Why This Role Might Be Crucial in the AI Revolution

Understanding the Role of AI Analysts

As businesses look to harness the power of artificial intelligence (AI), a new and crucial position is gaining recognition: the AI Analyst. This role goes beyond the technical aspects of AI, focusing on the intersection of data analytics and business strategy. While much attention is directed at engineers and developers creating advanced AI models, the importance of AI Analysts in interpreting data within a business context is becoming increasingly clear.

The Gap Between Business Data and AI

Organizations have spent years structuring their data around various business applications. For instance, customer relationship management (CRM) data is tailored specifically for CRM purposes while enterprise resource planning (ERP) data serves only ERP needs. However, AI operates differently and requires a more adaptable approach.

According to Andy MacMillan, CEO of the analytics platform Alteryx, we are about to witness a major shift in how companies use their data. “What is essentially the world’s largest data preparation and transformation project is about to start,” he stated. Businesses need to rethink their data utilization to match the requirements of AI systems, which cannot simply digest existing data without additional context or understanding.

• Example of Sales Commissions: When querying a sales leader about commission plans, various contextual details such as team structure, region, and compensation plans need to be considered. AI systems must gather information from multiple data sources, including human resources for salary details, to gain an accurate picture.

Defining the AI Analyst

The primary function of an AI Analyst is to bridge the gap between technical data understanding and the practicalities of business operations. These analysts possess a blend of technical skills in data handling and a deep understanding of business dynamics.

MacMillan describes the ideal AI Analyst as someone who not only comprehends data but also understands what drives the business itself. Their role involves translating business processes into data workflows that can be efficiently processed by AI. They are adept at identifying the critical questions that need answering and ensuring the context needed for meaningful answers is available.

“For the next decade, there’s going to be a huge opportunity for individuals familiar with data sets and business operations,” MacMillan projected. “These analysts are business users, not merely data scientists, bridging the practical and strategic aspects of data usage.”

The AI Data Clearinghouse

Organizations also face governance challenges about what information should be made accessible to AI. There’s often a conflict between the desire to employ AI and the need to protect sensitive data. MacMillan highlights the importance of an “AI data clearinghouse,” which serves as a structured method of reviewing and approving data for AI interactions.

• Workflow Structure: Utilizing a formalized data workflow process, businesses can manage what data is shared with AI systems, ensuring compliance and security while promoting innovation.

Shifting From Dashboards to Dynamic Insights

AI is revolutionizing how businesses interact with analytics. Traditional dashboards required extensive human interpretation; however, AI can now generate narrative reports highlighting key insights automatically. This shift means receiving straightforward reports detailing performance metrics rather than simply numerical displays. MacMillan refers to this innovation as “Magic Reports” that provide contextual analyses rather than complex dashboards.

The Future Landscape of Business Analytics

The way organizations prepare data for AI signifies a significant evolution in their approach to information assets. Current AI applications often emphasize creative tasks with unstructured data, yet the real value lies in effectively applying AI to structured business datasets. This evolution raises strategic questions regarding the balance of in-house versus external AI technologies.

Embracing Change in Analytics Roles

While there are concerns about AI replacing jobs, there is also considerable opportunity for those in analytics who adapt to these emerging changes. The role of the AI Analyst underscores the importance of merging technological capabilities with human insights into business processes.

As organizations embark on this transformative journey, acknowledging the critical human factor in making AI operational will be key to unlocking its full potential.