The Price of Progress: When Speed Becomes the Problem
Why skipping people, process, and patience costs more than you save.
The Rhythm of Progress
Progress always begins as a promise.
Every few decades, a new technology emerges claiming it will finally free us from inefficiency — that this time, business will be easier, faster, and smarter. The personal computer promised liberation from paper. The internet promised global reach. Cloud computing promised flexibility. And now, Artificial Intelligence promises intelligence itself — the power to automate not just what we do, but how we decide.
Each innovation begins as a breakthrough and ends as a balancing act — between the efficiency it enables and the complexity it multiplies.
The pattern is familiar: technology accelerates faster than our capacity to absorb it. We move quickly, but not necessarily forward.
AI is simply the latest expression of that rhythm — faster, smarter, but still vulnerable to the same human tendencies: impatience, overconfidence, and the illusion that speed equals progress.
Evolution of Promise
Every generation of technology has promised liberation.
The mainframe organized information.
The internet connected people.
Cloud computing distributed power.
Automation optimized process. And now,
Artificial Intelligence extends capability — from doing the work to deciding how it should be done.
Each innovation pushed us one layer further from the manual toward the cognitive — from managing effort to managing intent. Yet with every leap in speed and convenience came a hidden cost: we lost a bit more of the patience, discipline, and reflection that once made earlier systems succeed.
When Progress Feels Like Acceleration
n the boardroom, speed has become synonymous with strategy. Every discussion circles back to “faster” — faster deployment, faster ROI, faster innovation. The pressure to show results fuels a culture that values acceleration over alignment.
But as any transformation veteran will tell you, speed without structure compounds risk.
A recent McKinsey & Company survey found that 71% of organizations report using generative AI, but fewer than 5% have seen significant EBIT impact from it.¹ Similarly, Gartner projects that up to 30% of AI initiatives will be abandoned after pilot due to poor alignment and governance.²
The problem isn’t technology — it’s tempo. We treat adoption like a sprint when it’s really a relay — one that requires coordination, trust, and patience.
As one technology executive summarized it: “Technology doesn’t fail. People fail to design for people.”
The Progress Paradox
The history of innovation is a study in unintended consequences.
Computers made us faster, but they also made us less patient.
Databases gave us structure, but buried us in data.
ERPs unified operations, but fractured ownership.
CRMs connected customers, but diluted relationships.
Digital transformation modernized systems, but exhausted teams.
Now AI promises autonomy — and risks automating judgment itself.
We keep mistaking acceleration for advancement, mistaking convenience for capability. The result is a widening gap between the pace of technology and the maturity of the organizations trying to wield it.
The paradox is that AI isn’t the revolution — it’s the recursion. It’s another round in our ongoing attempt to outrun the work of alignment and validation.
The Hidden Cost of Speed
Every organization that has ever rushed transformation has paid the same price: hidden debt.
At first, speed feels like efficiency — until cracks appear. Governance corners get cut. Processes are automated before they’re optimized. People are asked to trust systems they barely understand.
These shortcuts accumulate into long-term liabilities:
Security debt: Deploying AI without governance creates compliance and reputational risk.
Process debt: Automating flawed workflows multiplies inefficiency.
People debt: Moving faster than culture can adapt fuels disengagement and burnout.
Deloitte’s research into failed digital transformations shows that more than 70% underperform expectations, not because of technology gaps but because of “a failure to align strategy, culture, and capability.”³
Speed amplifies both brilliance and dysfunction. What you automate, you multiply.
The Automation Loop
Every major technology wave follows the same four stages:
Overpromise: “This will change everything.”
Oversimplify: “Just implement it.”
Overload: “Why is this so complicated?”
Overcorrect: “Bring in consultants. Reorg. Retrain.”
This creates a familiar loop — automation chasing its own tail.
We saw it with ERP in the 1990s, CRM in the 2000s, and digital transformation in the 2010s. Now it’s happening again with AI — only this time, the cycle moves at exponential speed.
AI doesn’t create chaos. It accelerates whatever’s already there.
The Human Equation
Technology has always been a mirror. It reflects the systems, cultures, and decision-making habits that define an organization.
When companies adopt AI, they’re not just implementing code — they’re exposing culture. Who owns the data? Who defines success? Who can question the outcome?
In a 2023 working paper, researchers Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that access to a generative-AI assistant increased the productivity of 5,179 customer-support agents by 14% on average, with novices improving by up to 34%.⁴
The authors note: “Access to generative AI can increase productivity, particularly for less-experienced workers, but effects vary widely across skill levels.”
That’s the paradox of convenience: as machines become better at prediction, people can become worse at reflection.
The future won’t reward those who use AI most — only those who use it wisely.
Organizational Blind Spots
For leaders, the real challenge isn’t technical — it’s human.
In most AI projects, the technology works. The integration works. What fails is adoption. A recent industry analysis by IDC found that over 50% of AI pilots never scale to production, primarily due to lack of clear ownership and change management.⁵
Field Service Management tools reveal this pattern clearly. They promise predictive maintenance and real-time optimization, yet fewer than half deliver measurable ROI within 18 months.⁶ The issue isn’t code — it’s commitment. Teams don’t trust tools they didn’t help design.
The companies that succeed treat technology not as an imposition but as an invitation — to rethink how decisions are made, communicated, and owned.
“You can’t automate your way out of misalignment.”
The Industry Mirror
Few sectors illustrate this better than energy and industrial services — industries defined by complexity, regulation, and long-term capital cycles.
Many energy executives view AI as the key to modernization, yet progress remains constrained by data governance, infrastructure, and trust. According to a 2024 PwC Energy report, 40% of firms cite data quality as their top barrier to scaling AI.⁷ Others struggle with the compute demands that strain the very grids AI aims to optimize.
Caution here isn’t resistance; it’s responsibility. The companies that endure are those that validate before they automate — balancing acceleration with assurance, and ambition with accountability.
The Humanity in the Machine
Automation removes friction, but friction is where meaning lives. Those pauses — where people debate, decide, and reflect — are what make progress human.
When we remove friction, we remove reflection. And when we remove reflection, we remove trust.
AI is most powerful when it augments human judgment, not replaces it. Studies continue to show that companies using AI to enhance human skill outperform those using it as substitution. Technology amplifies what already exists — competence or confusion, clarity or chaos.
The question isn’t what AI can do — it’s what we’re ready to do with it.
Progress With Purpose
Every era of transformation reaches a reckoning point. AI is ours.
It won’t break organizations — it will reveal them. It will expose where trust is weak, where processes are brittle, and where leadership has confused movement with momentum.
The answer isn’t to slow down; it’s to move with intention. Validate before automating. Build capacity before capability.
At AI Advisory Group, we call this Decision Validation — aligning people, process, and technology so every initiative is both right and ready. It’s not about resisting innovation; it’s about ensuring progress has purpose.
The future won’t belong to the fastest companies. It will belong to those that learn, align, and last.
Because speed may win the race — but validation wins the decade.
References
McKinsey & Company. The State of AI in 2024: Generative AI’s Second Year. August 2024.
Gartner. Forecast: Generative AI Initiatives, 2024–2027. September 2024.
Deloitte Insights. Digital Transformation 2024: Bridging Strategy and Execution. February 2024.
Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. NBER Working Paper No. 31161.
IDC Research. The Reality of AI Deployment: Barriers to Production. June 2024.
Service Council. Field Service Transformation Trends 2024. March 2024.
PwC. Energy Industry Outlook 2024: Building Digital Resilience. April 2024.
Author Note
Some portions of this article may include AI-generated text or insights derived through AI-assisted research. Information was gathered from a variety of reputable sources, including news outlets, media organizations, and publicly available reports.
The views and interpretations expressed here are solely those of Christopher Donaleski and do not necessarily represent the positions of any organizations or partners referenced. While every effort has been made to ensure accuracy, any factual errors or misinterpretations will be promptly corrected upon identification.
