Connected equipment is generating more data than ever. For foodservice operators, the greatest value of this equipment comes from turning information into smarter decisions. Looking at your connected equipment, are you reaping the most benefit possible from the data you’re collecting? Your equipment tells a story: Temperature sensors in refrigerators and freezers can alert your team before food safety is compromised. Ovens and combi units can track cooking consistency to improve quality and reduce waste. Dish machines monitor wash temperatures and chemical concentrations to support sanitation compliance. HVAC sensors can identify airflow problems before they affect employee comfort or energy costs. Smart fryers measure oil quality, helping to extend oil life without sacrificing food quality.
Viewed together, this data reveals trends that individual alarms cannot. You can spot equipment that's using excess energy, identify recurring maintenance needs, optimize preventive service schedules, reduce downtime, and improve labor planning. The data can help you identify priorities so you can budget for them. Rather than simply reacting to equipment issues, you and your team can use sensor data to improve efficiency, control costs and strengthen food safety. You’re limiting unwelcome surprises.

Unexpected equipment failures can disrupt service, compromise food safety, and create costly emergency repairs. This is particularly true during spring and summer, when seasonal menu changes, catering volume, and fluctuating temperatures place added strain on kitchen systems. Increasingly, foodservice operators are using predictive analytics to identify maintenance issues before breakdowns occur.
Connected equipment can now monitor temperature fluctuations, compressor performance, energy consumption, and operating cycles in real time. When analytics platforms detect unusual patterns, operators can schedule maintenance proactively instead of reacting to failures after they occur. According to Deloitte, predictive maintenance programs can reduce equipment breakdowns by as much as 70 percent, lower maintenance costs by up to 25 percent, and boost overall productivity by 25 percent.
In senior living and healthcare environments, where refrigeration reliability and meal continuity are critical, predictive maintenance can help protect resident safety and reduce operational disruption. Multi-unit restaurant brands are also using analytics dashboards to compare equipment performance across locations and prioritize capital spending more strategically.
As spring and summer guest demand ramps up, predictive analytics can help operators improve uptime, extend equipment life, and reduce the stress of unexpected failures during peak service periods.

Data has become central to how foodservice businesses perform and compete. But at the same time, there are so many sources of data that it can be easy to lose track of how they relate to each other. Increasingly, data dashboards are helping operators connect food quality, labor, and safety metrics into one daily decision-making system. Instead of reviewing reports after the rush, managers can monitor real-time KPIs like ticket times, voids, staffing levels, prep completion, and temperature logs in one view.
Day to day, this changes how foodservice businesses operate. For example, if a dashboard shows lunch sales rising faster than forecast while labor is understaffed, managers can call in support before service suffers. If food quality scores dip while overtime climbs, it may signal rushed execution or insufficient prep. Dashboards can also flag food safety risks — like a walk-in cooler trending above 41°F — triggering corrective action before product loss or inspection issues occur.
The benefits climb with multi-unit brands and institutional operators. They can use dashboards to standardize execution across locations, improving consistency while reducing labor waste and safety blind spots. The result is better control, which enables action before concerns balloon into larger problems.

Artificial intelligence (AI) is moving beyond buzzword status to deliver measurable results in foodservice operations, including menu planning that improves nutrition, cuts waste, and streamlines costs. The USDA estimates that food waste costs the industry an estimated $160 billion annually, largely due to overproduction and inaccurate forecasting. AI-based demand forecasting tools analyze historical sales, seasonal patterns, and consumption trends to predict exactly how much of each dish will be needed. A number of studies have found that on average, kitchens using these tools are reducing waste by 40 percent or more when compared with traditional methods.
At the same time, using AI for menu optimization can tailor offerings to resident preferences and dietary needs. These systems examine past meal popularity and resident feedback to highlight the dishes that drive satisfaction while suggesting portion adjustments or substitutions for less popular items. As a result, they can more closely align their food preparation and consumption, which supports nutrition goals and reduces leftover food.
AI systems have the potential to deliver even more benefits in senior living contexts. When combined with care records, AI dining data can help flag nutrition risks like weight loss, low intake, or missed meals. This can be especially valuable in memory care, helping dieticians intervene proactively and identify residents at risk of malnutrition earlier.

Compliance is an ever-moving target for foodservice operators. FDA Food Code updates, local health inspections, allergen labeling, and employee certification rules create a complex, time-consuming landscape.
Technology is helping operators shift from reactive to proactive compliance management — and it can yield some critical benefits. For example, Nashville-based Hattie B’s used timestamped IoT temperature logs and digital checklists to demonstrate compliance during an illness investigation — something paper logs couldn’t have done as convincingly. Their digital evidence satisfied health inspectors and helped them prove their business wasn’t the source of the problem, according to a report from their vendor.
AI systems can now cross-validate temperature data, flag missing documentation, and align records with up-to-date regulatory standards. These tools are increasingly seen as the “low-hanging fruit” for immediate return on investment, according to a report from New Food Magazine.
However, to get the most of these resources, the right kind of human support is needed. Some operators deploy systems without sufficient training — staff may ignore alerts or see them as “noise.” Legacy kitchen equipment may not integrate with new platforms, creating blind spots. Overreliance on automation without periodic manual audits risks undetected failures. Also, AI models may lack transparency: if the system flags noncompliance but staff can’t trace why, confusion and mistrust can result.
For CEOs, the lesson is that technology can drastically streamline compliance — but only when accompanied by a solid change management strategy, integration planning, and periodic validation. If you have already adopted these tools, are you conducting sufficient training and validation to make sure you’re getting the most useful and accurate information you can?

​Ghost kitchens have evolved from a delivery-only workaround into powerful test labs for new concepts — thanks to real-time data analytics. Instead of spending many months and tens of thousands of dollars on a full-scale restaurant launch (or even just a limited-time-offer launch), operators can spin up a virtual brand in days, run it out of an existing kitchen, and use order, rating, and repeat-purchase data to decide whether it’s worth expanding.
Rebel Foods in India pioneered this model, using its Rebel Operating System to launch and track dozens of micro-brands. Each brand focused on a different taste preference and customer profile, creating cross-brand synergy across the same kitchen infrastructure. In the process, their technology helped them streamline operations, reduce waste, and make data-backed decisions at scale.
As conventional foodservice operators increasingly rely on data to adjust elements of their existing operations, using ghost kitchens’ tech-first approach can also inform future decision-making at scale. Texas-based Sushi Zushi, which is emerging from bankruptcy, is using this approach to regain its footing, according to a report from the San Antonio Express-News. It has been using its kitchens to trial several licensed brands, including Nathan’s and Bennigan’s, and employing analytics to see if a concept clicks locally before signing longer-term agreements.
Even if you’re not planning to expand into licensed brands in your own operation, you might use a similar testing approach on a smaller scale. For example, you could A/B test a promotion across multiple delivery apps to gauge demand before committing to marketing spend or additional staff. Looking at your upcoming plans for development, how might you lean on analytics to use your existing resources most efficiently?

Getting precise about forecasting pays big dividends in foodservice. A recent report from Food Management said facilities using predictive analytics tools reduced food waste by up to 25 percent and improved ordering accuracy by 30 percent. How well do your current systems analyze historical consumption patterns, resident preferences (if applicable), census data and weather trends to predict food demand? If you see room for improvement when it comes to your food waste percentage and order accuracy, you can approach it from a few different angles: Having integrated POS and inventory systems helps encourage the flow of data so the algorithm can learn your actual vs. projected usage – then you can adjust accordingly. Using menu software that records individual choices can help you fine-tune projections. Adopting modular menu planning can help you adjust your menu on the fly in response to unexpected shifts in demand. Finally, scheduling weekly reviews of your forecast to reflect supply issues, illness, or changes in occupancy can help you stay on top of waste too.
These approaches can be especially helpful in senior living foodservice, where seasonal illnesses, resident turnover, fluctuating appetites, and unpredictable supply chains can all have an outsize impact on the business. (A number of tech-enabled forecasting solutions – CBORD, MealSuite and TrayTalk are a few – now offer forecasting modules tailored to senior care.)

At a time when so many functions of a foodservice business can be monitored and measured, data is a critical resource. Yet many businesses are still figuring out how to translate their collected insights into helpful action that can make the business perform better. Hospitality Technology’s 2025 Restaurant Technology Study addressed this issue, among other topics, and found some common themes in the businesses that are handling this well.
The businesses that are getting data collection/action right are doing four things: They are breaking down data silos by integrating information that lives in separate places so that people in different parts of the business all see the full story. They are moving beyond just describing what happened in their restaurant the previous week – instead, they’re using their data to predict what’s coming (then adjusting promotions, labor and other factors in response). They’re making analytics accessible beyond the headquarters of the business – so field managers, franchisees and general managers can access role-based insights that matter to them (and affect their decision-making). Finally, they are using the insights they gain to drive targeted changes, then measuring the result (and using that to inform future plans).
While many of these insights can be collected from within the business, partner data is useful and actionable too. The Japanese restaurant chain Maki & Ramen has used data from their third-party delivery partner to get real-time insights into delivery times and guest preferences. This has informed both their staffing and inventory management.
CKE Restaurants implemented a robust traceability system across its supply chain to enhance its food safety program. It allows them to track food items from suppliers to restaurant tables. The change has helped them target recalls with greater precision and alert locations to expired products. The sharing of this information – and actions taken as a result – have helped the company improve its overall inventory management.

For the second consecutive year, digital marketing is the top tech priority for restaurants, according to a new survey of 550 restaurant operators from Restaurant Business and Nation’s Restaurant News. This is especially true for fast-casual brands, with 60 percent of them noting digital marketing as their top tech priority. That said, 36 percent of operators overall said they were dissatisfied with the current digital marketing tools available and 37 percent were dissatisfied with their digital loyalty program. That’s quite a bit higher than the dissatisfaction they report experiencing with their POS system or kitchen display system, for example.
Why the frustration? Some themes have emerged about where digital tools can improve. Synchronizing content across channels can be inconsistent — and the layout of the content within those channels may not be optimal for the user. Operators are also not as confident as they could be that they are optimizing the consumer data they collect.
Does any of this sound familiar? If you’re among the operators looking to invest in digital marketing tools this year, asking questions in several areas may help you get clarity as you discuss your needs with marketing vendors. For example, how well are you managing your online reputation? (I.e., when a negative review comes in, how quickly do you respond — and what are you doing to address the root cause?) When you change menu prices (or booking information or other aspects of your guests experience), is the information consistent across your physical and digital sales channels? Are you highly visible in online searches of local restaurants? Do you have a consistent presence on social media, with content that suits the strengths of each platform — like behind-the-scenes videos on TikTok, beautiful images of your food on Instagram and community building on Facebook? Are you segmenting and personalizing your marketing messages to guests? Are your most successful competitors approaching marketing any differently? How are you using your data to assess “known and unknown unknowns” about your business? Could you be doing anything more with your data to extract value from it? What support do you currently lack that could help you improve in these areas? Identifying your problem areas can help you zero in on where a vendor may be of best use to you — or where they may not be an ideal fit.

​Every bit of differentiation helps right now when it comes to competing in the restaurant marketplace – and tech is helping operators deliver the kinds of personalized experiences that build engagement. This year, where can you improve brand affinity in your business? A recent report from Restaurant Technology News recommends using data to drive personal experiences at the level of the transaction. This includes offering awards tailored to personal preferences, exclusive access to events and promotions, and co-creation opportunities that allow loyal guests to play a role in influencing the development of the menu. IHOP, for one, uses Google Recommendation AI to offer guests menus that are tailored to their past ordering history and preferred price points. Chipotle invites guests to vote on potential new menu items via their app. More foodservice brands and third-party apps are also using gamification as part of the ordering process to help users earn rewards and make the experience more interesting and engaging. Think of options like a digital prize wheel that users can spin to earn an instant reward, asking guests to take surveys or polls in exchange for a personalized discount, or offering points based on taking actions such as downloading your app or ordering multiple times within a designated timeframe.