Until recently, conversations about enterprise AI have focused on models, infrastructure, and computing power. But who leads Generative AI at LogicMonitor, many organisations are discovering that the real challenge isn’t the technology itself. It’s whether the business is prepared to use it effectively
The timing could hardly be more significant. AI capabilities are advancing at a pace that would have seemed impossible just a few years ago.
“I think there is this energy of if you’re a builder or a creator, it’s just like the possibilities are endless,” said Karthik Sj. “You are literally seeing from idea to software in hours.”
That rapid acceleration is creating enormous opportunities for enterprises. But it is also exposing a gap between what the technology can do and what organisations are ready to implement.
AI isn’t a silver bullet
One of the biggest misconceptions about AI is that enterprises can simply switch it on and immediately transform operations.
Instead, Karthik Sj argues that successful AI adoption requires careful planning, governance, and an understanding of where automation will deliver the most value. Organisations must identify suitable use cases, assess risk, and gradually expand deployment rather than attempting to automate everything at once.
“If the AI goes rogue, it’s going to do it at machine speed,” he warned.“And so that can have catastrophic impact for companies.”
That’s why LogicMonitor begins customer engagements with what it describes as an automation readiness assessment, helping organisations understand where AI can realistically deliver benefits and where further preparation may be required.
The human element still matters
Despite advances in autonomous agents, Karthik Sj believes AI projects succeed when business expertise and technical expertise work together.
Rather than replacing domain knowledge, AI depends on it. Organisations need people who understand existing workflows paired with AI specialists who can automate and optimise those processes.
“You really want to get a domain expert who knows the process really well paired with an AI expert who knows how to build agents,” he explained.
This collaboration is critical because AI systems still require evaluation and oversight. Real experts determine whether outputs are acceptable and ensure automated workflows align with business goals.
The result is a more pragmatic view of enterprise AI, one focused less on replacing workers and more on redesigning how work gets done.
The real bottleneck has shifted
Ironically, many of the technical barriers that slowed AI adoption are beginning to disappear.
Technologies, such as the Model Context Protocol (MCP), are making it easier for systems and data sources to communicate, while new architectures increasingly allow organisations to bring AI to their data instead of moving sensitive information elsewhere.
As these technical challenges fade, a new bottleneck is emerging.
“The bottleneck has now come down on the human side, on the organisations,” said Karthik Sj. “How are they organising resources to take advantage of this once-in-a-generation technology?”
That means organisational structure, team alignment, and executive commitment may become more important than technology selection.
AI is becoming a boardroom priority
Perhaps the clearest sign of AI’s growing maturity is the level of executive interest.
Based on customer engagement events across North America, Europe, and Asia-Pacific, Karthik Sj said he has been surprised by the enthusiasm organisations are showing for autonomous operations and AI-driven transformation initiatives. Many are no longer treating AI as an experimental side project. Instead, it has become a strategic business priority.
“I’ve been surprised by the level of interest, by the level of enthusiasm, by the level of customers that have this as a formal project,” he said. “This is absolutely now front and centre of the board.”
For enterprise leaders, that shift may be the most important AI development of all.
The technology continues to evolve at remarkable speed. Yet success increasingly depends on factors that are far more familiar: identifying the right problems, bringing together the right people, and creating a roadmap for change.
As enterprises race to adopt AI, the organisations that gain the greatest advantage may not be those with access to the most advanced models. They may be the ones best prepared to put them to work.
Watch or listen to the full interview on Petri.com
