When Legacy Meets Innovation: How Mid‑Market Manufacturers Can Multiply Safety Gains

An Executive Interview with Sharath Tadepalli, Director & Global Practice Leader for Data Science, Machine Learning and Artificial Intelligence at HGS

In mid‑market manufacturing, safety investments have traditionally been evaluated one system at a time — a new camera network here, an upgraded environmental sensor there. But according to Sharath Tadepalli, Director & Global Practice Leader for Data Science, Machine Learning and Artificial Intelligence at HGS, the real breakthrough comes when these legacy assets are connected and enhanced with emerging technologies such as artificial intelligence, machine learning, and the Internet of Things.

In that model, the whole truly becomes more than the sum of its parts. A CCTV system deployed years ago for security purposes, when linked with audio monitoring, environmental sensing, and AI‑powered analytics, transforms into a dynamic, real‑time safety intelligence network. Early warning signals — such as near‑miss incidents — become catalysts for coaching, training, and process improvement. The result is not just better compliance, but a measurable reduction in risk and a stronger safety culture.

In this Q&A, Tadepalli explains how mid‑market manufacturers can leverage the assets they already own, integrate them with emerging tools, and build a safety program that delivers strategic, operational, financial, and technological value. NOTE: This feature has been edited and organized into four sections: strategic assessments, operational imperatives, financial implications, and technology developments.

STRATEGIC ASSESSMENTS

BTR: Why do you believe workplace safety is moving beyond compliance reporting to a more proactive model?

Tadepalli: For a long time, environmental health and safety — or EHS — was seen as a checklist exercise. The focus was on passing inspections and avoiding penalties. That’s necessary, but it doesn’t address risk in real time. The shift we’re seeing now is toward practical risk reduction that prevents incidents before they happen. AI makes that possible by turning existing assets — like security cameras — into tools for continuous safety improvement, process optimization, and even cultural change.

BTR: What role do existing operational assets play in this transition?

Tadepalli: CCTV is the most visible example, but it’s just one piece of the puzzle. Companies have invested in audio monitoring systems, environmental sensors, and machine‑embedded diagnostics for years. These are often siloed. By integrating them through AI, machine learning, and IoT platforms, you create a unified intelligence framework. That allows you to monitor conditions, detect risks, and act before they escalate. It’s not about adding new layers of hardware — it’s about making what you already have smarter.

BTR: How does this apply specifically to mid‑market manufacturers?

Tadepalli: Mid‑market manufacturers often run lean operations. Budgets are tight, and systems have to deliver more than one benefit to be viable. That’s why leveraging legacy assets is so important. You don’t have to rip and replace. You can take the systems you already have — security cameras, machine sensors, environmental monitors — and connect them through AI and IoT to get far more value. This is especially powerful in facilities where operations are continuous and safety issues can have cascading effects on production schedules.

OPERATIONAL IMPERATIVES

BTR: You’ve emphasized “near‑miss” detection. Why is that so important operationally?

Tadepalli: Near‑misses are early warning signals. A misplaced bag on the shop floor, a loose wire dangling near a walkway — these may not cause harm in that moment, but they are accidents waiting to happen. Traditionally, they’re overlooked. With AI‑enabled monitoring, they can be detected, tagged, and addressed. That creates opportunities for direct, person‑to‑person coaching, as well as anonymized training for the broader workforce. The result is continuous improvement built into day‑to‑day operations.

BTR: How does this change collaboration inside organizations?

Tadepalli: Safety is no longer just the job of the EHS team. IT, operations, and facilities all play a role because the data comes from systems they manage. By working together, they can embed safety practices into daily workflows, instead of running them as separate initiatives. That’s what creates a real safety culture — not just rules, but shared ownership.

BTR: Can you share a scenario where this type of collaboration made a difference?

Tadepalli: We worked with a manufacturing client that had recurring slips in one section of its plant. Facilities blamed it on condensation from air‑handling systems, while operations pointed to workers not wearing the right footwear. By integrating environmental sensors, CCTV analytics, and maintenance schedules, the data showed the real cause — intermittent leaks from an overhead pipe that occurred only during specific production cycles. Once identified, maintenance could fix it, and operations could reinforce the appropriate safety protocols. That type of outcome only happens when data and teams work together.

FINANCIAL IMPLICATIONS

BTR: Many executives want to see a hard business case. What does the ROI look like?

Tadepalli: OSHA research shows that every dollar invested in prevention returns four to six dollars in avoided costs. That includes everything from workers’ compensation and legal fees to insurance premiums and productivity losses. But beyond the numbers, preventing even a single serious incident can pay for the system many times over. What’s harder to quantify — but equally important — is the impact on morale and retention. People want to work where they feel safe.

BTR: Is there a timing element to the investment decision?

Tadepalli: Absolutely. Unfortunately, many organizations come to us after a major incident. At that point, the cost of recovery is far higher than the cost of prevention would have been. The opportunity is to invest proactively — to act before something happens. That’s how you maximize both the safety impact and the financial return.

BTR: What about cost concerns for mid‑market companies?

Tadepalli: Mid‑market organizations often worry about budget, but they should see this as leveraging what they already own. You’re not buying an entirely new safety system — you’re enhancing the one you already have. The incremental investment in AI and integration software can be a fraction of what a new hardware rollout would cost. Plus, the operational efficiencies you gain — fewer disruptions, less downtime — add directly to the bottom line.

TECHNOLOGY DEVELOPMENTS

BTR: How do emerging technologies change what’s possible in workplace safety?

Tadepalli: The combination of AI, ML, and IoT lets you do things that weren’t feasible before. You can detect risks in real time, analyze patterns across multiple sensor types, and feed that intelligence directly into decision‑making. You can also integrate with enterprise systems so that, for example, a hazard detection automatically generates a maintenance order or updates onboarding materials.

BTR: How do you avoid costly hardware overhauls?

Tadepalli: That’s the beauty of this approach. Most organizations already have the hardware — the cameras, the sensors, the industrial equipment. We work with those assets, applying AI to make them smarter. You don’t need to rip and replace; you need to connect and enhance. That makes the adoption path far more practical and cost‑effective.

BTR: Where is this headed in the next few years?

Tadepalli: We’re going to see more integration of multimodal data — video, audio, environmental, and even wearable tech. The AI models will get better at understanding context. For example, a loud noise in one part of the plant might mean nothing if it’s part of a normal cycle, but in another context it could be a sign of mechanical failure. That’s where AI and IoT together will excel — knowing the difference and acting accordingly.

Bottom Line

The workplace safety conversation is evolving. The old compliance‑driven model — with its focus on inspections, checklists, and post‑incident reporting — is giving way to a proactive, intelligence‑driven model that detects and addresses risk in real time. By connecting existing operational assets through AI, ML, and IoT, organizations can turn security cameras, sensors, and machine data into a unified safety intelligence network.

This shift has strategic, operational, and financial implications. Strategically, it positions safety as part of the overall employee experience. Operationally, it turns near‑miss events into opportunities for coaching and training. Financially, it moves safety from a reactive cost to a proactive investment with measurable ROI. Technologically, it allows organizations to modernize without disruptive hardware overhauls.

For mid‑market manufacturers in particular, the opportunity is significant. Leveraging legacy assets and enhancing them with emerging technologies creates a multiplier effect — one that turns everyday operations into a continuous safety advantage. The tools to create a safer, smarter workplace are already in place. The question is whether leaders will connect them now — or wait until a major incident forces their hand.