When the Disruptors Get Disrupted

For most people in IT, change is constant.

New platforms arrive. Old tools fade. Processes are reworked. Skills must evolve.

In that sense, disruption has long been part of the job description.

Software developers create new and improved tools. They streamline workflows. They automate tasks that once required entire teams. Over time, they have reshaped and disrupted how work gets done across nearly every industry.

This pattern has been in place for decades.

For software developers, something different is happening now.

With the arrival of AI-assisted development tools, including systems like Anthropic’s Claude Code, disruption has begun to turn inward. These tools are reshaping how developers approach their own work.

For many in the profession, this feels unfamiliar.

Software development continues, but the definition and details of the role are shifting. Tasks that once required sustained manual effort can now be generated, refactored, tested, and explained with remarkable speed.

A developer who once spent an afternoon writing API integration code might now spend fifteen minutes directing an AI to produce it, followed by an hour reviewing edge cases and security implications. The center of gravity moves toward judgment and direction rather than execution and production.

When job roles experience disruption, responses tend to follow predictable patterns. Some people dismiss the change as temporary or overhyped. Others push back, trying to protect familiar and comfortable ways of working. Still others approach the change with curiosity and engagement, interested in how new capabilities can expand what’s possible.

Intent Makes the Difference

An important distinction often gets overlooked when discussing pushbacks.

Some resistance grows from denial. It spends energy cataloging flaws, defending established workflows, or hoping new tools disappear. That approach drains effort without shaping new outcomes. It preserves little and teaches even less.

Other forms of resistance grow from professional judgment.

Experienced developers often notice risks that early enthusiasm misses. Fragile abstractions, security gaps, maintenance burdens, and failures that appear only at scale become visible through lived experience. When developers raise concerns in the service of quality, safety, and long-term viability, their input strengthens the eventual solution. This kind of resistance shapes progress rather than attempting to stop it.

The most effective developers recognize this shift and respond deliberately. They move away from opposing new tools and toward advocating for their effective use. They ask better questions. They redesign workflows. They establish guardrails. They apply experience where judgment continues to matter.

In doing so, they follow the same guidance developers have offered others for years.

Embrace new tools.
Continually re-engineer how work gets done.
Move upstream toward problem framing, system design, and decision-making.

Greater Emphasis on Judgment

AI generates code with increasing competence. Decisions about what should be built, which tradeoffs make sense, and how systems must evolve over time still require human judgment. As automation accelerates, these responsibilities grow more visible and more critical.

This opportunity in front of developers calls for leadership.

Developers who work fluently with these tools, guide their thoughtful adoption, and help their teams and organizations navigate the transition become trusted guides through change. Their leadership shows up in practical ways:

-pairing new capabilities with healthy skepticism

-putting review processes in place to catch subtle errors

-mentoring junior developers in how to evaluate results rather than simply generating them

-exercising judgment to prioritize tasks that benefit most from automation

Disruption has always been part of the work.

The open question is whether we meet disruption as participants, or step forward as guides.

Photo by AltumCode on Unsplash

Measuring the AI Dividend

In the early 1990s, the term Peace Dividend appeared in headlines and boardrooms. The Cold War had ended, and nations began asking what they might gain by redirecting the resources once committed to defense.

Today the conflict is between our old ways of working and the new reality AI brings. After denial (it’s just a fad), anger (it’s taking our jobs), withdrawal (I’ll wait this one out), and finally acceptance (maybe I should learn how to use AI tools), the picture is clear. AI is here, and it’s reshaping how we think, learn, and work.

Which leads to the natural question. What is our AI Dividend?

Leaders everywhere are trying to measure it. Some ask how many people they can eliminate. Others ask how much more their existing teams can achieve. The real opportunity sits between these two questions.

Few leaders look at this across the right horizon. Every major technological shift starts out loud, then settles into a steady climb toward real value. AI will follow that same pattern.

The early dividends won’t show up on a budget line. They’ll show up in the work. Faster learning inside teams. More accurate decisions. More experiments completed in a week instead of a quarter.

When small gains compound, momentum builds. Work speeds up. Confidence rises. People will begin treating AI as a partner in thinking, not merely a shortcut for output.

At that point the important questions show themselves. Are ideas moving to action faster? Are we correcting less and creating more? Are our teams becoming more curious, more capable, and more energized?

The most valuable AI Dividend is actually the Human Dividend. As machines handle the mechanical, people reclaim their time and attention for creative work, deeper customer relationships, and more purpose-filled contributions. This dividend can’t be measured only in savings or productivity. It will be seen in what people build when they have room to imagine again.

In the years ahead, leaders who measure wisely will look beyond immediate cost savings and focus on what their organizations can create that couldn’t have existed before.

Photo by C Bischoff on Unsplash – because some of the time we gain from using AI will free us up to work on non-AI pursuits. 

The Known vs. The Obvious: Embracing AI in the Workplace

For years, we’ve heard that Artificial Intelligence (AI) will revolutionize industries. The idea is so prevalent that it’s easy to stop actively thinking about it. We acknowledge AI in headlines, in passing business conversations, and in abstract discussions about the future. Yet, much like a fish is unaware of the water surrounding it, we’ve been immersed in AI without fully recognizing its impact.

That impact is now undeniable. The question is: will we embrace it—or ignore it at our peril?

AI as the Invisible Force

AI is no longer a futuristic concept, or a background presence. It’s embedded in the tools we use every day, from the smartphones in our pockets to the chatbots handling our customer inquiries. It powers business decisions, optimizes operations, and influences nearly every industry.

Yet, because AI is so familiar, we often overlook it. The term itself has become a cliché—almost old news.  Something we assume we understand. But do we? How much do we really know about its capabilities, its limitations, or its potential disruptions?

Many still view AI as a distant idea, relevant only in the future or in industries far removed from their own. This perspective is outdated.

The Shift from “Known” to “Obvious”

AI is a driving force that can shape how we work, compete, and innovate. Organizations that continue treating AI as an abstract concept risk being blindsided by its rapid evolution.

This shift—from AI being “known” to becoming “obvious”—is critical. The moment we stop seeing AI as some far-off development and recognize it as an immediate force, we can take meaningful action.

Make no mistake: AI will transform your organization, whether you engage with it or not. The only choice is whether you’re leading that change or struggling to catch up.

The Cost of Waiting

A passive approach to AI is no longer viable. Waiting for the “right time” to adopt AI means falling behind competitors who are already leveraging its power. Yes, AI is complex, and yes, there are risks. But the greater risk lies in hesitation.

I’m old enough to remember the early days of the internet (I’m that old).  Most businesses dismissed it as a fad. Others chased the new idea with reckless abandonment and wasted tons of time and money.  But a relative few (at the time) experimented, learned, made incremental changes, and ultimately thrived in their use of the new “internet-powered” approach. Not to mention all the new multi-billion (trillion) dollar businesses that were made possible by the internet. 

AI is following a similar trajectory. Many are ignoring, even shunning, AI as something other people will figure out.  They don’t want to be the one pushing these new ideas within their organization.  It’s easier to stay in the background and wait for someone else to take the leap.

But others are already leaning in (to coin a phrase), experimenting, and learning.  They are incrementally (and sometimes dramatically) shaping a new future…and remaining relevant in the process. 

Learn the Basics

AI adoption doesn’t require immediate mastery. It starts with small, intentional steps.

You don’t need to be an AI expert, but understanding its core functions and business applications is essential.

Start by exploring industry-specific AI tools already in use.  How did I make this list?  You guessed it, I asked ChatGPT to give me a list of industry-specific AI tools in use today.  Will each one be a winner?  Not sure, but it’s a great list to use as a starting point:

Retailers use Amazon Personalize and Google Recommendations AI for AI-driven product suggestions, improving customer engagement and sales.

Marketers leverage HubSpot AI for automated email campaigns, Persado for AI-powered ad copywriting, and Seventh Sense for optimizing email send times.

Financial analysts turn to Bloomberg Terminal AI for market insights, Kavout for AI-driven stock analysis, and Zest AI for smarter credit risk assessments.

Healthcare professionals rely on IBM Watson Health for AI-assisted diagnostics and Olive AI for automating administrative hospital tasks.

Manufacturers use Siemens MindSphere for AI-powered predictive maintenance and Falkonry for real-time industrial data monitoring.

Customer service teams integrate Forethought AI for automated ticket triaging and Zendesk AI for intelligent chatbot interactions.

HR and recruitment teams utilize HireVue AI for AI-driven candidate screening and Pymetrics for bias-free talent assessment.

Experiment with Broad-based AI Tools

Don’t wait for the perfect strategy.  Start small. Generalized AI tools can improve various aspects of your business (again, I asked ChatGPT for this list):

Conversational AI & Research: Tools like ChatGPT, Claude.ai, or Anthropic’s AI help generate content, answer complex questions, summarize reports, and assist in brainstorming sessions.

Automation: Platforms such as Zapier AI, UiPath, and Notion AI automate workflows, streamline repetitive tasks, and generate notes and summaries.

Data Analysis: Solutions like Tableau AI, ChatGPT’s Code Interpreter (Advanced Data Analysis), and IBM Watson process and visualize data for better decision-making.

Customer Engagement: AI-driven tools such as Drift AI, Intercom AI, and Crystal Knows enhance customer service, lead generation, and sales profiling.

These are just a few of the many AI-powered tools available today. The landscape is constantly evolving.  Exploring AI solutions that fit your specific needs is the key to personal and professional growth.

Cultivate a Growth Mindset

Learning AI is a journey, not a destination. It’s okay to make mistakes.  It’s actually necessary. Feeling uncomfortable is a sign of growth. The more you experiment, fail, and adjust, the more effectively you’ll integrate AI into your work. AI isn’t about instant perfection.  It’s about continuous learning.

Lead from the Front

If you’re in a leadership role, set the tone. Your team will look to you for guidance. Show them that AI adoption isn’t just an IT initiative.  It’s a mindset shift.

Encourage experimentation, provide resources, and support a culture of AI-driven innovation. Companies that will thrive with AI aren’t the ones waiting for a complete plan.  They’re the ones embracing AI through hands-on learning and iterative improvement while incorporating these new discoveries into their future plans.

The Future is Now

AI is not a distant disruptor—it’s an active force shaping today’s workplace. Organizations that recognize this and take action will thrive. Those that don’t will be left behind.

It’s time to stop treating AI as a theoretical innovation and start engaging with it as a business reality.

The future isn’t waiting, and neither should you.

Photo credit: The graphic was generated by DALL-E.  I asked it to generate an image of an office on the ground floor that captures the essence of the blog post I had just written. 

In its first few attempts, it tossed in robots sitting amongst the office workers.  I like to think of myself as a forward thinker, but I’m not quite ready to accept that reality…even though I’m sure it’s rapidly approaching.  I asked DALL-E to eliminate the robots (for now).