The negative thoughts against AI

Over the past few weeks, I’ve been reflecting deeply on what I do and why I’m able to do it. This introspection has been heightened by my work with the University of South Queensland - School of Business, where they are using my Business 2030 framework as a springboard for entrepreneurship in their workplace-integrated learning work this year.

What I’ve realized is that my background is incredibly varied. I’m not an expert in one specific area, but I possess a broad understanding of how things work and how different elements interconnect from a first principles level as likely used the tool as version 0.9 or had a deep dive into an area so I could build products or service for that area. At my core, I’m a problem solver who isn’t limited by assumptions or biases.

As part of this work, I decided to examine the objections people have to change and how I handle them in the current AI hype we are seeing. I find that the answer often lies in recognising patterns from the past and having the courage to ask, “Why now?”—a challenge I ended my last presentation with the students doing the course.

AI gives us the information but we need to break free from assumptions. The real question is: Do you have the courage to act on it?

And in true Steve Jobs fashion, I left them with this last thought:

Your most valuable asset is your naivety.

One area over the years I have had to deal with is the objections people put up against the solutions I offer and I decided that this time, why not ask the AI about AI? I turned to ChatGPT’s reasoning model and I provided it with a detailed description of my needs and examples. The response it generated was almost exactly what I was hoping for—unedited and authentic.

AI Objection Answers

Below is a list of common objections people have about AI, paired with historical analogies and stories that can help counter each point. The goal is to show how initial barriers (like cost or complexity) can quickly change when new technologies become mainstream or when businesses adapt effectively.

1. “AI is too expensive.”

Historical Analogy:

? Dropbox’s Early Days: When Dropbox was founded, online storage was extremely expensive, and internet speeds were not ideal. Yet, they built their business on the premise that storage costs and internet bandwidth would rapidly become cheaper and faster. Their bet paid off, and Dropbox became a pioneer in cloud storage.

Counterpoint:

? Cost Curves and Economies of Scale: Just like storage costs plummeted, AI tools and infrastructure are getting cheaper over time. Cloud providers now offer pay-as-you-go AI services, meaning you only pay for what you use. This makes entry costs more manageable than ever.

? Early Adoption Advantage: By starting AI projects now, businesses can position themselves to benefit from reduced costs and more advanced capabilities as the technology matures—much like Dropbox did with cloud storage.

2. “We don’t have enough data.”

Historical Analogy:

? Social Media Growth: Platforms like Facebook and Twitter initially had limited user data. As user adoption grew, they harnessed that data to refine algorithms for targeted ads and personalized feeds. Early on, they didn’t need massive data sets to see results; they used what they had and built from there.

Counterpoint:

? Start Small, Scale Up: AI models can be trained on relatively small or specialized data sets to deliver valuable insights. Over time, as more data is collected, the AI’s capabilities grow.

? External Data & Partnerships: Businesses can also supplement internal data with publicly available data or partner with other organizations, much like social platforms integrate third-party data to enhance user experiences.

3. “We don’t have the in-house expertise.”

Historical Analogy:

? Early Web Development: In the mid-90s, most businesses had no web developers on staff. They often outsourced or hired new talent as the need arose. Over time, creating and maintaining a website became an essential skill set.

Counterpoint:

? Accessible Tools and Services: AI platforms now offer low-code or no-code solutions that allow non-experts to build and deploy models.

? Evolving Workforce: Just like businesses eventually hired web developers, data scientists and machine learning engineers are becoming a standard part of modern teams. If you don’t want to hire full-time, there are many consulting or outsourcing options available.

4. “AI is too complex to integrate with our existing systems.”

Historical Analogy:

? ERP System Adoption: When ERP (Enterprise Resource Planning) solutions were first introduced, many businesses felt they were too large, complex, and disruptive. Over time, modular ERP systems and better integration tools made adoption more straightforward, and today they’re nearly universal in large companies.

Counterpoint:

? Modular AI Solutions: Many AI services are designed as microservices or APIs that can plug into existing workflows without massive overhauls.

? Incremental Implementation: Start by automating or optimizing a single process, then expand. This mirrors how ERP adoption often began in a single department before rolling out company-wide.

5. “We’re not sure AI will provide a real ROI.”

Historical Analogy:

? Online Retail Doubts: In the early 2000s, many traditional retailers questioned the ROI of e-commerce. Amazon and others proved that online retail not only offered ROI but also became essential for survival in the digital age.

Counterpoint:

? Data-Driven Pilots: Begin with a small pilot project where you can measure outcomes, like improved efficiency or cost savings. Demonstrating quick wins helps secure broader buy-in.

? Competitive Differentiation: Just as online retail separates winners from losers, AI-driven insights and automation can provide a competitive edge. Waiting too long could mean losing market share to AI-enabled competitors.

6. “Our customers/clients aren’t asking for AI.”

Historical Analogy:

? Smartphones and the iPhone: Before the iPhone, most consumers didn’t realise they needed or wanted a touchscreen smartphone. Apple introduced it, and demand skyrocketed. Customers often don’t know they need something until they see its potential.

Counterpoint:

? Hidden Benefits: AI can improve customer experiences behind the scenes—faster support, personalized recommendations, and better product quality. Customers may not ask for AI explicitly, but they do notice superior service and results.

? Setting the Standard: If you lead with new capabilities, you set the bar for customer expectations. Once they experience the benefits, they’ll expect it everywhere.

7. “AI might replace our employees.”

Historical Analogy:

? Industrial Revolution & Automation: Every wave of automation has sparked fears of job loss. However, history shows that while certain tasks are automated, new types of jobs and roles emerge—often at a higher skill level and with better pay.

Counterpoint:

? Task Automation, Not People Replacement: AI typically handles repetitive tasks, freeing employees to focus on more strategic, creative, or interpersonal work.

? Upskilling & Reskilling: Companies that invest in retraining employees to work alongside AI see higher morale, better retention, and improved innovation.

8. “AI is just a fad—like 3D TVs.”

Historical Analogy:

? The Internet: Early on, many believed the Internet was just a passing trend. Yet it fundamentally changed how we do business and live our daily lives.

? Mobile Apps: Similarly, mobile apps were initially seen as niche novelties. Now, they’re indispensable for both consumers and businesses

Counterpoint:

? Widespread Adoption: AI is not just in labs; it’s already embedded in search engines, social media, e-commerce, finance, healthcare, and more.

? Business Transformation: AI has proven its staying power by consistently delivering measurable improvements in operations, customer engagement, and decision-making—unlike 3D TVs, which never had broad practical use cases.

9. “We’re worried about data privacy and security.”

Historical Analogy:

? Cloud Adoption: When cloud computing first emerged, businesses were extremely concerned about data security. Over time, best practices, certifications, and regulations (like ISO standards, GDPR, etc.) made the cloud more secure than many on-premises solutions.

Counterpoint:

? Regulations & Best Practices: AI platforms are now subject to stringent privacy laws (GDPR, CCPA), and many offer built-in compliance features.

? Zero-Trust Architectures: Modern security frameworks ensure data is protected at every step, from ingestion to AI model output. With the right strategy, AI can actually enhance data governance by detecting anomalies or breaches.

10. “We don’t see how AI fits our specific industry.”

Historical Analogy:

? Machine Tools in Manufacturing: At first, some manufacturers doubted CNC machines and automation would fit their unique processes. Today, they’re an integral part of nearly every manufacturing line.

? E-commerce in Retail: Brick-and-mortar retailers once believed e-commerce wouldn’t suit their products. Now, online channels are essential for most retail operations.

Counterpoint:

? Custom Solutions: AI can be tailored for industry-specific needs, whether it’s predictive maintenance in manufacturing or personalized recommendations in retail.

? Industry Success Stories: Most sectors already have AI success stories—from agriculture (drone-based crop monitoring) to healthcare (diagnostic imaging). Researching those examples can reveal how AI might be adapted to your unique context.

Final Thoughts

By drawing on historical precedents—from the early days of the internet and cloud computing to the adoption of mobile apps and beyond—you can illustrate how once-daunting technologies became both accessible and indispensable. AI is following a similar trajectory, and businesses that overcome these initial objections often find themselves with a significant competitive advantage in the long run.

This is what the AI created, but missed two items that I asked it to add:-

Objection: “AI will make us lazy.”

Historical Analogies:

? The Calculator Debate:

When calculators became widely available in schools, many feared that reliance on them would erode mental arithmetic skills. Instead, calculators liberated students from rote computation, allowing them to engage in higher-level problem-solving and creative thinking.

? The Printing Press:

Critics worried that the printing press would lead to a passive consumption of information, undermining critical thinking and literacy. In reality, it revolutionized access to knowledge, spurring inquiry, debate, and innovation.

Counterpoint:

AI, like calculators and the printing press, is a tool designed to enhance our capabilities rather than diminish our efforts. By automating routine tasks, AI frees us to focus on more complex, creative, and strategic activities. Rather than making us lazy, it challenges us to use our newfound cognitive bandwidth to address more sophisticated problems and generate innovative solutions. The key is to use AI to complement our skills, empowering us to work smarter and engage more deeply with the tasks that matter most.

In essence, just as calculators reshaped education and the printing press transformed society, AI has the potential to boost our productivity and creativity—not to make us complacent.

Objection: “AI lies sometimes.”

Historical Analogies:

? Early Journalism and the Printing Press:

When the printing press first emerged, misprints and factual errors were common. The early days of mass communication saw misinformation slip through, but over time, editorial practices, fact-checking, and peer review significantly improved accuracy.

? Human Error in Traditional Processes:

Before modern technology, even trusted sources occasionally spread inaccuracies—be it through misinterpretations or human error. Society developed mechanisms, such as peer review and public debate, to identify and correct these mistakes.

Counterpoint:

No system is infallible, and while AI can sometimes generate inaccurate or misleading information (often referred to as “hallucinations”), this is not a flaw unique to AI. Just as quality control evolved in the printing industry, we can implement robust verification protocols and human oversight to catch and correct errors in AI outputs. The aim is to use AI as an assistant that enhances our capabilities, while always ensuring that critical judgment and validation remain in human hands.

Final Word

In the end, AI is new, evolving at speed and what we see today is very different to what it will be tomorrow. When the iPhone was released, everyone just thought it in the analogy of those times, not seeing what it would become and this is repeated many times throughout history. AI is based upon the information it has, our strength is we can bring information AI can't see into the equation and join it in different ways.

Interesting times ahead and opportunity for those who have the courage.

Who am I

I’ve been building technology products for 40 years, having experienced and contributed to the last four major cycles of change—starting with the PC revolution, followed by the Web, then the Cloud, and now AI. Throughout these cycles, I’ve often been one to write the first version of products that push the boundaries of what’s possible.

With the Business 2030 thesis, all my years of experience working with and supporting small businesses have come together. I see AI as a transformative force that can empower small businesses in ways we couldn’t imagine before.

It’s an exciting moment for innovation and opportunity.