Outlook 2025: Five Hard Realities for CSCOs in the Late Twenties

Stocks are close to all-time highs as we get ready to close the first quarter of the twenty-first century. For some, it feels like the Roaring Twenties are back, with modern-day Gatsbys and a party that never ends. For many, however, a sense that trouble lies ahead hangs heavy in the air.??

Leaders must walk a fine line between high expectations for AI and the inevitable stresses of radical change.? Here are five hard realities supply chain executives face in the next few years, with suggestions to navigate choppy waters.?

Tariffs

The benefits of regionalization and diversifying away from China are obvious, plus new tariff threats are arising everywhere from Mexico to Vietnam.?Add to this intense geopolitical rivalry driven by Xi Jinping and a BRICS currency to challenge the US dollar, and it seems we’ve moved past the point where common sense prevails.?

Resilience and proactive compliance will not be enough. Supply chain leaders should engage with politics, policy, and the details of economic development as part of supply network investments. Making deals based on political calculus instead of just economics is the way to win in the late twenties.?

Cost Reduction

Supply chain has always been about cost, but the pressure is getting worse. Two macro factors will squeeze costs in every industry. The first is AI. Investors’ productivity expectations are huge. Zero100 analysis of earnings call transcripts reveals a 2x jump in mentions of AI over the past 24 months, showing that the original ChatGPT moment was no flash in the pan.?

The second is inflation, which could heat up if trade wars worsen. Consumers have plenty of choices, with e-commerce now comprising 16.2% of US retail sales (even more in the UK and Korea), so passing along costs doesn’t fly.??

Supply chains will be expected to preserve margins without raising prices. Look for AI use cases that are narrowly scoped and can deliver cost savings fast.?

Cybersecurity

AI and geopolitical tension are a perfect storm situation for cybersecurity. The riches promised by AI are irresistible, and learning curves are so steep that most business leaders are moving as fast as possible.?

Meanwhile, the incentives for criminals to steal data or plant ransomware are higher than ever. The worry is AI’s voracious appetite for data crashing into a crisis of confidence among consumers, business partners, and suppliers who provide that data. If the system can’t be trusted, data dries up, and AI can’t work.??

An old sharpshooter saying applies here: “Slow is smooth, smooth is fast.” In other words, start AI small and apply rigorous system, process, and data standards to harden security before scaling. Corporate IT will appreciate it, and your team will better understand how cybersecurity works and why it matters.?

Climate Change

COP29 was a failure with nearly all the effort spent trying to put a price tag on how much the developed world owes everyone else. Meanwhile, earth science news says that the problem is getting worse faster than was originally forecast. Our heated political environment is entrenching this unproductive death spiral of admiring the problem.?

Supply chain leaders have not taken their eyes off the ball. Most have Scope 3 carbon goals and metrics in place, while nearly all are on top of Scope 1 and 2 reductions.? In fact, the top quartile of digitally enabled supply chains are 15% ahead of SBTi targets. Now is not the time to stop decarbonizing.?

Instead, reframe the discussion around operational data measuring where supply chain has a direct effect. This includes sourcing, transportation, and production but excludes consumer use and recycling.?

Jobs

Reskilling is real, and a classic study of ATMs' effect on bank tellers is instructive, but redundancies are inevitable. Human-machine teams are the best path to exploiting AI in supply chain, and many automation investments (UPS’ warehouse strategy) come with pay raises for some. In the end, however, headcount reductions are an essential part of the AI transition.?

2025 is the right time to rethink ROI on employees. The “war for talent” a few years back had some companies overpaying data scientists who they couldn’t retain. Budgeting for full-time hires should be balanced with gig workers, contractors, and internal talent marketplaces.??

The twenty-first century is in full swing. It’s time to get real about what that means.??