The Future of AI: Slower, Stranger, and More Practical Than You Think
It all begins with an idea.
By: Nick Stratman
Artificial intelligence is hurtling toward us with dizzying speed, on the cusp of completely revolutionizing the world and making everyone’s job obsolete… or so we are told.
In conversations with colleagues, friends, and business leaders in healthcare staffing, I have noticed a growing anxiety, and a fair amount of misunderstanding, about what artificial intelligence will mean for our world. Some expect a sudden, catastrophic intelligence explosion that overturns entire industries overnight. Others imagine a smooth, frictionless upgrade to the status quo. I personally believe that this change will be much stranger, slower, and more human than either story suggests. That is why I am writing this article: to share a more grounded view of how AI might actually unfold in the years ahead.
Deploying AI into the real world is much harder than building it in a lab. Industries like healthcare staffing, logistics, and finance are built on intricate, deeply human systems that do not change overnight. Successful adoption of AI will require companies to reorganize workflows, retrain teams, and patiently integrate new tools into existing structures. Even the most advanced models cannot simply replace institutions that evolved over decades. In healthcare staffing, for instance, using AI for candidate matching and lower-level sales tasks like CRM data entry holds enormous promise. But this progress will be gradual, shaped by the slow work of navigating regulatory hurdles, credentialing requirements, and personal relationships.
AI adoption will happen through a series of decentralized business decisions, not from sweeping strategic plans. Different companies will experiment, succeed, fail, and adapt at their own pace. In healthcare staffing, some firms may quickly deploy AI to optimize clinician matching, automate credentialing, or predict client needs. Others may be slower to move, from a mix of both not knowing how to implement AI effectively or not fully understanding the benefits. The winners will not be those who planned the most, but those who learned the fastest from real-world use.
Understanding where AI thrives and where it struggles is crucial to separating hype from reality. In traditional chess, AI completely dominates the best humans, calculating countless variations far beyond even Magnus Carlsen’s reach. In Fischer Random Chess, where the board setup is randomized and intuition matters more, AI still wins, though its edge is narrower. Yet when the environment becomes vastly more complex and open-ended, as in StarCraft II, AI falters. It outperforms about 99% of humans, but only after immense amounts of specialized training and fine-tuning. The lesson is simple: when the space of possibilities is clearly defined, AI can thrive. When the environment is dynamic, unpredictable, and full of hidden variables, its power diminishes. In industries like healthcare staffing, this distinction matters. AI can already handle structured tasks like ranking candidates or flagging missing credentials. But for sprawling challenges such as building lasting relationships with providers or adapting to the shifting needs of hospital systems, human judgment remains essential, with AI serving as a capable but limited assistant.
One of AI’s more practical contributions may be the ability to extend expertise, rather than to replace it. In today’s economy, skills are slow to acquire and difficult to scale. AI can help by capturing certain decision-making patterns and making them widely available. In healthcare staffing, for instance, AI tools might learn from top recruiters and assist thousands of placements, improving speed and consistency. But even here, the impact will be uneven. Judgment shaped by experience, subtle human interactions, and trust-building will remain hard to replicate. Instead of replacing human expertise overnight, AI will amplify it slowly, piece by piece.
With that said, the adoption of AI in healthcare staffing will not be as simple as flipping a switch. Complex tasks such as understanding personal needs, navigating regulations, and building relationships will continue to require human involvement. AI can sharpen insights and automate administrative burdens, but it does not have the ability to make complex, high-trust decisions. In healthcare staffing and beyond, the future looks less like machines taking over and more like machines becoming trusted partners in human work.
The businesses that succeed will not be the ones that bet on any single outcome. They will be the ones that build cultures of flexibility, able to pivot as technologies evolve. In healthcare staffing and across the economy, adaptability will prove far more valuable than any crystal ball.
Above all, we should recognize that the rise of AI will not be a single moment but a sprawling, decentralized process, filled with false starts and unexpected bottlenecks. As new capabilities emerge, industries will adapt. Not because of an overnight intelligence detonation, but through millions of decisions made by leaders, workers, and customers grappling with the change themselves. The future will belong not to those who can predict it best, but to those who can keep pace with its surprises.