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How AI Will Change Software Delivery in the Next 2 Years

by | Angular

How AI will change software delivery in the next 2 years.

Everybody is excited about AI. The headlines tout how models can write code, generate tests, scaffold applications, and even “automate software development.” The promise is seductive: faster delivery, fewer bugs, more features, happier clients, larger margins. Yet for most organizations, meaningful gains in speed and quality from AI remain frustratingly elusive.

 

Despite pouring resources into AI pilots and tooling, many teams struggle to translate that promise into real results. Tools get adopted, but productivity gains appear marginal. Technical debt still accumulates. Architects still argue. Bugs still slip into production. In practice, teams still wrestle with requirements shifts, architectural complexity, coupling, maintainability, and team knowledge gaps.

The “Workslop” Problem: AI’s Productivity Paradox

A recent phenomenon called “workslop” is shining a light on one of AI’s underappreciated risks — output that looks okay, but fails to move the needle. Workslop refers to AI-generated content that masquerades as good work but lacks depth, context, or substance.

  • TechCrunch warns about coworkers producing AI-generated “slop” that forces recipients to fix or re-interpret the work. [TechCrunch]
  • The Register reports that 40% of U.S. workers say they’ve received workslop in the last month — polished but hollow output that wastes time. [The Register]
  • The Harvard Business Review / BetterUp collaboration coined the formal definition and highlights how that kind of output clogs communication, erodes trust, and erodes real productivity. [Harvard Business Review]
  • One survey estimates workslop costs organizations about US $186 per employee per month in wasted time. [American Bazaar]
  • Some researchers see workslop as one factor behind stats that 95% of organizations report “zero return” on AI investments. [TechCrunch]

Why Software Projects Still Fail (And Why AI Can’t Fix Those Root Causes)

Before imagining that AI will rescue every project, it’s worth revisiting why software projects fail — and which failures are beyond what AI can auto-fix. Common failure modes include:

  • Unclear or shifting requirements
  • Poorly managed scope creep
  • Lack of domain understanding
  • Overly tight coupling and monolithic design
  • Insufficient architecture planning / up-front design
  • Technical debt accumulation
  • Weak testing, quality assurance, or lack of continuous integration / delivery
  • Team skill gaps, poor alignment, communication breakdowns
  • Operational, deployment, reliability, and scaling issues

The Limits of AI: Knowledge, Architecture, and Craftsmanship

AI is powerful, but it is not magic. A few key limits to keep front of mind:

  • AI does not increase domain or architectural knowledge
  • Quality of suggestions depends on training data
  • Security, compliance, concurrency, performance edge-cases [arXiv]
  • Hallucinations, inconsistencies, and context loss
  • Emerging complexity in AI systems themselves [arXiv]

The Risk: Developing Bad Code Faster

One of the more subtle dangers is that AI can help teams produce bad code faster. If your team’s baseline is flawed — coupling, copy-paste, weak abstractions — AI simply amplifies that. Worse: When developers rely on AI suggestions without sufficient scrutiny, you may end up with increased technical debt, hidden coupling, or architectural entanglement that is harder to disentangle later.

What to Do Now: Adopt AI Thoughtfully (Don’t Let It Adopt You)

If you’re reading this hoping to “turn on AI and watch quality soar,” that’s not how this will play out. But you can get meaningful gains — if you follow a structured path. Here’s a recommended adoption approach (drawing from our earlier Briebug playbook):

  • Begin with internal enablement and pilot projects
  • Invest in architectural & AI-roots education
  • Train in your team best practices for architecture and software development
  • Scale a standardized, safe delivery playbook
  • Elevate adoption to increased capability and productivity

Why Experts Matter (And How Briebug Can Accelerate Your Journey)

Many organizations stumble because they treat AI like a “features toggle” rather than a strategic shift. That’s where a partner with domain expertise and proven patterns can accelerate adoption and help you avoid landmines. Here’s what expert partners bring:

  • Real-world playbooks and best practices for integrating AI into enterprise software delivery
  • Guidance on architecture guardrails, modular design, abstraction layers, and trend evolution
  • Help in training your team (skills, mindset, prompt-engineering, code review for AI)
  • Oversight in defining governance, security, and compliance rules around AI use
  • Experience in incremental rollout, pilot design, and scaling AI safely

It’s Time to Act — Don’t Be Late

AI in software delivery isn’t coming — it’s already here. But the winners in the next two years will be those who adopt thoughtfully, pragmatically, and with discipline. The laggards — those who treat AI as a gimmick or hand over code generation without guardrails — risk producing more “workslop”, more debt, and stalled productivity.

If you’re ready to take control and accelerate your software delivery, schedule a Discovery Call with us at Briebug. Let’s discuss your projects, your goals, and how you want your engineering stack and team to evolve over the next two years. Together, we’ll build a roadmap to get you there — faster, safer, and smarter.