The last few years have seen a proliferation of 'X'Ops roles in tech (StratOps, BizOps, SalesOps, RevOps, ProdOps, DesignOps, DevOps, etc.) with the goal of providing leverage for people in 'X' roles. Many of these leverage tasks end up being grindy work, such as planning and coordination, issue management, driving efficiency, and managing communications and notes. Given the repeatable nature of these tasks, I wonder how many of these roles will be replaced by GenAI Ops Copilots? #genAI #Ops #opsroles #stratops #bizops #prodops
Rebecca Cole
8mo
100% agree - Ops roles make the perfect use-case for GenAI. In fact, roles that are data or administrative in nature will be prioritized first. In my opinion, Ops teams have grown out of necessity to solve for company growth and scale. But this is also causing a lot of inefficiencies and duplication of efforts across these multiple Ops teams. For example, are SalesOps, MarketingOps and ProductOps - really solving for different customer data challenges? Do we need small armies of people to maintain data and reporting in different environments? The answer is no. These teams are solving for the same challenge, but doing it in silos. My prediction is that company’s will start to lean out and consolidate their Ops teams here very shortly in preparation for this transformation. And, as Ops leaders, it’s our job to help up level, reskill our teams now, so we don’t get left in the dust. My goal is to learn how to collaborate with GenAI and determine what tasks can be replaced vs. augmented. But either way, I’m excited to be on this side of change and be a champion for my team.
Adrian Albus
8mo
I've launched Bizops at 3 different tech companies... has so much potential to increase cross functional alignment & decision making velocity... but too often its disintermediated by Dept Heads or other factions who want power to remain within Depts vs. a transparent, cross functional view.
Jiquan Ngiam
8mo
We were working through our ideal customer definition for Lutra AI and 'X'Ops was top of our list. Instead of being replaced by Ops Copilots, I think they'll likely be the first heavy users of AI workflow tools, and perhaps the work will shift towards creating and managing AI automations (rather than doing the work by hand). Figuring out the right things to automate requires a lot of business knowledge.
10X hotel operations with gen AI | ex-Google 👋
8moIndividual high-volume low-complexity ops tasks could be mostly automated for years, but now gen AI is making it even more affordable. I'd still argue, though, that the key to successful ops lies in its strategic aspects, i.e. operating model design, deciding which "tool" to apply to which type of problem, performance mgmt, reliable processes, compliance, and seamlessly managing improvements. All this combined is art that many non-experts tend to seriously underestimate. IMO the keys to a breakthrough adoption of AI in ops are process mining and change management. When AI is able to deliver adequate reliability, transparency, and governance in these areas, not only will we be able to replace a significant % of workload in ops but also in roles that are ops-heavy without having an explicit ops designation in their names (and often offering an even greater potential for efficiency gains): 90% in program mgmt, 50% in product and people mgmt, 50% in sales, 30% in eng, etc. My view is that AI can replace software dev tasks earlier than ops because the amount of data available for models training is orders of magnitude greater in the case of code than for ops processes. Whether eng leads will admit this is a different question. 😉