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    AI Implementation Ahead. Proceed with Caution.

    Written by: Thomas Deakins
    EVP, Alliances, Redwood Logistics 
    Adjunct Professor and Lecturer, Haslam College of Business at the University of Tennessee, Department of Supply Chain

    We are in the middle of one of the biggest hype cycles I’ve seen in my career.
    Agentic AI. Generative AI. Autonomous decisioning. AI copilots everywhere.
    The excitement is real. The opportunity is real.

    But so is the risk.

    I am bullish on AI. I believe it can be a massive competitive advantage. But I also believe we need to approach it with discipline, not like the Wild West.

    Because if we don’t, history will repeat itself.

    This Feels a Lot Like the Dot-Com Era

     If you were in business during the dot-com boom, you remember the rush:

    • "We need a website."

    • "We need e-commerce."

    • "We need to digitize everything."

    Adoption happened fast. Money flowed even faster.

    And then came buyer’s remorse.

    Why?

    Because buyers and sellers weren’t aligned.

    They didn’t agree on:

    • The inputs required

    • The outcomes expected

    • The operational impact

    • The ROI model

     Sound familiar?

     Today, AI is walking the same tightrope.

    AI Is Powerful — But It’s Not Magic

    Too many organizations are asking:

    “How do we use AI?”

    Instead of asking:

    “Where specifically will AI improve our business, and how will we measure success?”

    Successful AI adoption requires far more than a shiny demo or a compelling pitch deck.

    It requires discipline.

    What Must Be in Place Before You Scale AI

    Here's the uncomfortable reality:

    1. A Clear Strategy — Not Experiments Without Direction 

    AI must align to business priorities.

    • What are the top 3–5 areas of improvement?

    • What KPIs will change?

    • What does success look like in 6, 12, 24 months?

    If leadership cannot clearly articulate this, pause.

    AI is not a strategy.
    It is an enabler of a strategy.

    2. Defined Outcomes and Measurable Results

    You must define:

    • The business problem

    • The baseline performance

    • The expected lift

    • The financial impact

    Without this, you’re not implementing AI — you’re funding curiosity.

    And curiosity without accountability becomes waste.

    3. Clean Data + Master Data Management Strategy (Garbage In = Garbage Out)

    AI does not fix bad data.

    It amplifies it.

    If your data is fragmented, inconsistent, or poorly governed, AI will simply accelerate poor decisions at scale.

    You need:

    • Clean, standardized data

    • A defined Master Data Management (MDM) strategy

    • Governance ownership

    • Clear accountability

     Otherwise, you are building intelligence on a broken foundation.

    4. Signed-Off Change Management Plan

    Technology implementation is easy.

    People transformation is hard.

    AI impacts workflows, reporting lines, performance metrics, and accountability.

    You need:

    • A formal change management plan

    • Executive sponsorship

    • Clearly assigned deliverables

    • Communication cadence

    • Defined adoption milestones 

    Without this, adoption stalls.

    And stalled adoption kills ROI.

    5. Workforce Strategy — Not Just Technology  

    AI will change roles.

    Some tasks will disappear.
    New skills will be required.
    Talent will shift.

    Organizations must answer:

    •  What new skills do we need?

    • What talent must we hire?

    • Who do we reskill?

    • How do we protect morale?

    If employees feel AI is being “done to them” instead of “done with them,” morale drops. Culture suffers. Productivity declines.

    That’s not innovation. That’s disruption without direction.

    The Real Risk Isn’t AI — It’s Misalignment

    The biggest danger isn’t the technology.

    It’s misalignment between:

    • Leadership and IT

    • Buyer and seller

    • Vision and execution

    • Expectations and deliverables 

     When that gap widens, AI becomes:

    • An expensive experiment

    • A drain on resources

    • A source of frustration

    • A culture disruptor

     And in the worst case — a competitive disadvantage.

    The Competitive Advantage Is Real — If You Earn It

     AI can drive:

    • Faster decision-making

    • Increased efficiency

    • New revenue streams

    • Better customer experiences

    • Smarter operations

    But only if the foundation is built correctly.

    If strategy, governance, data, workforce planning, and change management are aligned, AI becomes a force multiplier.

    If not, it becomes just another failed transformation initiative.

    My Perspective

    I’m excited about Agentic AI and Generative AI.

    I believe they will reshape industries.

    But I also believe:

    • Move fast — but not recklessly.

    • Innovate — but with structure.

    • Experiment — but with measurable goals.

    The companies that win won’t be the ones who move first.

    They’ll be the ones who move strategically.

    I’m curious:

    • Are you seeing structured AI adoption — or reactive implementation?

    • What guardrails has your organization put in place?

    • Where have you seen AI succeed — or fail?