AI Solutions Are Creating Artificial Needs

Author(s): Sophia Banton

Originally published on Towards AI.

AI Solutions Are Creating Artificial Needs

Was that task truly repetitive, or was it labeled as boring to justify automation?

A need is something people genuinely require to make their lives easier, solve a real problem, or improve their daily activities. An artificial need is something people are made to believe they require, even though it doesn’t genuinely improve their well-being or solve a real problem. By creating tools that don’t address actual issues but rather create the illusion of necessity, the AI industry has fallen into the trap of fostering artificial needs.

Make no mistake, AI can certainly address many genuine needs across workplaces — streamlining data analysis, enabling better customer service, and automating truly repetitive tasks. AI can also play a crucial role in specialized fields like medical diagnostics, fraud detection, and scientific research. However, many consumer and enterprise AI companies have fallen into the trap of creating artificial needs.

For example, do we need email summaries when we were already just skimming our emails? Skimming allowed us to judge what required deeper reading. Are we giving up our ability to think critically just to maybe save time, or are we creating a workforce that depends on AI for tasks people can easily do themselves? Ultimately, are we solving real-world problems, or just creating digital distractions?

A Case in Point

Nearly every professional creates PowerPoint slides at some point in their career. This opens the door for enterprise solutions that promise to improve daily workflows. But these solutions haven’t been delivering on their promises to enhance productivity and efficiency.

Take Microsoft Copilot in the enterprise, an all-in-one solution. It claims to revolutionize how we work, yet it struggles with basic tasks like image generation, leading professionals to frustrating workarounds, such as resorting to traditional image searches or using separate image generation tools, ultimately negating the supposed efficiency gains of Copilot.

Time Comparison for Getting an Image for a Presentation

• Google/Creative Commons search: ~2 minutes
• Dedicated image generator: ~5 minutes
• Fumbling with Copilot: Indefinite time + eventual workaround

This raises the question: How often do we actually need unique images? The answer for most business users is rarely. Yet, we’re paying premium prices for AI solutions that complicate simple workflows. The need for AI-generated images in presentations was never a significant pain point for most users, yet it’s being presented as a must-have feature. In other words, AI tools are being marketed for tasks that were never actual pain points.

The disadvantages of these all-in-one AI platforms are mounting:

• Clunky interfaces trying to do everything but excelling at very little
• Restrictive enterprise policies limiting functionality
• Limited transparency about the underlying technology that powers these solutions
• Subscription costs that far exceed the value delivered thus far

The question on the table: are we embracing AI because it genuinely solves problems, or because we’ve been convinced by tech companies that we need it?

The True Costs of Irresponsible AI Adoption

These challenges point to a deeper problem. Beyond the immediate frustrations and inefficiencies created by artificial needs, there’s a more significant long-term consequence for organizations investing in these solutions. The greatest cost of AI being marketed for invisible problems isn’t the price companies pay. Rather, it is the erosion of trust in AI tools among employees.

Employees already encounter AI in their personal lives through tools like ChatGPT, where they experience its limitations firsthand and develop justified skepticism. When workplace AI then falls short of marketing promises, they quietly revert to familiar workflows, creating wasted investment and failed digital transformation efforts. This creates a twofold challenge for adoption where it’s genuinely needed: distrust in AI capabilities and frustration with AI outcomes.

When employees keep using AI tools that overpromise and underdeliver, they lose trust in AI — even when it could actually help.

AI Leadership Must Prioritize Real Value

As Jimmy Carter once said, “We must adjust to changing times and still hold to unchanging principles.” In the AI era, that unchanging principle is that we should build technology that solves real-world problems. AI excels when it optimizes data analysis, enhances customer support, and advances fields like clinical diagnostics and anomaly detection — areas where it solves real problems instead of creating artificial needs. It has the potential to open new doors to opportunity when applied with genuine purpose.

For AI to fulfill its promise of expanding opportunity, AI leaders must do more than build AI — they must prioritize solving real problems and driving meaningful progress. AI leadership requires building tools that deliver meaningful value, not digital distractions. Before your next AI investment, challenge vendors to prove they’re solving a problem your team actually has — not one they’ve invented to sell a solution. Remember: A tool without purpose becomes noise.

About the Author

Sophia Banton is an AI Solution Lead specializing in Responsible AI governance, workplace AI adoption, and AI strategy in IT. With a background in bioinformatics, public health, and data science, she brings an interdisciplinary approach to AI implementation and governance. She writes about the real-world impact of AI beyond theory, bridging technical execution with business strategy. Connect with her on LinkedIn or explore more AI insights on Medium.

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Published via Towards AI