulting and development for a wide range of clients — from private businesses to public sector and government platforms. Under his leadership, Essential Designs delivers custom software solutions tailored to each organization’s unique challenges and team workflows.
Scott doesn’t shy away from the reality that AI has changed the game at a blistering pace.
“Development has gotten faster, cheaper, and way more unpredictable,” he says.
“Half the work is now arguing with AI to stop adding too many things in. The rest is using it to ship things in half the time.”
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That speed is a breakthrough — but it’s not without trade-offs.
What used to take weeks now takes hours, yet that acceleration brings new complexities: hallucinations, unexpected bugs, and edge cases that can spiral into major issues if left unchecked.
“It’s like hiring 1,000 interns who don’t listen very well,” Scott says.
As AI tools evolve, so do the platforms that rely on them. Developers are no longer the sole gatekeepers of custom software.
Business users now have access to powerful low-code and no-code platforms, which allow users with minimal coding knowledge to build applications using visual interfaces.
These platforms are increasingly powered by AI, enabling non-developers to create and deploy software faster, though the trade-off can be a lack of customization and scalability.
Where agencies once built everything from scratch, clients today often show up with tools already in place.
This doesn’t mean agencies are obsolete — but it does mean their role is evolving.
What Agencies Must Get Right
Scott says the real value lies in guidance, strategy, and making sure the tools are solving the right problem in the first place.
“AI used to mean ‘hobby project,’ but now it’s serious. Real companies are betting real money on it,” he explains.
“But it still needs smart people behind it or it just builds bad software… faster.”
That’s where agencies step in — not to out-code AI, but to ask the harder questions AI can’t:
- What does success actually look like for this product?
- Is automation the answer, or is it just the shiny object?
- How will this tool scale, integrate, or be maintained over time?
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Without that layer of strategic thinking, AI can quickly become a liability, generating bloated features, mismatched UX, or solving the wrong problem entirely.
The biggest mistake Scott sees businesses make? Letting AI run unchecked — or worse, deploying it without fully understanding what they’re trying to solve.
“Train your people first,” he says. “Give them room to experiment (and screw up). Don’t expect overnight miracles. Buy coffee.”
In Scott’s view, the best agencies are translators between business goals and technical execution.
For agencies, this means leaning even harder into their strengths: structured thinking, problem-solving, and hard-won experience.
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That might look like helping clients frame the right problem before choosing a solution, pressure-testing AI-generated outputs, or building guardrails around tools so they don’t cause more harm than good.
It’s about being the human layer of judgment — the one that can say, “Yes, this works,” or “No, this will break something important.”
And this is where the real differentiation happens.
“Design the problem first, not the tool. If you start with ‘let’s add AI,’ you’re already lost,” Scott claims.
AI Still Needs a Human Hand
One of the strongest examples of AI at work came from a recent insurance project at Essential Designs. The client needed to process over 100,000 forms — fast.
The agency built an AI-powered QA system that could scan, flag, and triage errors in real-time.
It’s a clear win, but not a plug-and-play solution. AI still needs to be monitored, managed, and evaluated regularly.
“Key performance metrics for AI include speed, accuracy, and cost savings,” Scott notes.
That’s where agencies still prove indispensable — as a safeguard against misuse and misalignment.
They help clients ask the right questions before deploying AI, build checks into the system to catch errors early, and make sure outputs align with actual business goals.
Agencies help prevent problems by setting clear goals, designing smarter systems, and applying experienced oversight from the start.
“Tools don’t replace thinking,” he says. “You can hand someone a hammer, but it doesn’t mean they can build a house that won’t fall down.”
How Smart Agencies Are Adapting to the AI Shift
AI is changing the way software is built, and agencies need to move from just executing to enabling smarter solutions.
To stay valuable, they must focus on what AI can’t do alone: strategy, human insight, and hands-on experience. The best way to accomplish this is to implement the following:
- Get AI-Literate Fast: Teams don’t need to be experts, but they must understand tools like LLMs and no-code platforms — and where they fall short. Stay current by experimenting and building fluency across roles.
- Lead with Strategy, Not Code: Clients need help defining what to build and why, not just execution. Agencies that act as strategic partners will gain trust and long-term work.
- Adopt Human-in-the-Loop Models: AI needs oversight. Agencies should integrate review processes, testing, and risk mitigation into AI-powered solutions.
- Invest in Continuous Learning: Relevance depends on learning today. Agencies should create space for teams to experiment, fail safely, and share insights, turning AI into a competitive advantage.
- Focus on Outcomes, Not Outputs: The future of software is about measurable results. Agencies should frame their value around business goals: time saved, quality improved, and customers served.
