When To Use It And When To Skip It

Aditi Godbole – AI/ML Strategy Expert optimizing enterprise software for efficiency, innovation & data-driven decision-making.

I would say, artificial intelligence (AI) is the most overhyped and misunderstood tool in business today. While we see some companies thrive with AI, others waste millions chasing trends that don’t fit their needs. According to Gartner, nearly “30% of AI projects are expected to fail after the proof-of-concept stage, mainly due to unclear business objectives, poor data strategy, and [underestimating implementation challenges.]” Now, the key question we need to ask is: When does AI truly create value—and when is it just an expensive mistake that is detrimental to the business?

The AI Hype Vs. Reality

The rapid change in the AI landscape has been fueled by advancements in generative AI models, specialized AI chips and edge computing. In these exciting times, open-source AI tools and no-code platforms have lowered entry barriers and made AI more accessible.

However, AI is not a cure-all—companies that rush into AI face high costs, insufficient data and ethical dilemmas, leading to poorly executed AI solutions that do not drive any value. For example, Air Canada’s AI chatbot delivered misleading customer support information at a vulnerable time for their customer, which resulted in legal consequences where the airline was held liable for negligent misrepresentation. In contrast, Google’s AI-driven data center cooling system reduced energy costs by approximately 40%, showcasing AI’s potential for strategic efficiency when properly implemented.

When To Use AI In Business

To avoid common pitfalls that come with building AI solutions, we need to determine when AI is a valuable investment; business leaders must consider the specific challenges they face. AI succeeds in situations that require advanced computation, pattern recognition and automation that go beyond traditional software capabilities.

Let’s look at the key areas where AI can be most effective:

1. Complex Decision-Making & Pattern Recognition: AI is a proven strategy that is effective for tasks that involve pattern recognition, classification and forecasting. Industries like finance, e-commerce and healthcare leverage AI for fraud detection, risk assessment and personalized recommendations.
2. Automation for Efficiency & Cost Reduction: Many routine tasks can be automated using AI-powered chatbots, robotic process automation (RPA) and intelligent workflows. For example, Logistic firms use AI for optimize delivery routes which leads to reduction fuel consumption and improved turnaround times.
3. Enhanced Customer Experience: AI-driven personalization transforms customer engagement. Netflix’s AI recommendation engine increases user retention, while AI-powered chatbots provide 24/7 customer service. In retail, AI helps predict customer preferences and tailor marketing campaigns.
4. Big Data Processing & Predictive Analytics: AI can analyze large datasets in real time and provide actionable insights. Financial institutions use AI to assess credit risk, and manufacturers leverage AI for predictive maintenance, reducing downtime and operational costs.
5. Agentic AI for Autonomous Decision-Making: Agentic AI goes beyond traditional automation by enabling systems to independently make strategic decisions, adapt to new situations and execute complex tasks with minimal human intervention. In fields such as autonomous robotics, real-time cybersecurity, agentic AI can provide dynamic adaptability and self-improving capabilities.
6. Industries Requiring Real-Time Insights: AI is essential in sectors demanding rapid decision-making, such as cybersecurity, where AI detects anomalies in real time, and healthcare, where AI aids in early disease diagnosis.

When To Skip AI In Business

We know that AI presents compelling opportunities. However, it is not always the right solution. There are scenarios where AI may not provide sufficient value, leading to unnecessary costs and complexity. Business leaders should avoid AI implementation in the following cases:

1. If-Then Logic Beats AI: Not every problem requires AI. If a challenge can be solved using simple rule-based logic, traditional software is often a faster, cheaper and more effective solution.
2. When High-Quality Data is Lacking: AI models require large, well-structured datasets. Poor-quality or biased data leads to unreliable AI models. Without sufficient, representative data, businesses risk producing inaccurate or ethically questionable outcomes.
3. When Explainability is Critical: AI’s black-box nature can be a liability in highly regulated industries like finance and healthcare, where clear decision-making transparency is required. Techniques like LIME and SHAP can help interpret AI models, but in some cases, traditional analytical methods may be a better fit.
4. When Costs Outweigh Benefits: AI adoption involves significant investments in infrastructure, talent and ongoing maintenance. Businesses must evaluate whether the expected efficiency gains or revenue growth justify the upfront costs.
5. When Ethical and Sustainability Concerns Arise: AI can perpetuate biases in training data and requires substantial computing power, increasing its carbon footprint. Businesses should evaluate whether the AI solution aligns with their ethical and sustainability goals before deployment.

The Strategic Approach To AI Adoption

AI can bring tremendous value to a company’s operations if it is applied strategically. Businesses should assess their AI readiness by considering data availability, infrastructure requirements and potential regulatory constraints. Pilot programs can help validate AI initiatives before full-scale deployment, reducing financial and operational risks.

Businesses must also be strategic in the execution of their AI initiative. Before adopting an AI program, it is import to understand the different types of AI to determine the best fit for a business problem:

• Traditional AI (Machine Learning & Deep Learning): Best suited for structured prediction tasks, fraud detection and recommendation systems.

• Generative AI (GenAI): Effective for content creation, synthetic data generation and conversational AI, but requires careful monitoring to avoid misinformation.

• Rule-Based Automation: Ideal for deterministic processes, such as compliance checks or inventory management, where clear-cut logic is sufficient.

• Agentic AI: Designed for environments requiring self-directed decision-making and adaptability, such as autonomous vehicles, financial trading and AI-driven cybersecurity operations.

The AI Decision-Making Framework

Before investing in AI, ask yourself:

1. Does this solve a problem AI is uniquely suited for?
2. Do we have high-quality, representative data to support it?
3. Will this provide long-term ROI beyond the hype?
4. Are ethical concerns and sustainability addressed?
5. Have we chosen the right AI approach (traditional ML, GenAI, rule-based automation, or Agentic AI)?

Leaders who take a thoughtful, purpose-driven approach will be the ones who truly capitalize on AI’s potential for their businesses. Now ask yourself one final question: How is your company ensuring AI adoption aligns with real business value?

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?