In his 40 years leading McLeod Software, one of the nation’s largest providers of transportation management systems for truckers and 3PLs (third-party logistics providers), Tom McLeod has seen many a new technology product introduced with much hype and promise, only to fade in real-world practice and fail to mature into a productive application.

In his view, as new tech players have come and gone, the basic demand from shippers and trucking operators for technology has remained pretty much the same, straightforwardly simple and unchanged over time: “Find me a way to use computers and software to get more done in less time and [at a] lower cost,” he says.

“It’s been the same goal, from decades ago when we replaced typewriters, all the way to today finding ways to use artificial intelligence (AI) to automate more tasks, streamline processes, and make the human worker more efficient,” he adds. “Get more done in less time. Make people more productive.”

The difference between now and the pretenders of the past? McLeod and others believe that AI is the real thing and, as it continues to develop and mature, will be incorporated deeper into every transportation and logistics planning, execution, and supply chain process, fundamentally changing and forcing a reinvention of how shippers and logistics service providers operate and manage the supply chain function.

“But it is not a magic bullet you can easily switch on,” McLeod cautions. “While the capabilities look magical, at some level it takes time to train these models and get them using data properly and then come back with recommendations or actions that can be relied upon,” he adds.

THE DATA CONUNDRUM

One of the challenges is that so much supply chain data today remains highly unstructured—by one estimate, as much as 75%. Converting and consolidating myriad sources and formats of data, and ensuring it is clean, complete, and accurate remains perhaps the biggest challenge to accelerated AI adoption.

Often today when a broker is searching for a truck, entering an order, quoting a load, or pulling a status update, someone is interpreting that text or email, extracting information from the transportation management system (TMS), and creating a response to the customer, explains Doug Schrier, McLeod’s vice president of growth and special projects. “With AI, what we can do is interpret what the email is asking for, extract that, overlay the TMS information, and use AI to respond to the customer in an automated fashion,” he says.

To come up with a price quote using traditional methods might take three or four minutes, he’s observed. An AI-enabled process cuts that down to five seconds. Similarly, entering an order into a system might take four to five minutes. With AI interpreting the email string and other inputs, a response is produced in a minute or less. “So if you are doing [that task] hundreds of times a week, it makes a difference. What you want to do is get the human adding the value and [use AI] to get the mundane out of the workflow.”

Yet the growth of AI is happening across a technology landscape that remains fragmented, with some solutions that fit part of the problem, and others that overlap or conflict. Today it’s still a market where there is not one single tech provider that can be all things to all users.

In McLeod’s view, its job is to focus on the mission of providing a highly functional primary TMS platform—and then complement and enhance that with partners who provide a specialized piece of an ever-growing solution puzzle. “We currently have built, over the past three decades, 150 deep partnerships, which equates to about 250 integrations,” says Ahmed Ebrahim, McLeod’s vice president of strategic alliances. “Customers want us to focus on our core competencies and work with best-of-breed parties to give them better choices [and a deeper solution set] as their needs evolve,” he adds.

One example of such a best-of-breed partnership is McLeod’s arrangement with Qued, an AI-powered application developer that provides McLeod TMS clients with connectivity and process automation for every load appointment scheduling mode, whether through a portal, email, voice, or text.

Before Qued was integrated, there were about 18 steps a user had to complete to get an appointment back into the TMS, notes Tom Curee, Qued’s president. With Qued, those steps are reduced to virtually zero and require no human intervention.

As soon as a stop is entered into the TMS, it is immediately and automatically routed to Qued, which reaches out to the scheduling platform or location, secures the appointment, and returns an update into the TMS with the details. It eliminates manual appointment-making tasks like logging on and entering data into a portal, and rekeying or emailing, and it significantly enhances the value and efficiency of this particular workflow activity for McLeod users.

LEGACY SYSTEM PAIN

One of the effects of the three-year freight recession has been its impact on investment. Whereas in better times, logistics and trucking firms would focus on buying tech to reduce costs, enhance productivity, and improve customer service, the constant financial pressure has narrowed that focus.

“First and exclusively, it is now on ‘How do we create efficiency by replacing people and really bring cost levels down because rates are still extremely low and margins really tight,’” says Bart De Muynck, a former Gartner research analyst covering the visibility and supply chain tech space, and now principal at consulting firm Bart De Muynck LLC.

Most industry operators he’s spoken with have looked at AI. One example he cites as ripe for transformation is freight brokerages, “where you have rows and rows of people on the phone.” They are asking the question “Which of these processes or activities can we do with AI?”

Yet De Muynck points to one issue that is proving to be a roadblock to change and transformation. “For many of these companies, their foundational technology is still on older architectural platforms,’’ in some cases proprietary ones, he notes. “It’s hard to combine AI with those.” And because of years of low margins and cash flow restrictions, “they have not been able to replace their core ERP [enterprise resource planning system] or the TMS for that carrier or broker, so they are still running on very old tech.”

For those players, De Muynck says they will discover a disconcerting reality: the difficulty of trying to apply AI on a platform that is decades old. “That will yield some efficiencies, but those will be short term and limited in terms of replacing manual tasks,” he says.

The larger question, De Muynck says, is “How do you reinvent your company to become more successful? How do we create applications and processes that are based on the new architecture so there is a big [transformative] lift and shift [and so we can implement and deploy foundational pieces fairly quickly]? Then with those solutions build something with AI that is truly transformational and effective.” And, he adds, bring the workforce along successfully in the process.

“People have some things in their jobs they have to do 100 times a day,” often a menial or boring task, De Muynck adds. “AI can automate or streamline those tasks in such a way that it improves the employee’s work experience and job satisfaction, while driving efficiencies. [Rather than eliminate a position], brokers can redirect worker time to more higher-value, complex tasks that need human input, intuition, and leadership.”

“With logistics, you cannot take people completely out of the equation,” he emphasizes. “[The best AI solutions] will be a human paired up with an intelligent AI agent. It will be a combination of people [and their tribal knowledge and institutional experience] and technology,” he predicts.

EYES OPEN

Shippers, truckers, and 3PLs are experiencing an awakening around the possibilities of technologies today and what modern architecture, in-the-cloud platforms, and AI-powered agents can do, says Ann Marie Jonkman, vice president–industry advisory for software firm Blue Yonder. For many, the hardest decision is where to start. It can be overwhelming, particularly in a market environment shaped by chaos, uncertainty, and disruption, where surviving every week sometimes seems a challenge in itself.

“First understand and be clear about what you want to achieve and the problems you want to solve” with a tech strategy, she advises. “Pick two or three issues and develop clear, defined use cases for each. Look at the biggest disruptions—where are the leakages occurring and how do I start?”

Among the most frequently targeted areas of investment she sees are companies putting capital and resources into broad areas of automation, not just physical activity with robotics, but in business processes, workflows, and operations. It also is about being able to understand tradeoffs, getting ahead of and removing waste, and moving the organization from a reactionary posture to one that’s more proactive and informed, and can leverage what Jonkman calls “decision velocity.” That places a priority on not only connecting the silos, but also on incorporating clean, accurate, and actionable data into one command center or control tower. When built and deployed correctly, such central platforms can provide near-immediate visibility into supply chain health as well as more efficient and accurate management of the end-to-end process.

Those investments in supply chain orchestration not only accelerate and improve decision-making around stock levels, fulfillment, shipping choices, and overall network and partner performance, but also provide the ability to “respond to disruption and get a handle on the data to monitor and predict disruption,” Jonkman adds. It’s tying together the nodes and flows of the supply chain so “fulfillment has the order ready at the right place and the right time [with the right service]” to reduce detention and ensure customer expectations are met.

It is important for companies not to sit on the sidelines, she advises. Get into the technology transformation game in some form. “Just start somewhere,” even if it is a small project, learn and adapt, and then go from there. “It does not need to be perfect. Perfection can be the enemy of success.”

The speed of technology innovation always has been rapid, and the advent of AI and automation is accelerating that even further, observes Jason Brenner, senior vice president of digital portfolio at FedEx. “We see that as an opportunity, rather than a challenge.”

He believes one of the industry’s biggest challenges is turning innovation into adoption, “ensuring new capabilities integrate smoothly into existing operations and deliver value quickly.” Brenner adds that in his view, “innovation is healthy and pushes everyone forward.”

Execution at scale is where the rubber meets the road. “Delivering technology that works reliably across millions of shipments, geographies, and constantly changing conditions requires deep operational integration, massive data sets, and the ability to test solutions in multiple environments,” he says. “That’s where FedEx is uniquely positioned.”

DEFYING AUTOMATION NO MORE

Before the arrival of the newest forms of AI, “there were shipping tasks that had defied automation for decades,” notes Mark Albrecht, vice president of artificial intelligence for freight broker and 3PL C.H. Robinson. “Humans had to do this repetitive, time-consuming—I might even say mind-numbing—yet essential work.”

Application of early forms of AI, such as machine learning tools and algorithms, provided a hint of what was to come. CHR, which has one of the largest in-house IT development groups in the industry, has been using those for a decade.

Large language models and generative AI were the next big leap. “It’s the advent of agentic AI that opens up new possibilities and holds the greatest potential for transformation in the coming year,” Albrecht says, adding, “Agentic AI doesn’t just analyze or generate content; it acts autonomously to achieve goals like a human would. It can apply reasoning and make decisions.”

CHR has built and deployed more than 30 AI agents, Albrecht says. Collectively, they have performed millions of once-manual tasks—and generated significant benefits. “Take email pricing requests. We get over 10,000 of those a day, and people used to open each one, read it, get a quote from our dynamic pricing engine, and send that back to the customer,” he notes. “Now a proprietary AI agent does that—in 32 seconds.”

Another example is load tenders. “It used to take our people upwards of four hours to get to those through a long queue of emails,” he recalls. That work is now done by an AI agent that reads the email subject line, body, and attachments; collects other needed information; and “turns it into an order in our system in 90 seconds,” Albrecht says. He adds that if the email is for 20 orders, “the agent can handle them simultaneously in the same 90 seconds,” whereas a human would have to handle them sequentially.

Time is money for the shipper at every step of the logistics process. So the faster a rate quote is provided, order created, carrier selected, and load appointment scheduled, the greater the benefits to the shipper. “It’s all about speed to market, which whether a retailer or manufacturer, often translates into if you make the sale or keep an assembly line rolling.”

LOOKING AHEAD

Strip away all the hype, and the one tech deliverable that remains table stakes for all logistics providers and their customers are platforms that provide a timely and accurate view into where goods are and with whom, and when they will get to their destination. “First and foremost is real-time visibility that enables customer access to the movement of their product across the supply chain,” says Penske Executive Vice President Mike Medeiros. “Then, getting further upstream and allowing them to be more agile and responsive to disruptions.”

As for AI, “it’s not about replacing [workers]; it’s about pointing them in the right direction and helping [them] get more done in the same amount of time, with a higher level of service and enabling a more satisfying work experience. It’s human capital complemented by AI-powered agents as virtual assistants. We’ve already [started] down that path.”

Data centers are the backbone of today’s digital economy. Every second of uptime, every day of project delivery, directly impacts a client’s bottom line and a contractor’s reputation. The financial stakes are undeniable: a 60MW data center project delayed by just one day can incur an opportunity cost of $500,000. Extend that to a week, and you’re looking at millions in lost revenue or competitive ground. For general contractors, such delays aren’t just bad for business; they can severely damage trust and future opportunities.

In this environment, crafting accurate, realistic, and truly achievable construction schedules isn’t merely a best practice; it’s a strategic imperative.

The Inherent Flaws of Legacy Scheduling

For years, tools like Oracle’s Primavera P6 have been the industry standard for large-scale construction scheduling. They offer power and precision, no doubt. But they are also inherently rigid and complex. Building or modifying a schedule in these traditional systems demands specialized training, effectively centralizing control with a small, specialized group of schedulers or planners. They become the gatekeepers of the entire process.

This siloed approach creates significant blind spots. Data center projects are incredibly complex, requiring seamless integration of mechanical, electrical, structural, and IT infrastructure. Coordination challenges are guaranteed. When only a handful of individuals can genuinely contribute to the master schedule, critical insights from superintendents, subcontractors, or field engineers are inevitably missed.

The outcome? Schedules that appear solid on paper but often fail to align with jobsite realities. Overly optimistic sequencing, misjudged dependencies, and underestimated risk factors invariably lead to costly schedule slippage.

Unlocking Efficiency: The Power of Visual and Collaborative Scheduling

Enter the next generation of scheduling tools: visual, cloud-based, and inherently collaborative platforms designed to make the scheduling process faster, more transparent, and, crucially, more inclusive.

Unlike traditional tools confined to desktop software and static Gantt charts, these modern solutions empower teams to dynamically build and iterate schedules. Tasks, dependencies, and milestones are visually mapped, immediately highlighting potential bottlenecks or opportunities to safely accelerate timelines through parallel work.

More critically, their collaborative nature opens the scheduling process to the entire project team, including field engineers, trade partners, project managers, and even clients. Everyone can review and comment on the schedule in real time, identify potential conflicts proactively, and propose alternatives that genuinely improve efficiency. The result is a plan that is not only more accurate but truly optimized.

A Broader Brain Trust for a Superior Schedule

The principle is straightforward and powerful: collective intelligence builds a better plan.

In a data center project, every discipline brings unique, invaluable expertise. The MEP contractor might see chances for concurrent work in electrical and cooling systems. The structural team could pinpoint a sequencing issue impacting crane utilization. The commissioning manager might realize tasks can start earlier based on equipment readiness.

When these diverse perspectives are integrated into the schedule, the plan becomes far more resilient and efficient. Collaborative scheduling tools make this input practical and structured, without sacrificing control. The visual aspect makes it easier for non-schedulers to engage meaningfully. They can visually grasp how their proposed changes impact the overall timeline.

This democratization of the scheduling process cultivates a culture of ownership and accountability. When every team member understands the plan and has contributed to its formation, project alignment improves dramatically. Miscommunications decline, coordination excels, and the risk of costly delays diminishes significantly.

Intelligent Schedule Compression Through Collaboration

Beyond accuracy, visual scheduling enables intelligent schedule compression. In conventional environments, shortening a project’s duration often happens reactively, after a delay or missed milestone. Collaborative planning, however, identifies optimization opportunities from day one.

Consider overlapping workstreams previously assumed to be sequential, a move that can shave days or weeks off a schedule. Adjusting resource allocations or reordering tasks based on real-world input can yield similar gains. The key difference: these are proactive decisions, informed by those on the ground, rather than reactive ones made under duress.

For data center clients, keen to bring capacity online as quickly as possible, these efficiencies translate directly into competitive advantage. Delivering a project even two weeks early can represent millions of dollars in added value.

Transparency Drives Trust

Transparency is another critical, often underestimated, benefit. With all stakeholders working from the same live schedule, there’s no confusion over versions, no endless email threads with outdated attachments, and no surprises when changes occur. Updates are real-time and visible to everyone with access, including the owner.

This level of openness fosters trust, both internally within the contractor’s organization and externally with the client. Owners appreciate clear visibility into progress and potential risks. Project teams benefit from streamlined communication and reduced rework. In the high-stakes, competitive data center market, trust is a powerful differentiator.

Data-Driven, Continuous Improvement

Modern scheduling platforms also generate rich data on project planning and execution. Over time, this data becomes an indispensable tool for benchmarking performance, identifying recurring bottlenecks, and continuously refining future schedules.

For instance, analytics can reveal typical durations for specific data center construction phases, common deviation points, and which sequencing strategies consistently deliver faster results. Armed with this intelligence, contractors can hone their planning models, becoming far more predictive.

In an industry that prioritizes precision and repeatability, the ability to learn from past projects and apply those lessons to new ones is invaluable.

A New Standard for a Digital Era

The data center market is expanding rapidly, fueled by AI, cloud computing, and insatiable data demands. The pressure on contractors to deliver complex projects quickly and reliably will only intensify. Those adopting modern, collaborative scheduling approaches will gain a decisive edge.

By moving beyond static, specialist-driven scheduling to dynamic, inclusive planning, general contractors can achieve:

• Significantly greater accuracy in project forecasts.
• Shorter construction durations through optimized sequencing.
• Higher team engagement and accountability.
• Enhanced transparency and trust with clients.
• Systematic continuous improvement across all projects.

Legacy scheduling tools have served their purpose, but their limitations are increasingly mismatched with the speed and complexity of today’s data center projects. The future of data center delivery lies in processes and tools that are as connected and intelligent as the facilities themselves.

Conclusion

The message for data center builders is unambiguous: planning differently is no longer optional. Visual, collaborative scheduling isn’t just a technological upgrade; it’s a fundamental mindset shift that transforms scheduling into a shared, strategic advantage. When the entire project team can see, understand, and shape the plan, they can build faster, smarter, and with far greater confidence. And in a world where every day of delay can cost half a million dollars, that’s not just progress, it’s significant profit.

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About the Author:

Phil Carpenter is the Chief Marketing Officer of Planera, a construction tech startup revolutionizing project management with its visual, Critical Path Method (CPM)-based scheduling and planning software. For more information, visit www.planera.io.

The post Building Data Centers Faster and Smarter: Visual, Collaborative Scheduling Isn’t Just an Option—It’s a Business Mandate. appeared first on Data Center POST.