Focused AI adoption will significantly improve ROI by 2026
Companies that treat AI like a side project won’t get far. What we’re seeing now is a shift away from experimentation and toward focused, disciplined AI integration. Most businesses saw around a 16% return on their AI investments this year. Not bad, but not game-changing. That’s going to change, fast. By 2026, companies that tighten their AI priorities and concentrate on specific use cases will double that return.
Smart businesses are focusing less on flashy front-end experiments and more on where AI can seriously move the needle, think back-office operations, automation, decision support. That’s where the value is. The winners will be those who stop trying to build everything in-house and instead work with specialized vendors. Why? Because vendor-built tools are already optimized, tested, and scalable. According to Martin Reynolds, Field CTO at Harness, companies that do this outperform in-house models by two times. That’s not a small gap, that’s a competitive delta.
Executive teams should be clear on this: a fragmented AI strategy burns resources. If it’s not solving real problems, it shouldn’t be built. Focus, consolidate, move fast. As use cases mature and vendors refine tools for performance, these platforms will deliver sharp ROI and hard metrics, not hype.
AI-generated code introduces new software supply chain vulnerabilities
There’s a lot of buzz about using AI to write code. It’s fast, scalable, and impressive, on the surface. But look closer. These AI development tools are often trained on outdated or vulnerable public codebases. That means they don’t always recognize security flaws that could already be exploited in the wild. Worse, these AI-generated code suggestions rarely come with traceable origins. You get accurate-looking outputs, just no idea where they came from or whether they’re secure.
Martin Reynolds from Harness called this out directly. As AI-generated code grows, so do the vulnerabilities. In fact, research shows nearly 45% of the code AI systems generate might be flawed or insecure. If you’re letting that into your software pipeline, you’re inviting risk at scale.
C-suite teams need to be awake to this. Secure systems don’t come from guesswork. You’ll see more enterprises embedding automated policy enforcement and continuous monitoring directly into their dev workflows. Tools like Software Composition Analysis will move from nice-to-have to non-negotiable. This isn’t about slowing down. It’s about making sure your speed doesn’t create liabilities.
We’ve seen this movie play out with compromised open-source components before. The stakes are higher now with AI-generated code being deployed into production environments. Automating the audit trail and enforcing runtime checks are key moves for any company scaling AI in development. Make security routine, not reactive.
Unchecked cloud costs threaten to offset AI investment gains
AI runs on compute. And that compute isn’t free. As AI and machine learning workloads scale, cloud spend is moving in the same direction, up. You could be looking at as much as 50% overspend if you don’t get serious about governance. Most companies still rely on periodic reports to manage this, and that’s outdated thinking. Real-time cloud cost monitoring needs to be the norm, not the exception.
Martin Reynolds, Field CTO at Harness, is clear about the risk. Without adaptive controls, AI can become a runaway cost center. The fix is straightforward: use AI to manage AI. Implement real-time anomaly detection, automatically scale resources up or down based on actual demand, and kill wasteful processes before they impact the bottom line. If you don’t have an autonomous optimization strategy in place, you’ll struggle to scale AI profitably.
For leadership, this comes down to judgement and control. You’re investing in AI to improve performance, not inflate infrastructure costs. Whether it’s auto-scaling workloads, rightsizing compute instances, or flagging financial leaks instantly, you need real-time visibility and action loops. That kind of operational discipline will separate efficient companies from inefficient ones. The investments you’re making today in AI only produce returns if they’re backed by automated financial oversight.
Regulatory frameworks are forcing stronger AI governance in development
The regulatory clock is ticking. Governments aren’t standing by while AI outpaces policy. Laws like the EU AI Act and stricter U.S. regional regulations are already reshaping how companies handle AI, particularly code generated by machines. These new frameworks demand transparency, auditability, and higher accountability for algorithmic decisions. Companies need to align now, not later.
Martin Reynolds from Harness explains it well: as AI-generated code becomes mainstream, the risks scale with it. Nearly 45% of AI-generated code may contain vulnerabilities. That’s not a theoretical problem, it’s a real, documented weakness baked into your systems if you’re not paying attention. Regulators see that. And they’re responding with more detailed compliance expectations.
This isn’t optional. CTOs and CIOs need to embed automated policy enforcement and real-time security checks throughout the development lifecycle. Continuous scanning. Stronger audit trails. Built-in compliance, not bolted on. If your system can’t show what it’s doing and why, across environments and releases, you’ll face legal and reputational consequences.
For boards and executive teams, the message is clear: AI delivers value when it’s deployed responsibly. Governance must scale with automation. Leaders who treat compliance as a strategic priority will not only reduce exposure, they’ll build trust in their AI systems among customers, partners, and regulators.
AI is advancing from code generation to full software lifecycle automation
We’re now seeing AI take on more than just writing code. The technology is moving into the full software development lifecycle, testing, deployment optimization, system monitoring, even incident resolution. This shift is significant because it addresses the real barriers that kept AI from delivering end-to-end efficiency. Speed without stability isn’t enough. What matters is automation that can anticipate issues, act on them, and improve over time.
Multi-agent AI pipelines are central to this. These systems aren’t just supporting engineers, they’re operating across multiple stages in development. They’ll scan code, optimize workflows, detect possible failures before launch, and trigger auto-remediation when something breaks. According to Martin Reynolds, Field CTO at Harness, this will be a defining moment for AI in engineering. By 2026, these autonomous capabilities will become baseline expectations in high-performing teams.
For executives, this means your software operations are on the edge of becoming truly self-sustaining. Development pipelines can become faster, more predictable, and less dependent on manual intervention. But to get there, you’ll need to invest in architectures that can support autonomous action safely and reliably. It’s not just about tools, it’s about building systems that learn, improve, and perform without disruption.
This trend also has implications for workforce strategy. As low-level tasks become automated, your team’s skill focus shifts upward. More on architecture, security, data modeling, less on repetitive debugging or deployment. That’s where the talent will drive value. Leaders who act early on this shift, adopting scalable automation that spans across QA, CI/CD, and runtime monitoring, will increase quality, accelerate delivery, and position their teams toward higher-leverage output.
Key takeaways for leaders
- Focused AI investments drive real ROI: Leaders should prioritize targeted, back-office AI applications and proven third-party platforms to double current ROI by 2026 and avoid the inefficiencies of scattered in-house deployments.
- AI-generated code increases security risk: Executives must invest in automated code audits and policy enforcement, as nearly 45% of AI-generated code may be vulnerable and lacks traceability, posing growing compliance and security risks.
- Cloud spend requires real-time control: To prevent AI workloads from inflating infrastructure costs by up to 50%, organizations should implement real-time cloud cost monitoring, anomaly detection, and automated resource optimization.
- Regulation is raising the cost of poor governance: With frameworks like the EU AI Act tightening oversight, companies must adopt continuous security scanning, enforce policy by default, and maintain detailed audit trails to stay compliant and competitive.
- AI is redefining software development automation: Firms should accelerate adoption of multi-agent AI pipelines for testing, deployment, and incident resolution to reduce human error, speed up delivery, and shift teams toward higher-value engineering work.
