AI Adaptation vs Outcome: The Brutal Truth Companies Must Face

The Hard Reality: There Is No AI

Let’s be clear. AI, as most companies imagine it, does not exist. The term artificial intelligence is thrown around as if we are dealing with machines that think and reason like humans. We are not. What we have are Large Language Models (LLMs) and machine learning algorithms. They are powerful tools, but they do not possess intelligence. The problem? Companies are deploying them without a plan for tangible results.

The Hype vs Reality

Businesses are spending millions on AI, expecting it to transform operations overnight. The expectation? Greater efficiency, seamless automation, and better decision-making. The reality? Disappointment. AI is only effective in specific areas—customer support chatbots, content creation, data analysis, and process automation. Expecting it to think or innovate is a delusion.

Example: Useless Chatbots

Many companies roll out AI chatbots to cut costs. The problem? They frustrate customers, fail at understanding queries, and often push users back to human agents. Instead of streamlining customer service, they add friction.

Example: AI in Hiring – A Broken System

Companies use AI-driven recruitment tools to filter candidates. But these tools are trained on biased data, leading to discrimination. Amazon scrapped its AI hiring tool after discovering it was biased against women. Without human oversight, AI hiring can be worse than manual screening.

Example: Fake AI – Old Tricks in a New Wrapper

Converting a Word file to a PDF is not AI. A script that sorts emails into folders is not AI. Yet companies market basic automation as AI-powered technology. This deceptive branding misleads businesses into thinking they are adopting cutting-edge solutions.

Example: AI in Supply Chain – High Costs, Low Value

A supply chain manager applying a time-series model to predict demand isn’t using AI—it’s just statistics. Many AI-based forecasting models offer little accuracy improvement but come with massive costs. Companies pay more for software without seeing real benefits.

AI Without Results Is Worthless

AI is not the problem. The issue is how businesses use it. Throwing money at AI without defining clear objectives is like hiring an employee with no role. AI must deliver measurable impact:

• Lower Costs: Is it reducing operational expenses?
• Increased Revenue: Is it boosting sales or improving conversions?
• Operational Efficiency: Is it streamlining processes or adding complexity?
• Better Customer Experience: Are chatbots improving satisfaction or pushing customers away?

The Fix: AI Must Be Tied to Outcomes

Too many companies adopt AI without a plan. They implement tools without understanding their value. The result? AI becomes an expensive, underutilised gimmick. To change this, businesses need an outcome-driven approach:

1. Set Clear Goals: AI should be implemented to achieve specific objectives—whether increasing sales, reducing churn, or optimising workflows.
2. Measure Success: Track AI’s impact on key metrics. If it isn’t working, adjust or abandon it.
3. Stop Calling Everything AI: If it’s just automation or statistical modelling, say so. AI should only refer to systems that truly learn and improve over time.

Conclusion: AI Must Have Purpose

The future of AI is not in blind adoption but in practical application. Companies must move beyond hype and demand real, measurable results. AI should be a tool, not a magic solution. Those who succeed will be the ones who understand why they are using AI and what they expect from it.

Call to Action

If your company is not seeing results from AI, rethink your approach. Focus on measurable impact. Align AI with clear business goals. Stop investing in technology for the sake of it. AI without results is a waste. Make it work for you or don’t use it at all.