Convergence in 2026: 5 takeaways from Kyle Campos’ Forbes piece (and what to do next)

The last decade of cloud was defined by acceleration. Faster releases, faster scaling, faster everything. But speed also compounded complexity, and complexity produced specialization. DevOps for velocity. SecOps for risk. FinOps for cost. ITSM for governance.

Each discipline solved a real problem. But together, they fragmented how organizations operate. Teams built their own tools, data models, and success metrics. Visibility improved, yet coordination lagged. Automation often stopped at the edge of someone else’s responsibility.

In a recent Forbes Tech Council article, CloudBolt CTPO Kyle Campos argues that we’ve hit the limits of that specialization. The next phase of cloud maturity is integration—what he calls convergence: connecting the systems where insights are generated with the systems where action happens.

Here are the most useful takeaways from Kyle’s piece and how to apply them as you plan for 2026.

1) The problem isn’t visibility. It’s execution velocity.

Kyle’s point is straightforward: dashboards can highlight inefficiencies, but they rarely resolve them. By the time a cost issue surfaces, the environment has already changed. The people with the clearest view of the problem often aren’t close enough to fix it.

What to do next: Pick one recurring “we see it, but can’t act on it” issue (e.g., Kubernetes overspend, approval delays, policy drift). Write down who sees it, who can fix it, and what’s currently in the way. That gap is your convergence opportunity.

2) Build, manage, and optimize overlap—so treating them as phases creates churn.

In practice, build decisions determine day-2 management. Operational issues reveal design lessons. Optimization findings should prevent the same inefficiencies from returning. Convergence formalizes that feedback loop so context and action travel together.

What to do next: Identify one workflow that crosses teams (provisioning, access, policy changes, cost allocation). Map the handoffs. If the flow relies on tickets, spreadsheets, or tribal knowledge, you’ve found a place where convergence would reduce friction immediately.

3) Kubernetes makes the gap obvious: scaling is easy; right-sizing isn’t.

Kyle calls out a pattern many teams recognize: node-level scaling has become straightforward, but workload-level optimization still lags. The result is environments that scale faster than teams can “metabolize,” leaving inefficiency behind.

What to do next: If Kubernetes is in scope for you, prioritize getting cost and ownership to the workload level. Optimization gets dramatically easier when teams can connect “what it costs” to “what it is” and “who owns it.”

4) Convergence is being forced by three realities: AI, hybrid complexity, and market realignment.

AI can surface patterns and recommend remediation faster than manual triage, but those outputs don’t help if they end in another report. Meanwhile, workloads are spreading across hybrid and edge environments where handoff-based governance doesn’t scale. And vendor shifts and market consolidation are compressing timelines for governance decisions and increasing lock-in risk.

What to do next: Pressure-test your current model against vendor change. If a major portfolio shift forces you to rewrite your operating model, that’s a sign you need stronger shared context and more adaptable control paths.

5) Convergence doesn’t mean “one team does everything.” It means shared context.

Kyle frames convergence as each team operating from the same context:

• governed blueprints that embed cost/compliance/security early,
• operations patterns that feed learnings back into policy and templates,
• finance signals that appear inside the execution path, not after the fact.

What to do next: Start small: choose one policy that’s currently enforced late (or inconsistently) and move it upstream into templates/blueprints—with an owner and a clear exception path.

Read the full Forbes article: Why Cloud’s Next Chapter Depends on Convergence