by Tyler Kelley
For many business leaders, artificial intelligence feels like an overnight sensation. Though AI research has existed for over 50 years, its applications for enterprise only recently captured mainstream attention over the last 12-18 months with the rise of systems like ChatGPT.
This rapid adoption cycle mirrors the trajectory of search engine optimization (SEO) in the early 2000s. After years of niche development, SEO suddenly surged into the spotlight as companies raced to capitalize on the internet boom. Growth exploded – but so did the hype.
Much like those early SEO days, AI now finds itself at a pivotal point between hype and substantiated impact as organizations balance tremendous enthusiasm with pragmatic assessment. Applying lessons around objective evaluation can help businesses adopt AI for the right reasons and outcomes.
The Explosive Hype of Early SEO
SEO went through a period of breakneck spending growth as usage of Google and other search engines began rising exponentially in the early 2000s. Companies rushed to optimize websites for higher rankings and traffic acquisition.
However, the SEO industry also became rife with underhanded tactics, spam sites, and firms selling snake oil optimizations with little methodology or lasting results. Even well-intentioned practitioners struggled with the limited visibility into early Google ranking algorithms.
By the mid-2000s, the space was clouded by hype. Yet gradually, increased public awareness of core ranking factors along with maturing best practices helped legitimate SEO solidify as an essential, multi-billion dollar digital marketing discipline.
Cautious Optimism for An AI Inflection Point
In 2023, AI adoption finds itself at a similar point of mass enthusiasm mixed with inflated claims that require prudent navigation. With International Data Corporation predicting over $500 billion in global spending this year, excitement is palpable across industries. However, unrealistic expectations also abound.
Moving Past the Hype with Critical Evaluation
For SEO, public awareness and consensus around core ranking factors provided guideposts to separate legitimate and inflated claims.
Similar guidelines can enable organizations today to objectively assess AI offerings:
Focus on proven capabilities - Clear understanding of areas where AI reliably delivers today includes personalization, forecasting, content creation, computer vision applications and robotic process automation versus more speculative cases.
Verify performance metrics - Require transparency from vendors and partners around training data, model benchmarks and measured accuracy/impact stats versus taking claims at face value.
Audit for risks preemptively - Thoroughly evaluate solutions for potential biases, security flaws and misinformation vulnerabilities rather than assuming responsible development.
Maintain realistic expectations - Be wary of promises crossing over into exaggeration around autonomous reasoning, creative judgment and other very human skills not yet achievable by AI.
Just as SEO matured into an essential marketing discipline through pragmatism, AI adoption rooted in practicality over hype holds the same trajectory for responsibly reshaping business and society substantially for the better.
For business leaders navigating an explosion of AI solutions, focusing on proven capabilities, measurable stats and mitigating risks provides the balanced approach required to maximize benefits rather than chasing exaggerations.
Just as thoughtful SEO strategies became critical for digital success, pragmatic AI adoption paves the road for transformative progress. By spotlighting substance over hype today, organizations can steer AI toward reaching its fullest potential in the years ahead.
Tyler Kelley is the Co-founder and Chief Strategist of SLAM! Agency, where he helps mission-driven organizations and innovative businesses amplify their impact to drive meaningful change. In this column, Tyler provides actionable insights to help businesses and leaders navigate our increasingly AI-driven world.