Dos and don'ts of AI implementation
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Dos and don'ts of AI implementation

Machine learning (ML) and artificial intelligence (AI) have the potential to be invaluable assets for company success. Businesses can automate hours of tedious data-sifting work to enable quicker and wiser business decisions by deploying AI. Automation and AI do not, however, eliminate the requirement for human accountability. To ensure AI benefits rather than hinders your organization, it's crucial to adhere to best practices. Here are five blunders to avoid while using AI to achieve further business objectives.

Valid use cases

Many organizations are already aware of the advantages of AI. If your business doesn't use automation in some capacity, you're undoubtedly lagging behind your rivals. A PwC poll found that 86% of respondents anticipated AI becoming a "mainstream technology" in their company.

Despite the widespread use of AI, haphazard attempts to integrate AI into your organization are unwise. For the best results, employing AI in good use cases is crucial. Instead of thinking, "Can I use AI in this situation?" consider, "Am I using the correct AI in the right situation?" It must eventually be cost and time-effective for a company to deploy AI for some business activities. If AI operations conflict with business objectives, time and company resources will be lost.

Talent pool

The hiring environment is evolving. Nearly 50% of tech recruiter respondents to a recent CodingGame study claim they are having trouble filling available positions. The necessity for vigilance in the hiring process, especially in AI, is crucial as employment in the tech industry gets increasingly challenging.

Hiring for AI is similar to creating a good team. You shouldn't hire similar specialists as you need many different tasks to be undertaken by various experts. Don't hire only data scientists. Consider a candidate's specialized experience and skill sets and match them to your company's requirements. For instance, detailed study and solution creation require a profound understanding of modeling, while the solution requires data engineering knowledge.

Data management

Data is the basis of all AI-related business objectives because it powers AI engines. Neglecting to protect their data is one of the biggest errors businesses make. This starts with the false assumption that the IT division is responsible for all data management. Business subject matter experts and data scientists should be included before data is collected and entered into AI systems. Executives should offer oversight to ensure the correct data is collected and preserved effectively. Non-IT staff must understand that their experience is a crucial input to the AI system and that good data helps provide high-quality AI recommendations. Assure that the duty of collecting, analyzing, and managing data is shared by all teams.

Data management practices are a crucial part of data care. Data management and governance must change processes to handle the increased volume, velocity, and variety of data while maintaining compliance with governmental and business requirements. This includes procedures for regular evaluation and accountability, as well as data gathering and preservation.


AI needs human intervention to continue to function as a successful long-term solution. For instance, AI processes must adapt if AI acts improperly or if business objectives change. Inaction or inadequate involvement may lead to AI recommendations that interfere with or work against company goals.

Think about AI-based pricing models, for instance. If the AI system is not designed to adapt to market changes, its effectiveness will decline. In other words, when the nature of the source data changes, the AI system must adjust to fit the market's needs.

Performance of the sales team is one technique to gauge the usefulness of AI. Effective sales teams should be willing to evaluate their performance based on how well they implement AI that creates value because they want to comply with pricing recommendations to help them accomplish their objectives. Profit margin and revenue are typical KPIs for pricing-related metrics. Another benefit of tracking KPIs is knowing which sales teams or team individuals are utilizing AI. It might be time for intervention if the recommendations do not promote KPIs' accomplishments.

Intervention should be scalable and repeatable through highly automated methods to lighten the load on AI users. The AI system's inputs should be reviewed, and the output should be checked to make sure it is what was expected. All of these activities ought to be done consistently all year long. Don't wait for AI to break down before you step in; your margins might have been compromised by then.

Biased data

When presented with a small or unrepresentative dataset, AI systems, and their results might be prejudiced, just like humans. (This is true for both descriptive analytics and AI models.) The existence of biases and the subsequent analysis of those biases frequently have little to do with the goals of AI. Therefore, the gatekeepers of the AI, not the AI itself, are frequently to blame when the effects of such prejudices manifest.

As was already mentioned, data and intervention are crucial elements of a successful AI application. This is particularly true when biases in AI are discovered. But it's always preferable to avoid an issue than to deal with it. Avoid using data that can unintentionally be prejudiced against a particular race, gender, class, etc. For instance, modeling focused solely on consumers' location and income may result in skewed results.

Explainable AI can help eliminate or correct biases. Explainable AI techniques can pinpoint the primary causes behind an AI model's predictions or recommendations, making intervention much simpler. The intervention must be quick, repeatable, and scalable once explainable AI methodologies have demonstrated how AI produces biased outputs to prevent future negative effects on your organization and your customers.

When used properly, AI may be a priceless resource for your company. The impacts can range from a higher return on investment to achieving business objectives to satisfied clients. Your ability to apply AI strategically and create rules to steer clear of frequent blunders will enable you to expand both your business and your AI implementations at the same time.