Will AI Disrupt "Routine" or "Complex" Business Functions and Job Roles?

One of the emerging paradoxes of artificial intelligence is that its greatest value for business processes includes both processes that are “routine” or “repetitive,” or, on the other hand, complex. 

But the key lies in relative value. High-value AI use cases typically involve complex decision-making, pattern recognition, predictive analytics, or end-to-end process automation. 

Lower-value use cases are often repetitive, routine, or administrative tasks where AI provides incremental, but not transformative, improvements. 

So even if some might think AI will be most disruptive for lower-skill occupations and tasks, it seems highly likely that the bigger amount of disruption will come for job functions that are cognitive, across most industries. 

Industry	High-Value AI Use Cases	Lower-Value AI Use Cases
Healthcare	Diagnostics (image/lab analysis), personalized treatment, drug discovery, operational optimization 1,2,3	Routine admin tasks (e.g., appointment reminders), basic data entry
Finance	Fraud detection, algorithmic trading, credit risk assessment, automated loan processing 1,2	Simple customer queries, basic transaction categorization
Manufacturing	Predictive maintenance, quality control, supply chain optimization 2,3	Standard inventory updates, basic production scheduling
Retail	Personalized recommendations, demand forecasting, dynamic pricing, customer support bots 2,3	Standard order processing, generic marketing emails
Software Development	Code generation, intelligent code completion, automated testing/debugging, documentation generation 4,5	Simple script writing, basic code formatting
Education	Adaptive learning platforms, personalized content, automated grading 1,3	Scheduling classes, distributing standard materials
Business Operations	Hyperautomation (end-to-end process automation), advanced analytics, generative content for marketing/HR 6 3	Expense report filing, simple workflow notifications

AI Inference Costs Will Become More Predictable, as Did Cloud Computing "As a Service" Costs

Though it is rational to note that AI inference costs are somewhat unpredictable at the moment, that also was true of cloud computing in general. But as the technology matures, it is likely--probably inevitable--that AI inference “as a service” will develop methods of providing customers more cost predictability. 

 

Cloud computing, after all, also featured unpredictable, often expensive usage-based pricing, that made customer budgeting difficult. 

But the market responded, creating pricing models (reserved instances, committed use discounts, and hybrid approaches) that provided cost predictability.

AI inference is likely following a similar trajectory. Token-based pricing means the cost per inference can fluctuate based on model complexity, input length, and provider capacity.

But providers already are experimenting with different approaches beyond pure pay-per-token models: subscription tiers, reserved capacity options or volume discounts that provide more predictable monthly costs. 

Enterprise contracts increasingly include committed usage terms that offer better rate predictability as well. 

And  competition also will drive providers to offer more customer-friendly pricing structures. AWS, Google Cloud, and Azure all evolved toward more predictable pricing options as the market matured and customers demanded better cost management tools.

At the same time,  models, hardware and inference acceleration will naturally drive down costs. So will analytics. 

Some of us cannot see the cost unpredictability being a long-term issue for AI inference.

Does AI Use Really Reduce "Critical Thinking?"

Lots of people, and some studies, might suggest frequent use of artificial intelligence reduces the ability to think critically, generally thought to include skills such as:

• Analysis: Breaking down information into its components, identifying underlying structures, and understanding relationships among ideas.

• Interpretation: Understanding and explaining the meaning of information, data, or events.

• Evaluation: Assessing the credibility, strength, and relevance of arguments, evidence, and sources.

• Inference: Drawing conclusions from available information and recognizing possible implications or consequences.

• Explanation: Clearly and coherently expressing one’s reasoning and justifying conclusions.

• Self-regulation: Monitoring and correcting one’s own thinking processes and biases.

• Open-mindedness: Considering alternative viewpoints and being willing to revise one’s own beliefs in light of new evidence.

• Problem-solving: Identifying problems, generating solutions, and implementing effective strategies.

• Creativity: Making connections between seemingly unrelated ideas and generating innovative solutions.

• Communication: Effectively articulating ideas, listening to others, and engaging in constructive dialogue.

Be honest: how many people do you actually know these days who actually are good at critical thinking? 

Maybe studies of AI chatbot use on “critical thinking” only measure pre-existing propensities. If you have ever taught college students, in the days before AI existed, you might already believe that critical thinking skills are unevenly distributed, and it has nothing to do with AI. 

You might also tend to believe that graduate school often sharpens such skills far more than did your undergraduate education. If so, then more education might enhance critical thinking skills to a significant extent. 

The rejoinder might be that all of us might have experienced some lack of proficiency in one or more of the bullet points noted above, ranging from ability to evaluate sources to remaining open minded. 

That noted, at least some studies suggest a possible link between use of AI chatbots and a decline of “critical thinking.” 

Factor	Negative Impact on Critical Thinking	Neutral or Positive Impact on Critical Thinking
Frequent, passive use	Yes 1,2,3	—
Thoughtful, interactive use	—	Yes 5
Younger age	Yes 1,2	—
Higher education	—	Yes 1, 2
Low motivation	Yes 4, 5	—

When Does AI Not Add Much Value for Consumer Hardware?

As useful as artificial intelligence is for software, that might not mean it is equally compelling for many types of consumer hardware. 

I always see the display of the Ray-Ban smart glasses when at my optometrist’s office, but that does raise issues: where’s the value? How much added value is there, compared to the extra cost? And for some of us, there are other tradeoffs.

How do smart glasses integrate with one’s prescription lens requirements? How about frame selection? 

One issue with embedding AI into consumer devices is the willingness to pay. Most consumers are careful about paying too much of a premium for such features. The other issue is simply value: it remains unclear whether the features are important enough, on a regular basis, to make the investment worthwhile. 

And then there are the other physical impediments. Until recently, I wore prescription corrective lenses, so it has not been clear whether I’d have the choice of frames I’d prefer, or how much additional cost the corrective lenses feature would add to the smart glasses. 

Use Case	Description
Real-Time Translation	Live translation of speech/text, projected in view
AI Voice Assistant	Hands-free digital assistant for tasks and queries
AR Overlays	Navigation, notifications, and contextual info in the user’s field of view
Object Recognition	Identifies and describes objects, scenes, or people
Accessibility	Features for visually/hearing-impaired users (captions, scene description, hearing aids)
Hands-Free Communication	Dictation, messaging, calls, content capture without hands
Health & Wellness Monitoring	Eye tracking, fatigue detection, and health feedback
Spatial Computing & Gestures	Air typing, gesture-based controls, and virtual interface interaction
Professional/Industrial Use	Workflow optimization, remote support, and real-time data for industry-specific tasks

But similar questions can be asked about embedding AI in most consumer appliances, especially the ones we use most often, including PCs, smartphones, and household appliances. 

Consider the trend of consumers using minimalist phones, for example. Sometimes the AI features are not desired. For many basic household appliances, it isn’t clear that the added AI features add enough value (toaster, kettle, blender). For other appliances, such as audio and video playback gear, users might only want faithful linear performance. 

In many cases, the increased complexity also means higher failure rates, in addition to greater cost. And then there are often battery life issues as well.   

The success rate  for new consumer electronics products is generally low, with failure rates commonly cited between 50 percent and 90 percent, depending on the source and how "failure" is defined. Some studies suggest success rates are as high as 60 percent, and failure rates as low as 40 percent. 

Packaged goods products likely fail at higher rates, perhaps as high as 95 percent. 

So it might not be too surprising that “smart” devices have not yet generally “caught on” with consumers. That noted, smart glasses seem to be faring better than the other alternatives for smart devices that are not PCs or phones. 

Device Type	2024 Installed Base / Sales (Global)	2025/2026 Projection	Notes
AI/AR Smart Glasses	~1.5 to 2 million units sold	10M units/year (by 2026)	Ray-Ban Meta is leading growth3,4,6
US Smart Glasses Users	~13 million (2024)	~14.4 million (2025)	Includes all smart glasses 6
Smart Pins/Buttons	Not reliably reported	N/A	Still niche, early-stage market 6,7