News June 9, 2026

Enabling AI the Right Way: Why AI Readiness is the Secret to Supply Chain Transformation

By Simon Dubuc, Managing Director

Updated: June 15, 2026 | 6 min read

The novelty of artificial intelligence has worn off. While advanced tools are more accessible than ever, many organizations remain stuck. They launch high-profile initiatives only to watch them stall in the pilot phase.

The issue isn’t the technology itself but moreso it’s how we apply it.

Too many companies drop AI directly into legacy processes and wonder why nothing changes. To understand this roadblock, we have to look back at another massive technological shift: the invention of electricity.

The Electricity Paradigm: New Tools, Old Workflows

When factories first transitioned from steam power to electricity, plant managers swapped out steam engines for electric motors but left the physical layout of the factory completely unchanged. Because the workflow didn’t evolve, productivity stagnated for nearly 30 years. True economic growth only happened when architects completely redesigned factories around the decentralized capabilities of electric power.

Today, we are repeating history. Dropping AI into a fundamentally flawed supply chain process doesn’t fix the problem; it simply automates and amplifies inefficiency. For an AI deployment to thrive, the underlying workflow must be intentionally redesigned.

Mind the Execution Gap

The disconnect between corporate ambition and operational reality has created a massive execution gap across leadership teams:

88% of business leaders believe their data maturity supports AI at scale.
95% of Generative AI pilots fail to deliver measurable P&L impact.
While 14% of organizations use AI in at least one function, only 6% qualify as true AI high performers.

Organizations typically stall due to fragmented data, trouble pinpointing the right operational use cases, user anxiety, and execution paralysis from balancing too many competing tools without a roadmap.

Understanding the AI Maturity Curve

Before launching an initiative, you must map out your intended operational destination. AI adoption progresses through three distinct stages, shifting from human execution to autonomous action:

Generative AI (Human-Led): The AI assists by creating content or surfacing insights, but all decisions and actions remain with the operator (e.g., suggesting reorder quantities that a planner must manually approve).
Prescriptive AI (AI-Led, Human-Validated): The AI generates predictions and decisions, executing routine tasks autonomously while flagging anomalies for human validation.
Agentic AI (AI-Led & Validated): The AI operates with full operational autonomy, using built-in validation protocols to handle edge cases and escalating to management only when major thresholds are breached.

The Four Pillars of AI Readiness

True AI Readiness is an organization’s holistic ability to effectively adopt, deploy, and scale these solutions. Achieving it requires aligning four core pillars:

Data Readiness: High-quality, structured, and accessible data that fuels AI and automates complex business rules without requiring massive corporate data lakes.
Infrastructure: Knowing your current systems and platforms. AI integration should leverage your existing landscape—no immediate, massive overhaul required.
Talent & Skills: Building internal AI literacy across departments, ensuring everyone from leadership to end users understands and trusts the tools.
Strategy & Leadership: Securing executive sponsorship, establishing clear governance, and setting AI-aligned business objectives.

How to Get Started: A Stepped Methodology

You don’t need a company-wide overhaul to begin. Winning organizations focus on building momentum through a structured, iterative methodology where each success compresses the time and cost of the next project.

Step 1: Identify AI Candidates: Target high-friction, decision-heavy pain points and evaluate them for business value and technical feasibility.
Step 2: Validate Readiness: Confirm you have the right data components to achieve your specific objectives. Right-size the scope—sales AI readiness looks different than supply chain AI readiness.
Step 3: Select & Pilot: Start with the outcome and work backward. Align a specific technology solution to your targeted use case and launch a focused pilot.
Step 4: Iterate & Scale: Measure impact against operational baselines, build user trust, and roll out the solution in managed waves under a standardized governance framework.

The Cost of Waiting

The hype cycle is over: AI readiness is no longer a vague future project, but a pressing competitive necessity. Organizations that delay risk falling behind faster adopters and facing a talent drain as AI fluency becomes a baseline expectation for modern professionals.

Don’t wait for a perfect corporate layout. Identify your high-friction bottlenecks, assess your readiness pillars, and launch a targeted pilot to create value where it matters most.

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