1) The biggest change was the conversion of runbooks into separate workflows. Our "SRE copilot" currently keeps an eye on logs and telemetry in a sandbox, correlates issues, opens tickets, makes suggestions for enhancements, and performs standard remediations (cordon-drain-reprovision on unstable GPU nodes, rollback on subpar releases). It's not a chatbot; it's an action taker. 2) The agent's handling of monotonous, repetitive tasks resulted in a 35-40% reduction in mean time to resolve for recurring problem classes and a significant decrease in after-hours pages. Humans moved up-stack to preventative, capacity planning, and chaos drills rather than monitoring alarms. 3) Start out small and limited. Choose three high-volume, low-blast-radius runbooks and give them strict role-based access, canary settings, and a human-in-the-loop for final approval. Ship weekly, keep an eye on the MTTA/MTTR and "false-action rate," and only broaden the agent's scope when the data suggests it is safe to do so. 4) Assign one or two competent platform engineers who have worked with workflow agents or shipping LLM to seasoned SREs or SecOps staff (think Airflow/Argo + LangChain/LLM ops). Internally upskill through agent design and on-call rotations, and look for people with incident response experience rather than just prompt engineers. 5) Hallucinations, unclear decision trails, privilege creep, and vendor lock-in are all real. Immutable audit logs, least privilege by job, tiered execution (plan, dry-run, and enforce), a "big red button" kill switch, and regular red-team simulations against the agent itself are all required. 6) As with any new SRE, provide the agent with a pager, KPIs, and postmortems. If it is unable to defend its actions, it does not act.
How Agentic AI Makes IT Work Easier I'm Aaron Chichioco, IT Specialist at Partner Systems. One thing I see all too often is an IT team that spends too much time on small jobs - checking logs and alerts, and continuing to fix problems that become worse. Agentic AI can perform the repetitive work, while teams deal with bigger fish to fry. How agentic AI changes IT work Agentic AI changes IT work by doing system monitoring, which is what takes up an IT team's valuable time. AIs can monitor logs, server states, and user activity every minute of every day. Once the AI identifies the important issue, it recommends the next steps: rerun the backup or lock the account, so the team can get straight down to fixing issues. Effects on everyday operations Issues are detected earlier, and they get fixed in minutes instead of hours. Low space, failed backup, or strange logins get resolved before having time to cause downtime. No lost time or data, as everyone gets to continue doing what they are doing without incident, ultimately reducing outages and the related impact. Best way to start A good start is to take one task that is relevant, such as backups or user access, and schedule it for AI testing first. Allow it to run on a trial basis and let your team check the outputs, and in the beginning, only allow AI to perform safe, simple tasks. If this goes well for an extended period, then begin adding other tasks gradually, too. How CIOs find the right people CIOs should hire individuals with IT operations experience with scripting or automation proficiencies. They do not need AI research backgrounds. Practical problem-solving is more important than theory. Possible risks Improper fixes to systems can lead to downtime. For instance, turning off the wrong server can cause a major incident. An additional risk is that the system is granted too much access too soon, creating the potential for data loss or security gaps to be exploited. This is also why CIOs should put very strict rules around any use of AI, and have human approval before any changes are made when required. Agentic AI is not intended to run everything on its own. It needs limits and regular checkups so mistakes do not spread. With the right setup, it adds value to managed IT services by lowering risks and maintaining system stability. Aaron Chichioco IT Specialist, Partner Systems https://partnersystems.com/
Hi, I have worked as a product lead for Google Gemini where I worked on developing some of the Agentic AI capabilities. Happy to answer some of these questions. The key way I think Agentic AI is transforming IT operations is by stitching data and logs across multiple apps and helping you take actions on those. So I think that's where the transformative power is coming from. At the end of the game, I think it's more of an efficiency gain and productivity gain that will happen. The best way to implement Agentic AI is to: 1. First come up with a high-level architecture of what they want from that Agentic AI 2. Using some of the Agentic AI frameworks from Land Graph or the evolving frameworks that are getting available 3. Make sure that the Agentic AI that have proper permissions in place have a proper communication and collaboration method in place I think getting the talent for Agentic AI is a critical part. How Agentic AI or LLMs in general have changed things is instead of becoming a deterministic workload, it has become a probabilistic workload. So the biggest change here is the skill gap that the team needs to know is how to run constant evals to understand the accuracy of the systems that they are building, and that's actually one skill that the team has to either develop in-house or hire somebody who has done that work. I know that working in Google, that skill set was easy to gain, but outside of Google, I am seeing fewer and fewer of these folks have the right skill set to be able to do that. I think one of the potential fallbacks of Agentic AI is if it is not implemented properly in a safe and secure manner, there is a huge data leakage issue that can happen. It can have unintended consequences where it can accidentally delete an important database etc. So that's why having constant eval, having better observation/observability of the tools is critical. Happy to chat more. Drop me a note at neha@arambhlabs.com
1. Describe a key way in which agentic AI can transform IT operations In software development organizations, agentic AI can replace certain junior-level roles. By integrating with tools like JIRA, AI can take on small, well-defined, ticketed tasks efficiently. It often delivers faster, more cost-effective, and higher-quality results than a junior developer. 2. How will this transformation impact affected operations? Teams that adopt agentic AI will be able to move faster than those that don't. Team structures will shift, with a higher demand for senior talent to manage the agentic architecture and oversee AI-driven workflows. 3. What's the best way to implement agentic AI? Start small. Select champions within the business to experiment with agentic AI in your environment. Once you understand the right balance of integration and the types of tasks the AI can handle competently, expand usage across teams. 4. How can a CIO locate agentic AI talent? Begin internally. Many team members are already interested in this technology and may be willing to champion its adoption. These internal advocates can lead initial experimentation and guide implementation. 5. What are agentic AI's potential pitfalls? AI does not remove the need for human oversight. Outputs must be reviewed by capable humans who understand the code's implications, especially in legacy systems. All work should continue to go through standard QA and testing processes. 6. Is there anything else you would like to add? Agentic AI is still in its early stages. Organisations should focus on building comfort and confidence gradually rather than attempting to transform everything at once.
Hey John, As someone who has implemented agentic AI for 18 customers in 2025, I would like to offer my perspective on what works. Best ways to implement agentic AI A single large prompt for running complex tasks is slow, expensive, and unreliable. You inevitably arrive at a multi-agent architecture for most enterprise use cases. In a multi-agent system, the output of one agent becomes critical input for multiple downstream agents. So, the architecture needs a supervisor agent that orchestrates the flow of information. And, you need to preserve state for multi-turn dialogues for conversational use cases. Potential pitfalls Beyond the right technology architecture, there are two reasons why agentic AI projects fail. One, companies experience 'death by a 1000 POCs'. Different teams run their own proof of concepts with limited scope and potential for RoI. Selecting the right, high impact use case is still the most important challenge in IT investment. Two, pilots take too long to show value. Gen AI is a fast-moving space. 6-month pilots mean board priorities change long before your pilot sees the light of day. Locating agentic AI talent This is a tough spot for companies that are looking for talent. We have solved this in our company with a comprehensive, 6-month internship program. This year, we hired more than 40 interns to work with our team of AI and ML engineers. Our interns learn the theory and practice of gen AI by working on real-world customer projects. They get to experience what it takes to build, implement, and scale gen AI solutions for diverse industries.
Managing marketing operations for 3,500+ FLATS(r) units taught me how agentic AI transforms property management through automated resident journey orchestration. Instead of manually tracking move-in issues across our Chicago, San Diego, Minneapolis, and Vancouver properties, AI agents now automatically detect patterns in resident feedback through our Livly system and trigger appropriate responses. The change is massive for operational efficiency. When we finded recurring oven complaints from new residents, I had to manually create FAQ videos and coordinate distribution to onsite teams. Now AI agents automatically identify similar patterns, generate maintenance content recommendations, and schedule staff training - eliminating the 2-week lag that previously caused 30% move-in dissatisfaction. Implementation success comes from starting with your highest-volume, most repetitive resident interactions. We began with simple lead qualification through our UTM tracking system before expanding to complex resident experience management. The key insight from managing our $2.9M marketing budget: let AI handle pattern recognition and initial responses, then focus human expertise on relationship building and complex problem-solving. Your biggest pitfall will be over-automating resident communications too quickly. Early AI responses felt robotic and actually decreased our tour-to-lease conversions by 3% before we found the right balance between automation and human touchpoints.
After representing employees in over 1,000 employment cases and serving as Chair of the Labor and Employment Law Section of the Mississippi Bar, I've seen how agentic AI can transform legal document review in employment litigation. Our firm now uses AI agents that automatically scan through thousands of email chains and personnel files to identify discriminatory language patterns that would take paralegals weeks to find manually. The impact on our case preparation has been massive. We used to spend 40+ hours having staff review findy documents for age discrimination cases like those I handle in Madison. Now AI agents flag potential evidence of discriminatory intent, letting us focus our expertise on building stronger legal arguments rather than document hunting. For implementation, start with your most document-heavy processes first. We began with simple keyword scanning for EEOC complaint preparation before adding complex pattern recognition for retaliation cases. The key from handling 20+ employment trials: let AI handle the initial evidence sorting, then apply human legal judgment to the flagged materials. Your biggest risk is over-relying on AI for legal strategy decisions. Early on, we almost missed a crucial constructive dismissal argument because we trusted AI's case categorization too heavily. Now we use AI for evidence findy but keep all legal reasoning and client counseling firmly in human hands - that's where our 20 years of employment law experience actually matters.
As someone who's built federated AI platforms processing genomic data across thousands of institutions globally, I've seen how agentic AI transforms complex data operations that traditional IT can't handle. The key change is **autonomous data orchestration** - AI agents that can independently find, harmonize, and analyze datasets across distributed environments without human intervention at each step. At Lifebit, our federated system processes multi-omic datasets from dozens of countries simultaneously, with AI agents automatically handling data format inconsistencies, privacy compliance checks, and computational resource allocation. What used to require weeks of manual coordination between institutions now happens in hours - our agents negotiate data access permissions, execute analyses, and deliver insights while keeping sensitive health data at its source. The best implementation approach is **workflow-first, not technology-first**. Start by identifying your most repetitive, high-stakes data processes where mistakes are costly. We began with genomic workflow automation using Nextflow because researchers were spending 60% of their time on data prep instead of findy. Build agents that can handle the decision trees your experts use daily. For finding talent, look for people who understand both distributed systems and domain expertise - not just AI theorists. The biggest pitfall is assuming agents can operate without domain knowledge built in. In our space, an agent that doesn't understand genomic data quality standards could approve flawed datasets for clinical decisions.
Built an AI-powered innovation platform that now handles 24/7 competitive intelligence monitoring for enterprise IT teams. Our agents continuously scan for emerging technologies, track competitor moves, and identify potential disruptions before they hit mainstream tech media. One telecom client finded a critical 5G security vulnerability through our automated monitoring three weeks before it became public knowledge. This transforms IT from playing defense to strategic offense. Instead of reacting to market changes, IT leaders get early warnings about everything from new cybersecurity threats to breakthrough technologies their competitors are piloting. We've seen companies save 40+ workdays annually just on market research alone. Start with one specific workflow like vendor evaluation or technology trend tracking. Don't try to automate everything at once. Focus on agents that augment existing team strengths rather than replacing human judgment entirely. The biggest trap is expecting AI agents to understand business context without proper training data. I've watched companies deploy agents that flag every minor patent filing as "critical innovation" because they lack industry-specific knowledge. Your agents are only as smart as the problems you teach them to solve.
Hey, as someone who's run a physical therapy practice for 15+ years and dealt with thousands of patient records and scheduling nightmares, I can tell you where agentic AI would be a game-changer for healthcare IT operations: **predictive patient flow management**. The AI agent could analyze appointment patterns, no-show rates, and treatment progressions to automatically optimize scheduling and predict which patients need extended care plans. At Evolve, we've seen how manual scheduling creates bottlenecks - patients with chronic conditions like Ehlers-Danlos Syndrome need 60-90 minute sessions while post-surgical cases require 30-45 minutes. An agentic AI system could automatically detect these patterns from intake forms and medical histories, then schedule accordingly without human intervention. The change would eliminate the constant phone tag between staff and patients that currently wastes 2-3 hours daily at our Brooklyn locations. We'd see 30-40% better appointment utilization and dramatically fewer scheduling conflicts that force patients to wait weeks for follow-ups. For implementation, start with your patient intake and documentation workflow since that's where the most standardized data lives. The biggest pitfall is trusting AI with clinical decision-making - I've treated terror attack victims where subtle movement patterns indicated underlying trauma that only hands-on assessment could detect.
After 17+ years in IT and founding Sundance Networks, I've seen agentic AI transform network monitoring through predictive maintenance. Instead of waiting for systems to fail, AI agents continuously analyze network traffic patterns and proactively replace components before they crash. This shift from reactive "fix it when broken" to predictive "prevent it from breaking" has eliminated 80% of emergency calls for our manufacturing clients. The implementation sweet spot is starting with your most painful repetitive tasks. We began with automated security patch management across our clients' endpoints - the AI agent now handles routine updates during off-hours and only escalates complex compatibility issues to our techs. This freed up 15 hours weekly that we redirected to strategic projects. For finding talent, skip the unicorn hunt for "agentic AI specialists." Instead, hire solid network engineers who understand automation and train them on AI tools. We've had better luck with technicians who grasp the underlying infrastructure than AI experts who've never troubleshot a failed switch at 3 AM. The biggest trap is over-automation without human oversight. We learned this lesson when an AI agent "optimized" a client's firewall rules and accidentally blocked their payment processing system for two hours. Now we require human approval for any changes affecting critical business functions, regardless of how confident the AI seems.
Running SiteRank for 15+ years, I've seen how agentic AI transforms IT operations through automated content optimization and real-time SEO adjustments. When Google's algorithm updates hit, our AI agents automatically rewrite meta descriptions, adjust keyword densities, and redistribute internal links across thousands of client pages within hours instead of weeks. This shift eliminates the constant firefighting we used to do after algorithm changes. Last March when Google's core update tanked several clients' rankings, our agentic system had already identified the pattern changes and implemented fixes before most agencies even knew what happened. Revenue recovery went from typical 3-4 month cycles to under two weeks. Start implementation with your highest-volume, lowest-risk processes first. At SiteRank, we began with automated A/B testing of title tags across our hosting company clients before expanding to full content strategy. For talent acquisition, look for professionals who've actually deployed automation in production environments - not just those with AI certifications but no real-world scaling experience. The biggest pitfall is assuming AI agents understand business context without explicit training. I learned this when our system optimized a funeral home's content for "competitive pricing" - technically correct for SEO but completely tone-deaf for the industry. Always maintain human oversight for brand-sensitive decisions.
After scaling operations at DocuSign and later helping blue-collar businesses modernize through Scale Lite, I've seen agentic AI transform IT ops through intelligent workflow orchestration. Instead of IT teams manually triaging tickets and escalating issues, AI agents now analyze incoming requests, automatically route them to appropriate specialists, and even resolve common problems without human intervention. At Scale Lite, we implemented this for a janitorial company drowning in service requests. Their single IT person was spending 30+ hours weekly just categorizing and routing basic tech issues from field workers. Our agentic AI system now handles 70% of those tickets automatically--from password resets to equipment troubleshooting--freeing up actual strategic IT work. The implementation sweet spot is starting with your highest-volume, lowest-complexity IT requests. We mapped out every ticket type for three months, then built AI agents to handle the repetitive stuff first. Don't try to automate complex infrastructure decisions immediately. Your biggest pitfall will be over-automation too quickly. I learned this lesson at Tray.io working with enterprise clients--AI agents need clear boundaries and human oversight loops. Build in easy override mechanisms so your IT team can step in when the AI gets it wrong, which builds trust for broader adoption later.
Through my experience running AirWorks Solutions, I've seen how agentic AI can revolutionize predictive maintenance scheduling in IT operations. Our HVAC systems generate massive amounts of sensor data daily, and AI agents can autonomously analyze patterns to predict failures before they happen - just like how we now anticipate which units need service based on temperature fluctuations and energy consumption spikes. The change eliminates reactive crisis management entirely. When we partnered with ServiceTitan's Power the Nation initiative, their AI-driven scheduling automatically optimized our technician routes and predicted equipment needs three weeks out. Our emergency call volume dropped 40% because systems were serviced before breaking down, and customer satisfaction scores jumped from 4.2 to 4.8 stars. For implementation, start with your most data-rich, repetitive processes where mistakes are costly but not catastrophic. We began by letting AI handle appointment scheduling optimization before expanding to inventory predictions. Look for talent who've actually managed automated systems in operational environments - someone who's debugged a failed automation at 3 AM understands the stakes better than someone with just theoretical knowledge. The biggest pitfall is assuming AI understands your specific industry context without explicit training. Early on, our system scheduled maintenance during a client's peak business hours because it optimized for technician efficiency rather than customer convenience, costing us a major account.
As someone who's built a national lighting infrastructure company from the ground up, I've seen how agentic AI can revolutionize predictive maintenance in critical infrastructure. At Vizona, we manage hundreds of high mast poles and LED systems across projects like Snowy Hydro 2.0 and Sydney Metro - traditional monitoring required manual inspections and reactive repairs that cost 3x more than preventive action. The change eliminates costly emergency callouts and unplanned downtime. Our LED systems now feed performance data that AI agents analyze to predict failures 2-3 months before they happen, automatically ordering replacement components and scheduling maintenance crews. What used to require quarterly site visits across remote locations now happens through intelligent monitoring that catches issues before communities lose lighting. Start implementation with your most critical, data-generating assets first. We began with our solar lighting installations because they already had sensors and connectivity - expanding to traditional pole-mounted systems came later. Look for talent in industrial IoT and manufacturing automation rather than pure AI backgrounds - these people understand how physical infrastructure actually operates under Australian conditions. The biggest pitfall is trusting AI decisions without understanding failure modes in harsh environments. I learned this during early solar deployments where algorithms optimized for efficiency but ignored weather patterns specific to remote Australian sites, leading to several systems going dark during critical periods when communities needed lighting most.
As an owner of a Managed IT Services provider, I believe agentic AI represents one of the most significant shifts in IT operations we've seen in decades. The key transformation lies in creating autonomous systems that don't just execute commands but actually make decisions and take actions independently to achieve specific goals. In my experience, this fundamentally changes how IT teams operate. Instead of firefighting and managing tickets all day, teams can focus on strategic innovation while AI agents handle routine maintenance, monitoring, and even complex problem-solving. Internally we're starting to create AI agent workforces to manage things like patching, reading through logs and lots of other low level, labor intensive but important work that frees up my techs for other work. For implementation, I always recommend starting small with a well-defined pilot project. Choose a specific pain point like automated patch management or predictive maintenance and let the AI prove its value there first. You need robust data infrastructure and clear success metrics before scaling. Finding talent is challenging, but I've had success going on upwork.com and hiring freelancers for specific tasks. Look for people who understand both traditional IT operations and automation. The best candidates often come from DevOps backgrounds. The pitfalls I worry about most are over-automation without proper oversight and the risk of AI making decisions based on biased or incomplete data. That's why it's important to always have a human at the loop and in the chain, before the AI work goes to the next phase a human actually needs to review everything before "passing go". What I'd add is that successful agentic AI adoption requires a cultural transformation and mindset change, not just technical implementation.
Having built Google News-approved outlets and worked with AI detection tools at One Click Human, I've witnessed agentic AI revolutionize content pipeline management. The key change is autonomous content quality assurance - AI agents now monitor published content 24/7, automatically flagging potential plagiarism issues and updating articles when detection algorithms evolve. This completely changes editorial workflows. Instead of our team manually checking every piece against Turnitin or reviewing AI detection reports, agents handle the heavy lifting and only escalate genuine concerns. We've cut content review time by 60% while maintaining higher quality standards across all our publications. Implementation works best when you start with your content validation processes. At One Click Human, we began by having agents automatically cross-reference our tool reviews with pricing updates from platforms like Jasper AI - preventing outdated information from hurting our credibility. For hiring, focus on professionals who understand both AI capabilities and editorial judgment, not just technical AI skills. The biggest trap is letting agents make editorial decisions without understanding brand voice. I learned this when our system flagged perfectly legitimate paraphrasing examples as potential plagiarism simply because they shared common phrasing patterns. Human editorial oversight remains critical for contextual decisions that affect reader trust.
Clinical Psychologist & Director at Know Your Mind Consulting
Answered 6 months ago
As a Clinical Psychologist who's spent 15 years working with parents facing severe mental health challenges, I've seen how agentic AI could revolutionize employee wellness monitoring in IT operations. The system could analyze patterns in email response times, meeting participation, and system access logs to identify employees showing early signs of burnout or mental health decline before they reach crisis point. This change would shift IT from reactive "someone's having a breakdown" responses to proactive mental health support. When I worked with Bloomsbury PLC training their line managers, we found that early intervention reduced staff turnover by identifying stress patterns weeks before employees considered leaving. Agentic AI could automate this pattern recognition across entire organizations. Implementation should start with anonymous behavioral data analysis rather than personal communications. CIOs need to partner with occupational health professionals who understand both workplace psychology and data privacy regulations - not just hire more developers. The major pitfall is treating mental health like a technical problem to be "solved." I've worked with parents experiencing severe pregnancy sickness who appeared highly productive right before complete breakdown. AI might miss the human context that a trained professional would catch, potentially flagging the wrong people while missing those who mask their struggles effectively.
1. Agentic AI introduces decision-capable systems that can autonomously perform multi-step tasks without constant human prompts. At J&Y Law, we have explored how agentic AI can triage client questions, detect documentation gaps, and flag urgent deadlines. This creates a more continuous workflow and reduces downtime between client needs and staff response. 2. The agentic AI transformation streamlines operations, reduces manual bottlenecks, and allows staff to focus where human judgment is irreplaceable. IT operations can expect faster turnaround times, greater consistency, and fewer redundancies. Importantly, these changes cascade across departments, enabling greater organizational agility and stronger service delivery. 3. Successful implementation requires intentional integration into the existing tech stack, such as case management software, communications tools, and analytics dashboards. Just as critical is training: staff should clearly understand how AI supports them, rather than feeling they must adapt to it. Here is where a process consultant is invaluable. They can help weave AI into workflows holistically, ensuring it fits across systems, services, and organizational culture. Without that strategic oversight, deployments risk being piecemeal and less effective. 4. CIOs should broaden their search beyond traditional IT roles. The best candidates often have hybrid profiles, combining technical know-how with expertise in AI ethics, systems thinking, and regulatory compliance in their sector. Partnerships with specialized consultancies, professional networks, and emerging AI communities can also surface valuable talent. 5. Rolling out AI too quickly or without guardrails is the biggest risk. If systems are not auditable, aligned with organizational values, and overseen by both technical and business leaders, they can create compliance risks or erode trust. Another pitfall is failing to think holistically, deploying isolated tools without considering the broader ecosystem, which often leads to fragmentation rather than transformation. 6. Agentic AI is a shift in how organizations operate. To realize its full potential, firms should approach it as an ongoing practice rather than a one-time project. With strategic guidance, holistic planning, and regular recalibration, agentic AI can become a durable advantage instead of a passing experiment.
Having scaled businesses through four distinct phases--from Fortune 1000 IT leadership to founding and exiting PacketBase--I've seen how agentic AI transforms operations through intelligent campaign orchestration and lead qualification. At Riverbase, our Managed-AI systems automatically adjust bidding strategies across Google, Meta, LinkedIn, and TikTok based on real-time conversion data, eliminating the manual optimization cycles that traditionally consume 60-80% of campaign management time. This change shifts marketing operations from reactive campaign adjustments to proactive revenue optimization. When we deployed intent-based targeting for a SaaS client, the AI identified prospect behavior patterns that predicted purchase intent 3x more accurately than traditional demographic targeting, automatically reallocating budget to high-converting audiences within hours instead of weeks. For implementation, start with your highest-volume, data-rich processes where pattern recognition drives decisions. We began with lead scoring automation before expanding to full multichannel campaign management. CIOs should look for professionals who combine technical AI experience with domain expertise--I specifically recruit marketers who understand both machine learning principles and conversion psychology. The biggest pitfall is treating agentic AI as a "set it and forget it" solution. During our early implementations, we finded AI systems can amplify poor initial strategies faster than humans can course-correct. Always maintain human oversight on strategic direction while letting AI handle tactical execution and optimization.