Workplace leaders recognize the importance of technology in optimizing the workplace, and many have taken steps to integrate digital solutions. But siloed systems are still stalling progress as they require significant resources to manage and analyze data. As many companies are predicting more days in the office in the coming year, the need for efficient data management is growing. 

By aligning IT, operations, and corporate real estate with experience-focused principles, enterprises can optimize the digital and physical workplace. A critical component is the implementation of technology backed by AI, explained Meg Swanson, chief market officer at Eptura, at the recent Gartner Digital Workplace Summit. The conference brought together digital workplace leaders focused on enhancing digital employee experience, leveraging generative AI, and improving technology adoption. 

Supporting a returning workforce: Current and coming challenges 

Swanson shared insights from Eptura’s 2025 Workplace Index report that bring current and upcoming challenges for workplace leaders into sharp focus. According to the research, organizations are struggling with disconnected systems that make it difficult for them to make informed decisions. 66% reported having between six and 40 technologies to manage their workplace operations. The result is that a third currently have 15+ full-time employees dedicated to analyzing this data. 

It’s clear that companies need more efficient ways to leverage their data, quickly. According to the Index, 34% of businesses surveyed expect to increase the number of days in the office over the next 12 months. Office attendance also tends to follow a pattern of peak occupancy occurring in the middle of the week. However, after many downsized their workspaces in recent years, they are now looking for ways to accommodate increased traffic while leveling attendance across the working week. 

To address these challenges, 71% of organizations surveyed had hired a digital workplace leader in the past 18 months. And a key skill set of this new organizational function is understanding how to drive tangible business impact by applying AI to support workplace operations. 

How progressive AI implementations deliver more value from data 

Companies are already leveraging AI for smart automation to streamline existing workflows. Most businesses are at early stages of AI adoption, but as they increase the breadth and depth of AI use cases, they can deliver additional value – often from the same data sets. 

An example of a simpler application of AI is leveraging data about employee needs and preferences to automate desk bookings. When an employee is reserving a desk, the AI auto-suggests desks that are close to the ones already booked by the employee’s teammates. At more advanced stages, AI can take that same desk booking data to predict the best time to schedule cleaning teams and maintenance work. Because the AI knows when the office tends to have lower occupancy, it can schedule work for times when it will disrupt the fewest people. 

Optimizing workplace operations with AI 

Each step of AI maturity increases business value by providing different benefits to improve employee experience and automate admin work for facility and workplace managers, giving them the time they need to focus on strategic thinking to deliver productive spaces. 

Descriptive analytics in the workplace: Dashboards connect and contextualize data 

Descriptive AI focuses on analyzing and summarizing historical data to provide insights. It includes the use of data mining, data aggregation, and machine learning techniques to identify patterns, trends, and relationships across large datasets. The primary goal of descriptive AI is to provide a clear and concise overview of past events, helping organizations understand their current state based on historical data. 

Dashboards are a powerful visual and interactive way to understand and interpret historical data. They help users quickly grasp complex information, make informed decisions, and monitor performance in real time. By leveraging dashboards, organizations can enhance decision-making processes and improve overall efficiency and productivity through quick insights, improved communication, and enhanced monitoring. 

Organizations can customize dashboards to include: 

  • Data visualizations: Include visual elements to represent data in a clear and understandable way and display data in tabular form, making it easy to compare different metrics 
  • Historical data summarizations: Summarize historical data to show how key metrics have performed and highlight trends and patterns in the data, such as seasonal variations 
  • Key performance indicators (KPIs): Include real-time and historical KPIs, providing a comprehensive view of performance over time 
  • Data aggregation: Aggregate data from multiple sources, providing a unified view of organizational performance, and integrate data from different systems to ensure a comprehensive and accurate representation 

Facility and workplace teams specifically can use AI-powered dashboards to get a more complete view of building and asset performance based on utilization. 

Diagnostic analytics in the workplace: Past patterns support more intelligent bookings 

Diagnostic AI helps teams use past patterns to uncover why something happened. It goes beyond simply describing what occurred by searching for underlying causes and factors that contributed to specific outcomes. The technology incorporates advanced analytics techniques, including statistical analysis, data mining, and machine learning, to identify patterns, correlations, and anomalies in data. Organizations can gain deeper insights and context, enabling more informed and strategic decision-making. They can understand what happened but also why it happened, which is crucial for developing effective solutions and strategies. 

Companies can use diagnostic AI to analyze historical booking data to understand why certain patterns of usage occurred. The company could then leverage these insights to make improvements to the booking system or office layout. By providing actionable insights, diagnostic AI helps organizations optimize their booking systems, improve resource utilization, and enhance the overall user experience. 

For example, when identifying overbooking patterns, diagnostic AI performs: 

  1. Time analysis: Examining booking data to identify times of the day when overbooking is most common 
  2. Overlap detection: Finding overlapping meetings and suggest adjustments to booking policies, such as implementing buffer times between meetings 
  3. Data-backed recommendations: Suggesting increasing the availability of rooms during peak times or suggesting alternative rooms to users 

The process is ongoing, with AI able to leverage new data to support continuous improvement. 

Predictive analytics in the workplace: Layouts tightly match behavior with automatic floorplan restacking 

Predictive AI is a branch of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Companies can use predictive AI to optimize operations by predicting maintenance and parts inventory needs and workspace demand to improve resource allocation. For example, a facility manager might use predictive AI to predict when machinery is likely to fail and schedule maintenance proactively. 

Because predictive AI helps organizations anticipate future events and trends, it empowers them to make more informed and strategic decisions. 

Automatic floorplan restacking involves using AI to dynamically adjust the layout and allocation of office spaces based on predicted usage patterns. The process can include reassigning desks, meeting rooms, and other facilities to optimize space utilization and meet the changing needs of employees. The AI-back feature delivers improved space utilization enhanced employee experience, and cost savings from data-back decision-making. 

The process involves the following automated steps: 

  • Data collection 
  • Pattern recognition 
  • Forecasting 
  • Dynamic adjustments 

For example, a large tech company with a flexible work policy uses an automatic floorplan restacking system that collects data on how employees use the office space. The system identifies that the demand for meeting rooms spikes on Mondays and Fridays, while individual workspaces are underutilized during these times. Based on this prediction, the system dynamically adjusts the floorplan to convert some individual workspaces into temporary meeting rooms on those days, ensuring the office is always configured to meet the current and predicted needs of the employees, enhancing productivity and satisfaction. 

Prescriptive analytics in the workplace: Facility operations grow more efficient 

Prescriptive AI delivers advanced analytics that goes beyond predicting future outcomes to suggest specific actions to take in order to achieve a desired outcome. For optimization, prescriptive AI uses algorithms to determine the best course of action to achieve a specific goal, such as maximizing efficiency, minimizing costs, or improving employee satisfaction.  

It can also incorporate rule-based systems to ensure that recommendations align with organizational goals, policies, and budgetary and temporal constraints.  

Recommending facility operational efficiencies involves using analytics to not only predict future needs and potential issues but also to suggest specific actions that can optimize operations, leading to improvements in efficiency and lowered costs. 

The first step for prescriptive AI collection can include looking at data from sensors, maintenance records, and usage patterns. Next, during predictive modeling, the AI examines occupancy patterns to identify peak usage times for different areas of the building, and equipment performance to predict when HVAC systems, elevators, and other critical equipment are likely to require maintenance. 

At the optimization stage, prescriptive AI makes suggestions for a variety of workflows, assets, and systems, including: 

  • Maintenance scheduling: Implementing a maintenance schedule that minimizes disruptions and maximizes equipment lifespan. For example, performing maintenance on HVAC systems during off-peak hours. 
  • Energy management: Adjusting the HVAC and lighting systems based on real-time occupancy data to reduce energy consumption. Prescriptive AI could recommend turning off lights in unoccupied areas or adjusting the temperature in less-used spaces. 
  • Space utilization: Reconfiguring the layout of the building to better meet the needs of employees. For example, prescriptive AI might suggest converting underutilized conference rooms into collaborative workspaces. 

Once the facility teams have the lists of recommended actions, they can decide which to implement. 

Cognitive analytics: Intuitive, individually customized support in the automated workplace 

Cognitive AI mimics human thought processes and cognitive functions. It is designed to simulate human reasoning, learning, and problem-solving capabilities. Key components include natural language processing, machine learning, reasoning and inference, and sensory input.   

An automated workplace is not about replacing human tasks with machines. Instead, the goal is augmenting human capabilities and making work more intuitive and effective. 

In the office, cognitive AI can act as an intelligent assistant, helping employees with scheduling meetings, managing emails, and providing information. Because cognitive AI can understand natural language and context, the interactions are more intuitive. It can also help create and then support smart workflows by predicting the next steps in a process and suggesting actions, reducing the need for manual intervention. 

It can also create individually customized workspaces through: 

  • Understanding preferences: Learn each employee’s preferences through interactions and feedback. For example, it might note that an employee prefers a certain temperature, lighting level, and background music. 
  • Automating adjustments: Automatically adjust the workspace settings. It can set the room temperature to the employee’s preferred level, adjust the lighting to their liking, and play their favorite background music. 
  • Contextual awareness: Adapt to the context of the work. When an employee is in a meeting, it might dim the lights and turn off background music to create a more focused environment. And when they’re working on a creative task, it might adjust the lighting to be more stimulating and play music that enhances creativity. 
  • Personalized recommendations: Provide personalized recommendations to improve productivity. It might suggest taking a short break if it detects signs of fatigue or recommend specific tools and resources based on the current task. 

By integrating these features, cognitive AI can help enterprises create highly personalized and efficient workspaces that enhance employee wellbeing, which increases productivity and retention. 

Connect people, places, and assets to drive efficiency and support employees 

Teams can use AI to streamline workflows, improve data analysis, and create personalized workspaces, making it critical to optimizing both the digital and physical workplace. By auto-suggesting desks close to teammates, predicting the best times for maintenance, and tailoring workspace to individual preferences, it can help create more dynamic workplaces, ultimately leading to higher satisfaction and better business outcomes. 

Learn how to automate your world of work. 

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Erin has 15 years of experience, including 10 years in thought leadership on workplace management and the built environment. In her current role she oversees teams responsible for worktech insights and engaging Eptura’s 16,000 customers worldwide. Previously she led communications for the International Facility Management Association, a global industry nonprofit dedicated to professional development for workplace strategists, building managers, and corporate real estate.