Vladislav Zaimov has spent years inside complex, mission‑critical telecommunications environments, where uptime, security, and risk management aren’t theoretical—they’re table stakes. He brings that lens to today’s MSP landscape, where AI, cloud, and cybersecurity converge under real regulatory and economic pressure. In this conversation, he unpacks why 2026 looks strong, how lessons from 2007–2009 still guide decisions, and how AI-assisted support, federated governance, and capital discipline shape day‑to‑day execution. You’ll hear how he reads the signals behind the optimism that over half of MSPs feel, respects the caution that roughly 22 percent hold, and turns projections into concrete partner actions. Along the way, he grounds strategy in lived stories—client transformations, QBRs that move the needle, and controls that keep innovation safe.
2026 looks promising for MSPs despite economic worries. How does this compare to MSP resilience in 2007–2009, and what lessons still apply? Walk me through one client story, key metrics you tracked, and what changed in your playbook afterward.
In 2007–2009, the MSPs that stayed close to cash flow, doubled down on core services, and communicated relentlessly didn’t just survive—they exited stronger. That pattern rhymes with 2026: demand for managed IT, cloud, and cybersecurity persists even when budgets tighten. One client, a regional professional services firm, faced volatile revenue and latency issues across multiple sites. We focused on essential availability and security, tracked incident volume and resolution time, and watched attach rates on cloud and cybersecurity bundles. Post‑engagement, we formalized a “resilience kit”: pre‑approved service tiers, a stricter change window, and quarterly scenario drills. The lesson that stuck is simple—prioritize reliability, transparency, and modular upgrades that clients can pace.
Surveys show over half of MSPs expect growth, while about 22 percent worry about slowdowns. Which signals do you watch to separate noise from risk? Share a recent forecast you adjusted, the steps you took, and the results you saw.
I weigh three signals: pipeline quality in cloud and cybersecurity, renewal intent sentiment, and time‑to‑decision on net‑new AI projects. When sentiment is positive but cycle time stretches, I treat it as caution, not contraction. Recently, we dialed back a growth forecast after seeing slower approvals in one sector, even as over half of our broader pipeline stayed healthy. We tightened qualification, shifted enablement to managed IT and cloud where decisions moved faster, and pushed advisory workshops to unblock AI pilots. The outcome was steadier bookings and fewer stalled deals, which protected margins and delivery capacity.
You’re betting on cloud, managed IT, AI, and cybersecurity. Where are you seeing the fastest returns today? Give a concrete example with revenue mix shift, deal size, and cycle time, and explain what you’d repeat or avoid.
The quickest returns are at the intersection of AI‑assisted operations and cybersecurity‑anchored cloud. A recent program packaged managed IT with AI‑powered ticket deflection and a hardened cloud landing zone. We saw mix shift toward recurring security services and faster movement on standardized bundles. What I’d repeat: lead with security outcomes and show how AI reduces friction. What I’d avoid: bespoke one‑off AI builds that elongate delivery without improving resilience.
Vendors use growth projections in quarterly business reviews. How do you turn that into partner action? Describe one QBR: the data you shared, the commitments you secured, the follow-up cadence, and a measurable outcome.
In a QBR built around 2026 projections, we brought pipeline heat maps, renewal risk flags, and service attach trends for cloud and cybersecurity. We aligned on enablement for sectors where over half of buyers showed readiness and created a contingency plan for segments echoing the 22 percent slow‑down concern. Commitments included co‑marketing for standardized offers, certification sprints, and joint prospecting. Follow‑ups were bi‑weekly for enablement and monthly for pipeline hygiene. The tangible outcome was increased attach of security services and a cleaner forecast that vendors and our team could act on with confidence.
As consolidation continues, how do you weigh build vs. buy vs. partner? Walk me through one M&A or alliance: your criteria, diligence steps, integration plan, and a metric that proved it worked.
I start with client impact: does this expand our cloud and cybersecurity depth without diluting reliability? In one alliance, criteria were complementary skills, cultural fit on security-first delivery, and a common view on AI governance. Diligence focused on incident management practices, knowledge bases, and renewal health. Integration centered on shared runbooks and federated data access, not full-stack upheaval. The success signal was straightforward—higher win rates for managed IT plus cloud bundles and smoother escalations across teams.
Clients may test DIY AI while you deploy tools like ChatGPT. How do you frame the value gap? Share a real support workflow you automated, the before-and-after metrics, and the safeguards you put in place.
I explain that DIY tools can be powerful but brittle without governance, context, and monitoring. We automated password reset and basic network triage with AI‑assisted prompts tied to policy and identity checks. Before, queues swelled during peak hours; after, first‑contact resolution climbed and handle times shrank, which clients felt as faster, calmer support. Safeguards included role‑based access, human-in-the-loop approvals for sensitive actions, and logging tied to compliance reviews.
Karl W. Palachuk talks about AI-assisted tech support. How have you redesigned the help desk around that idea? Give a step-by-step view of triage, routing, escalation, and knowledge updates, along with customer satisfaction and handle-time impacts.
We built a layered flow. Triage starts with an AI assistant that gathers context and checks known issues, then routes by skills and urgency. Escalations trigger structured prompts that surface runbook steps to engineers, with guardrails for changes. Every resolved ticket updates the knowledge base, which the assistant learns from after review. The result: higher customer satisfaction and lower handle time, plus fewer repeat incidents because fixes are documented and discoverable.
With cyber as a top priority, how do you balance rapid AI rollout with security and compliance? Walk me through your control stack, a recent incident or test, the playbook steps you used, and the recovery metrics.
Our stack includes identity-first access, network segmentation, data loss prevention, and monitored AI usage policies. In a tabletop test simulating prompt injection, detection alerted quickly, policies blocked risky calls, and we followed the playbook—contain, verify integrity, and restore least‑privilege access. We measured time to detect and time to restore service, and both stayed within our targets. The experience reinforced that you can innovate quickly if you build with controls from day one.
Analysts mention federated data governance and remote workforce support. How are you implementing both without slowing teams down? Share your architecture choices, policy tiers, onboarding steps, and two KPIs that tell you it’s working.
We use a hub‑and‑spoke model: central policies define guardrails, while domains own data products with clear contracts. Policy tiers distinguish public, internal, and restricted use, mapped to device posture for remote users. Onboarding includes identity proofing, baseline security training, and provisioning into least‑privilege groups. The KPIs: higher policy‑compliant data access without manual exceptions and steady or improved remote employee satisfaction with IT support.
The EU AI Act adds regulatory pressure. How are you staying compliant while keeping speed? Describe your model inventory, risk scoring, approval gates, and one story where the process caught a real issue before launch.
We maintain an inventory by use case and risk category, tie each to training data lineage, and assign risk scores that govern testing depth. Approval gates include security review, bias and robustness checks, and legal sign‑off for higher‑risk cases. In one review, documentation flagged unclear data consent in a training set; we paused, retrained on compliant data, and avoided a launch that would have created exposure. Speed comes from standard templates and pre‑approved patterns.
Marc Hoppers stresses capital efficiency over blitz growth. What trade-offs are you making now? Give an example with unit economics, payback periods, hiring plans, and the checkpoints you use to greenlight spend.
We’re trading customization for scale—fewer bespoke builds, more standardized offerings that protect unit economics. Payback discipline guides hiring: we staff after confirming durable demand and delivery capacity. Checkpoints include cohort performance, renewal signals, and margin by service line. It’s not flashy, but it compounds: steadier cash, happier clients, and fewer surprises.
For 2026 planning, what’s your data-driven roadmap? Lay out your top three bets across AI, cloud, and cybersecurity, the milestones by quarter, the metrics you’ll watch, and a story that shows how you’ll adapt if trends shift.
Three bets stand out: AI‑assisted operations tied to managed IT, secure‑by‑default cloud landing zones, and differentiated cybersecurity services. Quarterly, we’ll expand standardized bundles, harden controls, and deepen partner enablement where surveys show over half of MSPs anticipate growth. We’ll watch attach rates, cycle time, and renewal intent; if a segment drifts toward the 22 percent slowdown pattern, we’ll pivot enablement and repackage offers to meet buyers where they are. I’ve seen this movie before—those who iterate quickly on a resilient core win as conditions change.
Do you have any advice for our readers?
Anchor your 2026 plan in reliability first, then layer innovation where it compounds value. Use partner QBRs to turn optimism into action, with clear commitments and cadence. Treat AI as an operations tool as much as a product, and never ship without controls. Finally, respect the skeptics—their caution often spots the cracks that, once fixed, make growth sustainable.
