It’s no surprise that Artificial Intelligence (AI) is taking the world by storm. Between its ability to create music, pass the Bar exam, and provide questionable relationship advice, it's only natural to think of it as a revolutionary force, bursting onto the scene to change the world as we know it. But while AI’s impact is certainly dramatic, it’s far from an overnight sensation. It’s the acceleration of an evolution that’s been unfolding quietly for years.
That distinction matters, especially for the business leaders everywhere asking the same question: “How should we actually use AI?” Like any major shift, the path forward becomes clearer when you understand the road behind it. AI’s true value comes from grasping not only what the technology is capable of today, but how it evolved to this point and what that evolution means for your organization. Ultimately, the successful adoption of AI won’t depend on how accurately we predict the future, but on how deeply we understand our own teams, our existing processes, and the human systems that technology is meant to enhance.
An evolution, not a revolution
First things first: Calling AI “evolutionary” doesn’t mean slow. The pace of change we’re seeing now is breathtaking. But today’s breakthroughs didn’t come out of nowhere; they build on decades of work across multiple fields.
Modern AI grows out of decades of work in statistical natural language processing. For more than 30 years, researchers have been experimenting with ways to model and predict language mathematically: studying how often words appear together, flagging anomalies, and developing the foundations of early spam filters or predictive text. These efforts may look simple compared to modern chatbots, but they established the crucial data structures and algorithms for reliably capturing the meaning of language as numerical data.
At around the same time, neural networks were quietly making strides by solving messy, real-world problems like reading handwritten digits for postal systems, filtering junk email, and counting cars in satellite images - problems that traditional rule-based software couldn’t handle. Neural networks proved that if machines are fed enough examples, they can generalize beyond rigid programming and adapt to ambiguity. That flexibility is the same principle that allows modern AI systems to handle unstructured documents, images, and text today.
The final ingredient comes from an unexpected source: video games. Graphics Processing Units (GPUs) were designed to render rich 3D worlds by performing millions of simple calculations at once. What began as a tool for gamers turned out to be exactly what neural networks needed. The vast web of capital deployed for GPUs (instruction set architectures, chip fabs, compilers, and distribution channels - all dominated at the time by Nvidia) proved to work about as well for running neural networks as it did for games. Unlike traditional CPUs, which execute tasks sequentially, GPUs excel at doing vast amounts of math in parallel. That made it possible to train ever-larger models on unprecedented volumes of data. Without the computational horsepower of GPUs, today’s massive language models would remain theoretical.
Taken together, the linguistic insights of statistical NLP, the adaptive power of neural networks, and the scaling force of GPUs converge into what we now think of as modern AI. That convergence explains why technologies like intelligent document processing for financial services, automated invoice extraction, and even sophisticated PDF extraction tools are so effective today. They’re not an overnight sensation, but rather a product of decades of steady evolution and different disciplines intersecting at just the right moment to unlock new possibilities at scale.
Lessons from the Dot-Com bubble
What we’re witnessing in AI today feels eerily familiar to anyone who lived through the dot-com era. Back then, capital poured in, startups sprouted overnight, and companies raced to launch websites simply because they could. Strategy often came second. The mantra wasn’t “ready, aim, fire” but “ready, fire, aim,” leading to companies burning through capital without the results to follow.
The same pattern is playing out now in AI. Organizations are rushing to roll out AI-connected automation projects without stopping to ask the fundamental questions: Do we actually know which workflows need rethinking? Have we brought our teams along in the process? Too often, the answer is no, and the outcome is predictable: big spend, lots of churn, little return.
As a result, the companies to see the most success with AI projects won’t be those that are fastest to pull the trigger. They’ll be the ones who pause just long enough to get the aim right, aligning AI investments with real business goals, building on a solid foundation, and treating automation not as a fad but as a strategic lever for growth.
Who’s ready for AI success?
So what’s the point of all of this? AI doesn’t mean that we get to scrap everything and start from scratch. Largely speaking, the companies that are best positioned to succeed in the AI era are the same ones that have been best positioned to succeed for the last 30 years. Because even though AI feels revolutionary right now because of its exciting capabilities, the strategies, tactics, tools, and challenges are far from new. Highlighted below are some common denominators of the teams seeing the most success with AI adoption.
Operational Excellence
Leaders have a deep grasp of how the business actually runs — the “nouns and verbs” of daily operations. They know where the bottlenecks are, which tasks are draining time, and how information moves across departments.
Ownership
AI initiatives are usually championed by someone with credibility inside the organization, a person trusted enough to rally teams around change. Without that internal advocate, even the smartest technology struggles to take hold.
Clear target
Rather than chasing flashy experiments, look for language-heavy processes that lend themselves naturally to automation (contracts, invoices, bills of lading, insurance claims, factoring claims, HR forms, compliance reports, etc.). In those areas, AI can deliver quick, measurable results by eliminating repetitive work and streamlining workflows.
Enhancement, not replacement
Approach AI with a growth mindset, not a replacement mindset. The most successful teams frame automation not as a threat but as an “Iron Man suit” for employees, a way to augment human capabilities and make work more engaging.
When these conditions line up, AI doesn’t feel like a bolt-on experiment. It becomes part of the fabric of the business, quietly transforming how documents flow across the enterprise and freeing teams to focus on the work that matters most.
What’s the best way to leverage AI?
AI has arrived as a transformative force in business, but its power is rooted in decades of progress, not sudden disruption. For business leaders, that should be freeing. You don’t need to imagine some distant, futuristic use case to benefit from it. Instead, the real opportunity lies in looking closely at the way your business operates right now.
Where are teams bogged down by repetitive, document-heavy processes? Maybe it’s manually reviewing contracts, or typing in important invoice data by hand, or taking time to reconcile bank statements. Whatever it may be, these are the areas where document processing AI and workflow automation can deliver immediate returns.
The message is simple: don’t think forward, think present. Focus on the processes in front of you today, identify what can be streamlined or automated, and use AI as a practical, evolutionary step to improve them. Organizations that focus on improving the present, not predicting the future, turn AI into a genuine competitive advantage.
