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May 22, 2026: The Roles Aren't Going Away. The Work Inside Them Is.

  • Writer: James Sale
    James Sale
  • May 22
  • 4 min read

Three functions are getting their job descriptions rewritten right now: security operations, sales development, and financial close. Not by strategy decks or reorgs, but by AI agents taking over the parts of the work that burned the most hours and delivered the least judgment.


That shift creates a real question for anyone managing teams in these areas. You're not deciding whether to adopt AI. You're deciding whether your team is positioned to do the higher-value work AI can't do, or whether they're still structured around the work AI already can.


Security Teams Are Drowning in Alerts. AI Is Starting to Pull Them Out.

Security operations centers, typically called SOCs, are responsible for monitoring an organization's systems for threats, investigating alerts, and responding to incidents. The job has always been volume-intensive. The problem in recent years is that alert volume has outpaced analyst capacity by a wide margin, and the talent market for experienced security professionals hasn't kept up.


AI-based SOC agents, per recent analysis from Simbian AI, are being deployed specifically to close that gap: faster alert triage, automated investigation steps, and preliminary threat classification that previously required a human analyst to start the process. Splunk's framing of the same shift is instructive, generative AI (AI that can produce analysis and recommendations in natural language, not just flag anomalies) handles the repetitive, high-volume work, while human analysts focus on the calls that require actual judgment: contextualizing a threat, deciding on a response, or escalating something ambiguous.


The operational promise is real. The caution is equally real. Deploying AI in a security context without clean data pipelines, tested integration with your existing tooling, and defined escalation protocols means you may reduce analyst fatigue in one place while introducing new blind spots in another. Implementation quality here isn't a nice-to-have. It's the whole story.


> Worth doing now: If you lead a security function, map which investigation steps currently consume the most analyst hours without requiring a judgment call. Those are the first candidates for AI-assisted handling, and the clearest place to build a business case.


The SDR Role Isn't Dead. It's Being Rebuilt Around What AI Can't Do.

The question of whether AI will replace sales development representatives has been circulating long enough that it's starting to produce actual data rather than speculation. The short answer, per recent analysis from monday.com: no, not wholesale, and not by 2026. The more useful answer is that AI is changing what an SDR's time is worth.


AI SDR agents handle high-volume outreach, lead scoring, initial qualification sequences, and follow-up cadences. Landbase reports, in the company's own analysis, that AI SDR agents are delivering up to 70% higher conversion rates in early deployment contexts, though results at that scale should be read as vendor-reported and will vary significantly based on data quality, targeting, and how well the AI has been calibrated for a specific market.


What that leaves for human SDRs is the part that was always the hardest to systematize: reading a prospect's hesitation, adjusting a pitch in real time, building enough trust to move a cold contact toward a genuine conversation. Hybrid teams are the emerging model, where AI handles the pipeline mechanics and human SDRs handle the moments that require presence and adaptability.


If you manage a sales development team and you haven't yet mapped which parts of the current workflow are genuinely human-dependent versus which are just habit, that's the conversation worth having now.


Finance Teams Are Finally Getting Relief on Month-End Close

The financial close process, which covers everything from reconciling accounts to consolidating reports at the end of a period, has historically been one of the most deadline-compressed, error-prone stretches in any finance team's calendar. Per recent analysis from Rely Services, AI is being applied to the record-to-report cycle to reduce manual reconciliation work, flag discrepancies earlier, and accelerate the time from period-end to finalized reporting.


The practical value isn't speed for its own sake. It's that finance teams spending less time on mechanical reconciliation work have more time to actually analyze the numbers and advise the business. That shift in where finance spends its attention has strategic implications beyond just closing the books faster.


The honest operational caveat: AI-assisted close processes require clean, well-structured underlying data. Organizations with fragmented ERP systems or inconsistent data entry practices will find that AI surfaces problems they didn't know they had, before it can help them move faster. That's useful information, but it comes with a remediation cost that should be factored in before any deployment timeline is set.


The pattern across all three of these functions is the same. AI is absorbing the high-volume, lower-judgment work that historically defined entry-level and mid-level roles. What remains for the humans in those roles is harder to automate and more valuable to the business. The organizations getting ahead of this aren't waiting for the technology to force the redesign. They're making deliberate choices about what their teams should be doing once the AI handles what it can.


That's a leadership decision, not an IT one.


If you want to stay current on how AI is changing specific functions, and what it means for the teams and leaders navigating those changes in real time, that's exactly what Agenticism covers. Join at Agenticism for practical, grounded insights written for professionals making real decisions.


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