For many travel and logistics providers, workforce challenges have become an increasing source of anxiety. Labor shortages are frequent and labor costs are rising. Workforce planning missteps can result in reduced margins and degraded customer service. These workforce challenges affect more than three million people employed at the 40 top-grossing travel and logistics companies.
Companies have limited control over the future cost and availability of labor. But proper strategies can help maximize a frontline workforce’s productivity, ensuring the right number of employees are deployed to the right places at the right times. This can build resiliency to handle unexpected events. Across travel and logistics companies—such as airlines, hotels, logistics services providers, and others—significant value could be created through improved workforce management that simultaneously incorporates long-range, near-term, and day-to-day goals.
Today’s challenges stem from decisions made over past decades. Although they are rich with data, many companies have underinvested in the foundations and optimization tools that would allow them to use that data to plan more effectively. With limited visibility, workforce planning is often driven by outdated assumptions and methods, frequently resulting in situations where complex workforces are managed using overly simple models and spreadsheets. The tools companies currently use often lack the precision and agility needed to handle today’s workforce complexities in an ever-changing operating environment.
Newer solutions, such as generative AI (gen AI), can integrate large and disparate data sets to generate fresh capabilities and greater workforce agility. But tools and analytics alone are not sufficient to address ongoing challenges. Solutions should accommodate the unique needs of an organization, the goals of its different business units, and its existing technology architecture. Even the best workforce tools can be ineffective if not paired with the right process changes and performance management.
Companies should put in place holistic processes and collaborative managerial structures to help frontline employees adeptly handle the daily challenges they face. Tech advances can be paired with shifts in mindset and culture, spurring lasting productivity gains. We’ve seen companies use these approaches to boost operating margins, achieve cost reductions, and improve customer service ratings.
As baby boomer retirements ramp up, companies are projected to lose decades of expertise. Now is the time to refresh workforce planning for the next generation of employees—using a new generation of tools and strategies. Companies can consider these five themes as they develop a comprehensive approach to workforce management: digital demand reduction, advanced resource and capacity planning, hiring and people development, day-of decision making, and continuous improvement.
The industry faces significant workforce challenges
Workforce challenges abound in the travel and logistics sectors. For example, many companies are already dealing with—or could soon encounter—labor shortages resulting from a variety of factors:
- High attrition rates can lead to unfilled roles. Turnover of logistics employees is up by 33 percent compared with before the COVID-19 pandemic.
- An expected wave of baby boomer retirements could lead to shortages of qualified employees in many sectors. About four million boomers are leaving the workforce each year. Almost 6,000 pilots per year are reaching the Federal Aviation Administration–mandated retirement age of 65.
- Training bottlenecks across industries are constraining companies’ capacities to upskill workers who can fill roles involving greater complexity. The airline sector in particular is navigating a postpandemic return of demand (creating the need for more workers) coupled with highly specialized training requirements for vital employees such as pilots and mechanics.
- The rapid growth of e-commerce platforms—with 10 to 20 percent of every e-commerce dollar going toward logistics, McKinsey research shows—is ratcheting up general demand for logistics workers.
The rise in labor demand coincides with, and in some cases can exacerbate, an increase in costs (Exhibit 1). Costs have grown due to a confluence of developments—headlined by general inflation—with labor costs often playing a major role. Labor costs within the travel and logistics sectors have increased by as much as 40 percent from 2018 to 2023.
Meanwhile, the rise of e-commerce has fueled heightened consumer expectations about service speed (for example, same day) and experience (for example, seamless ordering and tracking), increasing pressure on logistics providers. And while labor demand and costs have increased for travel and logistics companies, workforce productivity has not risen commensurately. The highest-grossing 40 companies in travel and logistics increased productivity—as measured by revenue per employee—by 15 percent from 2018 to 2023, but other industries with similarly large, distributed workforces grew productivity faster (Exhibit 2).
Despite these trends, many travel and logistics companies continue to rely on outdated workforce planning approaches. These traditional approaches do not incorporate the full value of AI-powered innovations. They are often ill-equipped to manage seasonal peaks and unexpected complications, which require dynamic responsiveness to keep operations running smoothly. Sample data from one airline reveals that use of static planning models can lead to misaligned workforce scheduling—with as much as 60 percent of operating hours either understaffed or overstaffed.
Five AI-enabled actions to boost workforce productivity
Developing world-class workforce management capabilities can have applications across a wide range of work groups. Operators (including drivers, pilots, and crews), customer care workers (including call center employees and gate agents), planning teams (such as dispatchers and schedulers), and commercial and intake functions (such as reservation taking) can all benefit from AI-enabled approaches.
Many organizations excel at certain aspects of workforce management, which underlies the importance of identifying specific, remaining gaps for improvement. By applying AI-powered solutions in five areas, travel and logistics companies can get the most out of the workforces they have—while taking steps toward creating the workforces they want.
Digital demand reduction
Through digital demand reduction—making use of automation and self-service—some work can be rendered unnecessary before it reaches frontline employees. Freeing the front line from waste creates flexibility and agility for an organization. It allows more time to be spent on critical tasks that add value to the customer proposition.
Leading organizations are using AI to develop an understanding of when and why customer issues occur—before they occur. Predictive models can review past customer interactions to help anticipate future customer needs, and proactive interventions can resolve issues before they arise. The organization becomes able to make adjustments before customer complaints start flooding in.
On the employee side, automating repetitive tasks can allow employees to be redeployed to higher-impact activities. Digital intake tools streamline information collection, which can then be integrated from multiple systems to provide synthesized information to employees where and when they need it. Using gen AI to synthesize outputs could improve both productivity and employee experience. When work is routed to the right place quickly, it eliminates delays and the generation of redundant work orders.
On the customer side, digital tools can help solve problems quickly and efficiently. Creating online tools such as AI chatbots and self-serve applications that handle order management and scheduling gives customers the information they need—or delivers information the business needs—without generating tasks for frontline employees. We’ve seen companies use digital deflection to achieve 15 to 20 percent reductions in call volume.
The benefits of automation and new data tools will not be captured automatically. An organization must redesign work requirements and optimize workflows based on demand reduction. But done right, the gains can be substantial. Many travel and logistics companies comprise complex businesses, often grown over the years through mergers, acquisitions, and expansions into new service models, which can lead to an accumulation of redundancies or inefficiencies in workflows. Given the scale of these companies, digitally reducing demands on their large, distributed workforces can greatly increase productivity and result in outsize returns.
One logistics company was struggling with workflow redundancies between the employees who pick up and deliver packages and those who process, sort, and store them. A package would get scanned multiple times, and the same information would also get manually entered into multiple databases. Using ride-alongs, site visits, and workshops involving leadership from the technology, operations, and commercial teams, the company was able to spot opportunities to collect better data and generate new insights. This led to identifying AI-powered solutions that could integrate scanning systems and eliminate unnecessary steps, which led to a 10 percent reduction in processing time.
Resource and capacity planning
Travel and logistics companies must navigate demanding operational landscapes that involve extensive equipment and inventory assets, while accounting for uncertainties in customer demand. They need to optimize for both long-range and seasonal planning cycles. They must contend with weather challenges, supply chain tangles, and other unforeseen disruptions. All these challenges make it critical, but also difficult, to align workforce capacity with demand across geographies and throughout the business cycle.
Modern, more granular forecasting tools can use AI analysis of vast amounts of historical data to anticipate staffing needs more accurately, enabling companies to strategically allocate resources in line with specific job functions and skill requirements. AI-based models for long-term and short-term forecasting allow for more flexible staffing and improved accuracy through frequent updates. In environments where historical data doesn’t exist, it can now often be simulated from other sources. This can help to reduce overtime, minimize idle time, and avoid vendor penalties caused by inaccurate forecasting.
AI can also enable dynamic scheduling that adapts to accommodate daily demand fluctuations and seasonal volatility. Integrating multiple data sources provides a comprehensive view, improves planning tool accuracy, and ultimately leads to better-informed decision making. These tools create a continuous feedback loop between planning and execution, allowing companies to nimbly adjust to changing market conditions and tailor workflows effectively. This continuous loop also enables automation, significantly reducing the need for manual intervention.
One airline needed to update its processes to adhere to new regulations regarding the maximum duration of pilot shifts (which had created a need to have more pilots available in reserve). The airline introduced digital tools that improved the accuracy of pilot demand forecasts by roughly 8 percent. With better anticipation, resource planning became more precise. Careful integration of digital tools into the airline’s processes allowed for rapid deployment and scalability.
In call centers, AI-based forecasting models have provided significant value by streamlining workforce planning. These models can achieve more than 90 percent accuracy in predicting staffing needs, whether at 15-minute intervals or in projections stretching up to 18 months into the future. With this improved accuracy, overtime costs can decrease by 15 to 20 percent.
Hiring and people development
Leading organizations use digital solutions to manage talent pipelines from hiring through training and retention. AI can help identify great candidates (though it is important for organizations to pay close attention to the risk that bias could be introduced through AI assessments) and automated application processes and candidate screening can speed up hiring. Tailored training programs help to accelerate both onboarding and productivity growth. Simulation models guide learning and development by identifying the right training candidates at the right stages of advancement and testing for different potential scenarios.
Tech-aided deep dives into employee traits, behaviors, performance, and business outcomes can identify important workforce characteristics, which can form the basis for targeted recruiting strategies. AI-powered tools help companies forecast labor demand at a detailed level that considers skill requirements, job functions, and the cadence of different departments’ talent needs.
One airline faced a critical pilot shortage that had resulted in large part from challenges in the training planning process. Its training landscape encompassed more than 40 different training programs, ten types of trainers, and six promotion pathways. The company implemented a digital-twin solution with two modules: the first produced a more streamlined pilot training plan, and the second introduced simulations of disruptive events (for example, instructor sickness) to test the robustness of the plan against potential real-world situations. This resulted in a 5 to 10 percent reduction in training bottlenecks, a 3 to 5 percent decrease in overhiring, and a 15 to 20 percent reduction in planning time through faster scenario analysis.
Another airline faced a tight deadline to hire a large number of service professionals for its ground operations. To meet this goal, it transformed its recruitment strategy by adopting a data-driven, agile framework. By analyzing the hiring process step-by-step, it identified bottlenecks and gained insights into conversion rates. The agile approach allowed for rapid adjustments and testing, enabling the airline to reduce inefficiencies and improve the candidate experience. This helped the airline to meet its recruitment goals by streamlining the process and boosting transparency while creating a more adaptive system.
Day-of decision making
A typical travel and logistics company operates a distributed network comprising tens or hundreds of sites, each of which must run seamlessly and independently. For these sites to function, a local team needs to make hundreds of workforce decisions each day—such as where and when to deploy frontline employees and how to adjust plans when unexpected disruptions occur. To ensure efficiency, consistency, and decision making that aligns with broader company objectives, organizations must support local leaders by providing standards, frameworks, and tools to conquer day-of operational challenges.
It’s important to clearly define and communicate the policies that govern decision making so local teams can replicate that reasoning on their own. But even when local teams are empowered in this way, decisions should not be made in isolation. Frequent huddles help organizations learn what types of challenges local operations are facing and whether current policies and tools are sufficient to support field offices.
Companies can develop recovery playbooks tailored to specific situations. But between unplanned employee absences, late trains/trucks/planes, bad weather, and other unexpected impediments, each day presents its own unique workforce challenges, and unforeseen chaos always lurks around the corner. By leveraging analytical insights—such as real-time data about resource availability and task requirements, and anticipatory insights that help predict demand spikes and troughs—and integrating these analytical capabilities with recovery optimization and practical execution, teams can adapt to evolving needs in real time.
One global logistics company was failing to see productivity improvements in its large network of drivers. Analysis revealed that drivers’ routes were redesigned too infrequently, and drivers were relying on similar routes each time they deployed, without making adjustments for changing circumstances. When the company began using AI-enabled daily route optimization to minimize distance traveled, it saw a 15 percent reduction in travel time for drivers, leading to significant productivity gains.
Continuous improvement
Implementing new tools and workflows, and hiring exceptional individuals, are both great first steps. But they are not enough. An organization’s culture—encompassing shared values, beliefs, attitudes, and behaviors—will serve as the true engine of its transformation. Central to this culture is the ability to foster continuous improvement through clearly defined standards, effective performance management, and universal accountability.
Clear work standards will specify timing and quality expectations while identifying the proper personnel to execute tasks. Real-time performance management is possible when powered by a bevy of utilization, productivity, and efficiency metrics that are provided to all relevant employees through user-friendly digital dashboards. Supervisors can use levers to reward employees or hold them accountable. Both monetary and nonmonetary incentives are essential for motivating employees and ensuring alignment with organizational objectives.
Quickly understanding the root causes behind the over- or underperformance that is revealed by a metric allows for fast action and correction, leading to continuous improvement. Digital solutions can help automate root-cause analysis. Leading organizations are also using digital analytics to improve feedback and coaching: an AI recommendation engine can send personal nudges to employees suggesting areas to improve, and it can alert managers when a coaching opportunity arises.
One railroad company saw limited usage of its automatic routing tool, despite significant investment, due to frontline resistance. Technical challenges and fears of job displacement hindered employee adoption. The railroad company responded with an accelerated rollout and targeted interventions. A root-cause analysis guided tailored initiatives, resulting in a 40 to 50 percent increase in trained dispatchers, a 25- to 30-percentage-point rise in automated routing usage, and a threefold increase in network portions being routed automatically. AI can enable the next frontier of efficiency for frontline employees, but achieving full potential requires a combination of tools, processes, and change management.
Navigating transformations requires a strategic approach to change management. It entails building capabilities, supporting process improvements, and rethinking ways of working. Without effective change management strategies, even the best-designed tools and workflows may face resistance or fail to achieve their intended impact.
Any change management effort can begin with the creation of a compelling change story that clarifies the need as well as the benefits for everyone affected. It can make clear what should be changed and what the expectations are for new ways of working, and it can provide appropriate training so that employees are able to adopt the changes. Leaders can role model the change and reinforce its importance, helping to coach their teams through the transition and intervening when challenges arise.
Companies will likely have strengths in certain areas of workforce management, while gaps will exist in other critical areas. To understand these gaps, companies might consider asking themselves questions such as these:
- Do our policies and workflows effectively streamline tasks, removing unnecessary burdens on employees?
- Do we have the appropriate technical infrastructure and skilled team members to leverage AI-powered workforce management tools effectively?
- Is our current hiring and training pipeline aligned with operational demands, ensuring workforce readiness?
- How well do we manage and respond to uncertainty on the day of operations?
- Do we have transparent and timely performance metrics that are properly communicated to track performance and identify opportunities for improvement?
Once an organization identifies opportunities, how can it get started? The most innovative companies are moving beyond point solutions to an integrated approach that seeks not just to solve current pain points but to define a new performance culture that spans the organization. These efforts bring together data architects and scientists with frontline leaders and workers to rethink how work is done, driving improvements both in performance and quality of life. While the broader transformation may take years, impact through proof of concept and frontline change efforts can be seen in as little as a few months. Success will require a holistic solution that combines innovative analytics with operational expertise. But moving toward comprehensive, data-backed, AI-powered workforce planning is a journey that can begin immediately.
