Most midsize companies don’t have an AI access problem. They have an AI readiness problem. Issue related to strategy and integration are why there’s a reported 95 percent failure rate of AI rollouts to deliver measurable impact on P&L.
“The AI rollouts fail not because the models are bad, but because they never get embedded into real workflows,” says Ramana (Raam) Bhavaraju, founder and CEO of NCompas Technology Solutions Inc. “Most midsize companies treat AI as a side experiment, not as a business initiative. They spin up isolated pilots with no clear owner, no accountability and no plan to scale, so the results never move beyond a sandbox.”
Smart Business spoke with Bhavaraju about why most AI rollouts fail and what companies can do to ensure a better ROI.
What problems arise from poor AI rollout?
Leadership often does not apply change management solutions to their AI rollout. They invest heavily in models, but lightly on training, governance and workflow redesign. Because of that, employees either don’t trust the outputs or don’t change how they work, which creates a learning gap inside organizations. AI isn’t failing in the lab. It’s failing in the last mile between the proof of concept and how people actually work.
When AI is treated more as an experiment instead of an operational capability, the ROI only shows up in slide decks, but never in the P&L. About half of companies’ generative AI budgets go to sales and marketing, rather than back office automation and operational efficiency, which is where the highest ROI potential exists. This misallocation leads to very little hard impact.
Surveys also show that companies find deployment was harder than anticipated. That has more to do with the struggle around the integration, governance and the data readiness side. The result is that employees quickly bypass the technology rather than using it, which is essentially throwing that investment of time and money down the drain.
How should companies rollout AI?
Start with one high-value use case and tie that to hard metrics such as reducing manual processing, improving collections or speeding up operations — have a specific goal in mind, a specific operational metric, and drive that one use case.
Companies should also fix their data and integration. The fastest way to improve AI success is by cleaning up and connecting the data on which the models rely.
At the end of the day, it’s people and operational workflows that matter the most. AI works best when it’s embedded into the tools people already use with clear ownership, training and guardrails. Otherwise, a company is just creating a system that people will work around. Put lightweight governance around it — who owns the model, how it’s monitored, how frontline employees can raise issues — and emphasize human centric applications that people can actually adopt.
To determine the best use case, stakeholders should look for an aspect of the business that is big enough to matter but small enough to quickly show results. A use case that takes too long to deliver or has too little value will die on its own. It’s a big exercise, but it’s a useful exercise for business leaders to do with their departments.
Measuring return depends on the task. If an operation takes 10 days, does the AI application reduce that time? Can an AI application increase productivity by freeing up people to do tasks they otherwise would not have the time to do?
In most cases, it is not that AI is failing at a company. Instead, it’s more likely that most companies are approaching AI implementation in a way that almost guarantees disappointment. Before rolling out an AI application, clearly define the business problem it can address, lay a solid data foundation, and then apply it to one use case that can deliver maximum impact in the least amount of time. AI isn’t a moon shot. If it is done as a series of well-designed, well-integrated steps, the impact will go up dramatically. ●
INSIGHTS Artificial Intelligence is brought to you by NCompas Technology Solutions Inc.
