Learn When to Leverage AI And When Not To

There is no question that AI is reshaping how businesses operate and solve complex challenges. The latest McKinsey Global Survey found one-third of respondents use Generative AI regularly in at least one business function.

But how do you know when to leverage AI and when not to leverage AI? As AI takes center stage, understanding its capabilities and limitations is crucial for effective enterprise application. Keep reading to understand when AI makes sense.

What problems can AI solve?

AI can solve a wide range of business problems across different domains and industries, but one thing they have in common is that when done right AI solutions make our lives easier and our output more consistent and of higher quality. Here are some examples of how AI can help:

1. Data Overload and Analysis

   Problem: Businesses struggle with the amount of data being produced every day, making extracting meaningful insights a challenge.

   Solution: Analyze datasets via machine learning. Identify trends, patterns, anomalies and then take informed decisions for improved efficiency, customer experience, product development.

2. Inefficient Customer Support

   Problem: Providing prompt, efficient customer support can be difficult, especially with high call volumes.

   Solution: Chatbots and virtual assistants can handle routine queries, allowing human agents to focus on complex issues. AI chatbots can also operate 24/7, ensuring constant customer assistance availability and lower overhead costs for the organization.

3. Personalized Marketing

   Problem: To stand out in today’s competitive market marketing experiences across different channels and touchpoints need to be more personalized.

   Solution: Leverage customer data platforms, machine learning models and recommendation engines to analyze customer data, behavior patterns, and preferences.

4. Cybersecurity Threat Detection

   Problem: Traditional security measures are often inadequate in identifying and mitigating threats at the speed in which malicious activity moves.

   Solution: Machine learning algorithms can continuously monitor network traffic, user behavior, and system logs, detecting anomalies and potential threats in real-time. AI-driven security solutions, like ProArch’s Managed Detection and Response services, can adapt and learn from new attack patterns, providing proactive defense against evolving cybersecurity risks.

5. Predictive Maintenance

   Problem: Unexpected equipment failures can lead to costly downtime and production losses in manufacturing and industrial settings.

   Solution: AI and IoT sensors can monitor equipment performance, detect patterns indicating potential failures, and predict when maintenance is required. Predictive maintenance optimizes resource allocation, reduces unplanned downtime, and extends asset lifespan.

6. Efficient Resource Allocation

   Problem: Allocating resources effectively across various business units, projects, and operations can be challenging, leading to inefficiencies and suboptimal utilization.

   Solution: Leverage predictive models. Analyze historical data, requirements, availability. Optimize scheduling, staffing, budgeting. Ensure efficient resource utilization.

7. Intelligent Process Automation

   Problem: Many businesses still rely on manual, repetitive tasks that are time-consuming and prone to errors.

   Solution: Automate various business processes by combining robotic process automation (RPA) with machine learning and natural language processing (NLP). This can handle complex tasks, adapt to changing conditions, and learn from experience, significantly improving operational efficiency and reducing manual errors.

Real-world AI Use Case for Better Data Quality

One ProArch client’s ecommerce app pulled product data from hundreds of databases to determine optimal product placement based on ingredients and other factors. However, data quality issues like spelling errors and inconsistent branding updates made locating accurate real-time information challenging.

ProArch implemented an AI solution to clean and enrich data across all the retailer’s databases. The AI model identifies errors, fixes inconsistencies, and maintains data accuracy and the final step is human verification.

Previously, the retailer spent 3,000 hours per month on manual data verification. With ProArch’s AI solution, this dropped to just 600 hours – an 80% efficiency gain while ensuring customers have accurate product data.

Problems AI Can’t Solve

Yes, there are problems AI may not be a fit for like tasks requiring genuine creativity, emotional intelligence, or human intuition. Ethical decision-making in complex, ambiguous situations and transferring knowledge between vastly different domains can also pose challenges for AI systems. However, one of the most prevalent challenges AI does not solve is – the lack of quality data.

As Lakshman Kaveti, AI Lead and Managing Director of Digital Engineering at ProArch, puts it, “If your data is messy and disorganized, any AI solution will be useless—an expensive gadget that doesn’t actually work.”

Preparing data for AI is crucial. Organizations should prioritize the few matters to be prepare for AI initiatives:

• Establish a comprehensive data governance framework, including policies, standards, and decision-making processes.
• Implement a cloud data strategy to centralize data assets.
• Invest in data quality and cleansing initiatives to improve the accuracy of data feeding into AI models.
• Break down data silos to enable seamless data access across the enterprise with a data platform.
• Clearly assign data stewardship roles and responsibilities with designated "data owners."
• Roll out master data management (MDM) to ensure uniformity across enterprises.

You need clean, accurate, high-quality data before you’re ready for AI. If you lack that, Lakshman’s advice is blunt: “Don’t embark on an AI journey at all. Focus on your data strategy first.”

He recommends seeking external validation for proposed use cases, stating, “Don’t be afraid to bring in a consultant or AI consulting company to validate if this can actually be done.”

Another problem AI won’t solve is a process that isn’t already working. If the proposed idea or process is unlikely to perform as expected, especially in areas like marketing campaigns, sales outreach, and customer service procedures.

First, assess the effectiveness of existing strategies and workflows. If they are underperforming or flawed, deploying AI may only exacerbate issues rather than resolve them. Evaluate processes, identify areas for improvement, optimize them, and then consider integrating AI technologies.

If your challenge aligns with AI’s strengths while meeting ethical guidelines around data privacy and safety, exploring AI solutions could unlock immense business value.

ProArch’s AI consulting services guide businesses through the entire AI journey – from identifying optimal use cases to deploying robust, ethical AI solutions tailored to their needs.

Reach out to ProArch for guidance on your AI strategy, tools, and AI policy.