• Demand for custom software is showing clear signs of revival after two years of sluggish growth.
  • Entrepreneurs adopting AI are the primary force driving new projects and spending.
  • Companies delaying investment risk falling behind as competitors build AI‑integrated systems.
  • Nearshore and specialized development teams are increasingly seen as partners for rapid AI rollout.

Why demand for custom software is climbing again

After roughly two years of muted growth, the market for custom software development is picking up, driven largely by entrepreneurs and businesses racing to adopt artificial intelligence. Off‑the‑shelf tools and one‑size‑fits‑all platforms are useful, but many organizations now need bespoke systems to integrate AI models, work with proprietary data, and create product differentiation.

This renewed interest isn’t just about experimenting with AI features. It reflects a shift toward production‑grade deployments: automation of internal workflows, customer‑facing AI products, and custom integrations that stitch AI into existing systems. Entrepreneurs pushing these projects are acting quickly to capture advantage — and that urgency is translating into new spending on tailored development work.

What’s changing for buyers and vendors

For buyers (startups and established companies alike), priorities have shifted. Projects that enable data access, model integration, and secure APIs are rising to the top of roadmaps. Companies looking to turn prototypes into reliable services are investing in engineering, observability, and governance.

For development vendors, the opportunity is twofold: build new AI‑enabled applications and retrofit legacy systems to support AI workflows. Providers that combine software engineering skills with experience in ML integration, data pipelines, and security are attracting more briefs. Nearshore providers — with timezone alignment and tailored delivery models — are often mentioned as practical partners for rapid, cost‑effective development.

Risks and challenges to watch

Adopting AI quickly carries risks. Teams that rush to integrate models without clear data governance, testing, or monitoring can create reliability and compliance gaps. Security, model bias, performance degradation, and integration complexity are common sticking points that can inflate budgets and timelines.

Another challenge is talent: engineers who understand both software architecture and AI integration remain in high demand. Organizations that cannot staff these skills internally must choose vendors carefully, prioritizing proven experience over low cost.

What companies should do now

  • Audit current systems to identify where AI can deliver measurable value (automation, personalization, decision support).
  • Prioritize projects that have clear metrics and manageable scope for early wins.
  • Choose partners with demonstrated experience in AI integration, data pipelines, and production reliability — including nearshore teams if you need faster onboarding and closer collaboration.
  • Build governance and monitoring into projects from day one to reduce downstream risk.

Why it matters

The current uptick in custom software spending tied to AI adoption signals a broader move from experimentation to deployment. Businesses that act now and combine strategic planning with the right partners stand to gain a competitive edge. Those that wait risk being outpaced as competitors roll out more capable, data‑driven products and processes.

Image Referance: https://nearshoreamericas.com/ai-adoption-triggers-new-wave-of-custom-software-spending/