Unlocking Generative AI 

When it’s game-changing (and when it’s not):

Business leaders today face a critical decision in an increasingly AI-powered world: how do you know when generative AI is the right solution for your business challenge?

Think of artificial intelligence tools as different types of assistants in your toolkit. Generative AI (like ChatGPT or DALL-E) is like a creative writer or artist - it can generate human-like text, images, or other creative content that feels original and personalized. Classical AI is more like your analytical math tutor - it's excellent at finding patterns in data and making predictions based on what it has analyzed. Rule-based systems are similar to following a detailed recipe - they work through clear "if this, then that" instructions to handle straightforward tasks.

Not every business problem needs the creative power of generative AI. Sometimes, the predictive abilities of classical AI or the straightforward logic of rule-based systems work better. For example, when you want personalized customer service responses, generative AI shines. But when you need to predict next month's sales figures or automatically reorder inventory when supplies get low, classical AI or rule-based systems often deliver better results.

This guide will walk you through a practical framework with real-world examples to help you determine exactly when generative AI will revolutionize your business processes - and when you should stick with other approaches. You don't need to worry about technical definitions. Instead, the examples throughout this document will help you develop an intuitive understanding of which AI approach fits your specific business needs.

A simple framework for determining Generative AI suitability

First, establish your business challenge precisely and your desired results. The evaluation of generative AI requires precise specifications to determine its appropriateness for a particular situation.

• Strong Example: The global insurance company determined their claims processing department used 40% of their work hours creating standardized response letters. Their objective was to "minimize letter drafting time by 75% while preserving both personalization and regulatory requirements."
• Weak Example: A manufacturing company aims to use AI to improve operations without establishing the specific requirements for generative AI application.

Generative AI demonstrates excellence in producing varied, creative, and unstructured outputs. Your desired results should include:

• Human-like text: Marketing copy, product descriptions, reports
• Conversational responses: Customer service, internal support
• Creative content: Design variations, innovative concepts
• Data transformations: Summarizing complex information, translations

Classical AI or traditional approaches will serve you better when you need to make predictions, perform binary decisions, or perform precise calculations such as demand forecasting or quality control.

Illustrative Example: A retail clothing brand generated thousands of unique product descriptions for their e-commerce platform. Generative AI performed well in this scenario because it generated numerous creative descriptions at scale, without compromising the brand tone. The company used classical machine learning models to forecast product sales by region although they used generative AI to create unique product descriptions.

To determine task complexity, use the following framework:

In-depth example: A healthcare provider needed to communicate with patients about appointment scheduling. They implemented rule-based automation to send simple appointment reminders containing standard information. Patient no-show prediction required their use of classical machine learning approaches. Generative AI offered the company a solution to create individualized care instructions which required human-like communication that suited each patient’s unique situation.

• Sufficiency: Do you have enough relevant examples for training or fine-tuning?
• Quality: Is your data clean, comprehensive, and compliant with privacy standards?
• Relevance: Does your data accurately represent the scenarios you're targeting?

Real-world example: A mid-sized law firm established a plan to leverage generative AI technology for creating basic standard contracts. The firm possessed a vast collection of past contracts but their analysis revealed multiple problems with outdated provisions and inconsistent formatting. Before applying generative AI they cleaned and standardized their contracts database which improved both the automated draft quality and their contract management system.

• Generative AI advantage: Situations requiring human-like fluency, creativity, and variety—such as drafting marketing emails or generating product concepts.
• Classical AI/rule-based advantage: Tasks demanding high accuracy, such as financial calculations, compliance verification, or quality control.

Illustrative example: A financial services company applied generative AI to produce customized investment newsletters about market trends that benefited from conversational language. Nevertheless, for portfolio allocation recommendations, they used classical algorithms with strict risk parameters and compliance guardrails.

• Accuracy concerns: "Hallucinations" or factual errors in generated content
• Compliance risks: Regulatory issues in healthcare, finance, or legal contexts
• Brand consistency: Maintaining voice and messaging standards
• Implementation challenges: Technical expertise, integration requirements
• User acceptance: Stakeholder comfort with AI-generated outputs

Real-world example: A pharmaceutical company was considering the use of generative AI to create patient education materials. They identified regulatory compliance as a critical risk factor and implemented a hybrid approach: generative AI created initial drafts, which were then reviewed by medical and legal experts before publication. This workflow reduced content creation time by 60% while maintaining 100% regulatory compliance.

• Define success metrics: Specific, measurable outcomes (e.g., 40% reduction in processing time, 25% increase in customer satisfaction)
• Establish baselines: Current performance levels for comparison
• Gather comprehensive feedback: From both users and recipients of the AI-generated outputs
• Be prepared to pivot: If results fall short, reassess your approach

Success story: A global manufacturing company tested generative AI for technical documentation, achieving a 70% reduction in documentation time and a 15% improvement in user comprehension. Positive results led them to expand this approach across other product lines.

Industry-specific examples to understand it better

• Best Generative AI use: Personalized sales follow-ups referencing customer pain points.
• Best classical AI use: Predicting prospect conversion.
• Real example: A B2B software company increased win rates by 23% with personalized generative AI proposals.

• Best Generative AI Use: Contingency planning for supply chain disruptions.
• Best classical AI Use: Optimizing inventory levels and delivery routes.
• Real example: A retailer achieved an 18% reduction in carrying costs through generative AI scenario planning.

• Best Generative AI Use: Developing advertisement variations for A/B testing.
• Best classical AI Use: Campaign analytics for optimization.
• Real example: Consumer goods company increased ROI by 32% through generative AI localization.

• Best Generative AI use: Personalized onboarding materials.
• Best classical AI use: Predicting attrition risks.
• Real example: Tech firm used generative AI for onboarding, classical AI for retention prediction.

• Generative AI best use: Plain-language financial reporting.
• Classical AI Best Use: Fraud detection.
• Real Example: Financial firm reduced fraud losses by 65% with AI-driven solutions.

More real-world applications to explain the difference