Digital Readiness

The Enablement Layer

In Article 5, we explored how operational metrics can obscure customer reality—the gap between green dashboards and red customers. That dimension asked whether the delivery model enables the business it serves.

This final dimension asks a deeper question: what enables everything else to work?

Technology and AI are not a fourth location option alongside onshore, nearshore, and offshore. They are the enablement layer beneath every location decision, operating model choice, and workforce configuration. Get the foundation wrong and every other dimension suffers. Get it right and technology amplifies the value of decisions made across all five preceding dimensions.

Organizations that understand this are building something different: operations where automation handles volume, humans handle judgment, and data enables decisions instead of requiring detective work.

The Readiness Gap

The logistics industry has made real progress. Roughly sixty percent of freight forwarders now use cloud solutions. Digital booking platforms handle more than half of air and ocean freight transactions.

But digitization is not the same as AI readiness.

Having a modern TMS does not mean you are prepared for generative AI. Digital bookings do not mean your data can train a machine-learning model. Organizations routinely conflate these capabilities—and the result is expensive disappointment.

The evidence is consistent. Ninety-four percent of 3PLs cite AI as the most impactful future technology. Yet only twenty-five percent have implemented RPA, the foundational layer beneath advanced AI. Forty-two percent abandoned AI pilots in 2024, up from seventeen percent the year before.

High intent. Uneven execution.

The stakes are material. Enterprise logistics automation initiatives routinely require six- to seven-figure investments. When they fail, the cost extends beyond wasted spend to delayed competitiveness, workforce disruption, and—most damaging—organizational skepticism. An organization that fails at automation once often becomes one that avoids it forever.

By contrast, a Fortune 500 logistics firm achieved twenty-five percent faster delivery times, a twenty-two percent reduction in transportation costs, and a two-hundred-percent ROI within two years. The difference was not technology. It was readiness.

The Core Question

Are you prepared to integrate the tools that will shape the next decade?

This is not a platform question. It is a readiness question: whether your organization has the foundation, governance, adaptability, and workforce alignment to absorb technology in ways that create durable value.

Five diagnostic questions reveal whether you are positioned—or exposed.

Question 1: Have You Embedded Automation at Scale—or Are You Still Piloting?

Pilots prove concepts. They show that bots can extract data and workflows can trigger. Pilots are necessary.

What should concern you is when pilots never scale.

The regional office that automated three workflows while others remain manual. The proof-of-concept that is still a proof-of-concept two years later. The automation team reporting bot counts while leaders cannot quantify cost or quality impact.

Automation at scale means it operates inside standard workflows, not alongside them. Exception handling is designed, not improvised. Automation runs consistently across regions, not as isolated experiments.

The difference is rarely the technology. It is whether the bestshoring question—where work lives and who owns it—was answered first.

Indicator: Can you measure automation coverage as a percentage of eligible transactions, or do you count bots?

Question 2: Can Your Systems Actually Talk to Each Other?

Legacy systems are not the issue. Incompatible systems are.

According to Gartner, seventy-six percent of supply chain organizations struggle with master data quality. Only thirty-four percent report seamless data flow between IT and operational systems. Without integration, the data required for AI simply does not exist in usable form.

Freight forwarders and 3PLs feel this acutely: multiple TMS platforms, WMS instances, and carrier connections. The question is not age—it is connectivity.

Indicator: Is integration capability a primary buying criterion, or an afterthought?

Question 3: Do Your People See AI as a Threat—or a Tool?

Framing matters more than technology.

Replacement thinking asks which jobs can be eliminated—and triggers resistance. Deloitte found that seventy-two percent of failed logistics AI initiatives cited workforce resistance as the primary cause.

Augmentation thinking asks what people can do when routine work is automated. McKinsey found that companies allocating fifteen percent of AI budgets to training achieved three times higher adoption and ROI.

Generative and agentic AI are advancing quickly. But governance questions must be answered now: where is autonomy allowed, and where is human judgment required?

Indicator: Does your workforce understand how roles will evolve—or just that “technology is coming”?

Question 4: Could You Explain to a Regulator Why Your AI Made That Decision?

AI governance is where most organizations are least prepared.

AI-managed supply chains saw forty-seven percent more cyberattack attempts in 2024. Regulatory expectations around explainability and accountability are tightening.

Consider customs automation. AI can accelerate HS code assignment—but if training data is flawed or decisions are not explainable, compliance risk scales instantly.

Governance must ensure decisions are auditable, defensible, and accountable.

Indicator: Do you have documented AI data and accountability policies—or is governance deferred to legal later?

Question 5: Have You Modeled What Automation Does to Your Location Strategy?

Automation changes everything.

As transactions are automated, remaining work shifts to judgment and exception handling. As AI drafts communications, humans manage relationships. Cost arbitrage that once justified offshore models may erode, while proximity and expertise become more valuable.

Forty percent of workers will need reskilling by 2030. Sixty percent of organizations are already using outsourcing partners to accelerate AI adoption.

Indicator: Has leadership modeled how automation reshapes headcount, skills, and location economics over the next three to five years?

The Sequencing Trap

Most technology strategies are built backward.

Platforms are selected first. Integration problems emerge. Operations are consulted late. Adoption stalls.

You cannot automate your way out of a broken operating model. Technology amplifies whatever exists—good or bad.

Successful organizations treat technology as the sixth dimension, not the first. They align operating models, talent, governance, and customer outcomes before deploying tools.

For Organizations Just Beginning

Not everyone operates at the frontier—and that is acceptable if approached deliberately.

Start with foundation, not ambition. Standardize data. Automate basic processes. Focus on areas with clean ownership and visible value. Quick wins build momentum. Momentum builds capability.

The barrier is no longer access to technology. It is operational clarity.

The Vision Worth Building

Technology is not the strategy. It enables the strategy.

The real question is whether your operating model is ready to be enabled. That readiness depends on clarity about where work lives, confidence in governance, alignment between workforce planning and technology, and a direct connection between what you measure and what customers experience.

Organizations that can answer the five diagnostic questions with evidence—not aspiration—will capture real value. Those that cannot will see technology underperform, not because it failed, but because the organization was unprepared.

The Six Dimensions of Bestshoring Readiness function as a system. Technology readiness is the enablement layer—necessary, but never sufficient alone.

The good news: readiness is buildable. The path is knowable. And the organizations that move first will not look back.