We’re Not Ready for This
Wayne and Garth had us laughing out loud to their famous skit from Wayne’s World that made it to SNL. That brings back some great memories. But when it comes to the speed and adoption of AI, it’s not a factor of worthiness, but readiness. We’re not ready.
As a society, we are clearly not ready for the speed of AI enhancements and this new industrial revolution.
Every industrial revolution previous to this one, the replacing technologies were several factors above the initial cost of labor that it was replacing. The steam engine 50x more expensive, autos 25x more expensive than a horse, even the smartphone was 3x higher than a typical flip phone. But Generative AI is often cheaper than the labor it replaces.
It’s not just the cost that is different, the data reveals something unprecedented: AI is being adopted 23x faster than steam power while simultaneously costing as much as 1/5th the cost of the human labor it replaces. This breaks every historical pattern of industrial transformation—and it’s creating a cultural divide in society and in retail organizations that’s impossible to ignore.
Throughout my years 36 years of analyzing retail technology adoption, I’ve observed that successful implementations typically follow predictable timelines. Before my time, organizations had decades to prepare for electricity, years to integrate computers, and reasonable runway to deploy internet infrastructure. Each revolution provided breathing room for workforce adaptation and organizational change.
That luxury no longer exists.
Further, the speed of improvement in AI technologies is happening at a breakneck pace. In January alone we had a 9,000% increase in the value curve of utility per dollar. And currently, the AI’s ability to run autonomy is doubling every 4 months (since Jan 2024), and the cost per token is dropping in half every 9 months. And none of us are ready.
How does this apply to Retail?
Here’s what the above data doesn’t show: the stark difference in AI reception based on company performance. I’ve witnessed this pattern repeatedly—financial health determines whether AI is viewed as an opportunity or a threat.
When retailers are experiencing growth, AI adoption follows an entirely different trajectory. Teams embrace automation for inventory management, celebrate predictive analytics and personalization that boost conversion rates, and champion chatbots that handle customer service, particularly in peak seasons. AI becomes the hero technology that amplifies success, and the team enthusiastically accepts the technology as a partner and force multiplier.
But when revenues are declining, the exact same AI capabilities trigger defensive responses. Staff interpret automation initiatives as cost-cutting measures. Predictive analytics become tools for identifying underperformers. Customer service bots represent the first step toward human workforce reduction.
The technology remains identical. The reception transforms completely. And this creates a feedback loop. Successful companies adopt AI faster because their teams embrace it, which accelerates their competitive advantage. Struggling companies adopt AI slower due to internal resistance, widening the performance gap.
It is still Crawl, Walk, Run – Just Faster
As well, retailers are in a crawl, walk, run process and everyone feels like they are behind. RAND Corporation research suggests that 80% of AI projects fail entirely, with less than 30% moving past pilot stage (According to Gartner). After reviewing hundreds of AI implementations, they suggest the pattern becomes clear—technical capability isn’t the limiting factor.
The five leading root causes of failure tell the real story:
- Stakeholders misunderstand what problem needs solving
- Insufficient data to train models effectively
- Organizations chase technology trends rather than solving real problems
- Lack of infrastructure to clean and manage data
- Technology applied to problems too complex for current AI capabilities
Notice what’s missing from this list? Technical sophistication. The failures happen at the organizational preparation level, not the implementation level.
It’s not just that the financial condition of the retailer impacts adoption, that reality also impacts how failure is perceived in the organizations. High performers take the Thomas Edison approach, “We now know what doesn’t work, let’s try another way” where others often resign to an attitude of “We tried that already, it didn’t work.” That experience is critical.
Our research data shows it clearly. Those who had the scrapes and bruises early through the use of AI/ML are 35x more likely to see significant double-digit gains when Generative AI was added to the workflow.
Enter ClearSight AI: The Readiness Assessment Revolution
This is where IHL Group’s ClearSight AI framework becomes essential. Rather than diving straight into AI deployment, we jump into the messiness that describes most retail organizations. What we see is that to be successful, most companies have to have a “Come to Jesus” review of their operations to know where to take the next step. ClearSight helps address the fundamental questions: “Are we actually ready for this and what do we need to do to be ready?”
The methodology evaluates 7 critical dimensions before projects are invested in:
Legal Compliance: Does regulatory framework permit AI usage? Privacy laws, financial regulations, and trade secret protections can kill projects before they start.
Data Readiness: Beyond having data, is it clean, tagged, and structured for AI consumption? Most retailers discover their data quality issues only after expensive implementation begins.
Data Enablers: Which data, if cleaned, tagged and structured brings the greatest value to the company.
Systems Integration: Can existing infrastructure handle AI outputs? The most sophisticated algorithm is worthless if systems can’t act on its insights.
Skills Assessment: Do internal teams have necessary capabilities, or can external expertise be secured? Skills gaps identified early prevent mid-project failures.
Risk Evaluation: Does the project reduce organizational risk or create new vulnerabilities? Risk-reward calculations change dramatically when done upfront versus mid-implementation.
Business Value Quantification: Is the use case significant enough to justify AI investment? Strategic importance matters more than technical feasibility.
Overcommunication from Management is Critical
There are two things that impact every retailer’s adoption of AI. #1 – Quality of the data and #2 Management must overcommunicate the plans for and intentions of leveraging AI technologies. Culture has killed many a tech project. As AI is non-negotiable for most retailers. If the culture tries to kill the use of AI and is successful it is a rare retailer that will survive this next decade. The accelerated improvement of competitors is simply too much of an advantage.
The Velocity Paradox
Here’s the uncomfortable truth: the companies that most need AI’s efficiency gains are often least positioned to adopt it successfully. Financial stress creates exactly the cultural conditions that impede technological transformation. Meanwhile, already-successful retailers are leveraging AI to extend their advantages. They have the cultural capital to experiment, the financial runway to iterate, and the team psychology that embraces enhancement over replacement. This creates an acceleration paradox where technology designed to democratize capability actually amplifies existing performance gaps.
Looking Forward: Companies Must Adapt and Fast
We’re witnessing the first industrial revolution where technology adoption velocity exceeds human institutional capacity for change. Success won’t depend solely on technical implementation—it will require new frameworks for managing organizational psychology at unprecedented speeds.
The companies that thrive will be those that understand AI adoption as fundamentally a cultural challenge with technical components, not the reverse. They’ll invest in financial stability and team confidence as prerequisites for technological transformation. And they will choose an AI strategy that only supports business strategy.
The question isn’t whether AI will transform retail operations. It is whether organizations can create the cultural and infrastructure conditions necessary to harness this transformation constructively—regardless of their current financial position.
In this era, organizational psychology becomes a competitive advantage as important as technological capability. The retailers who recognize this early, and use frameworks like ClearSight AI to prepare systematically, will accelerate their growth and define the next decade of industry leadership.
And one final thought, it’s obvious as well that this has a personal component for all of us. Where are you with your AI skills? How are your kids adapting? I say that not to scare you, but to just make sure you are investing the time to learn. The overwhelming majority will be slow to adapt. Don’t be one of those.
What do you think?