Using AI to Solve Complex Tasks

Maurice Tony Ewing

20 years: AI and Digital Transformation

In this video, Maurice explains how to collaborate with AI by leveraging its strengths in data processing while using human creativity and intuition. He covers task structuring, prompt engineering, and the importance of human oversight for accurate, effective results.

Working with AI

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Using AI to Solve Complex Tasks

6 mins 9 secs

Key learning objectives:

Understand how to break down complex problems for effective AI collaboration
Understand AI’s strengths in data processing and automation, and human strengths in creativity and intuition
Outline structured steps to optimise AI outputs, including prompt engineering and token limit management
Understand the importance of human oversight in ensuring AI-generated insights are relevant and effective

Skills:

Technology-driven Advancement

Impacts:

Operational Efficiency

Overview:

Effectively using AI for complex problem-solving requires breaking tasks into smaller components and distributing them based on AI and human strengths. AI excels at data analysis, automation, and pattern recognition, while humans provide creativity, intuition, and emotional intelligence. Steps for optimal AI collaboration include defining the problem, dividing tasks, using precise prompts, managing token limits, and refining responses. AI should be guided to avoid its weak areas and deliver structured, relevant insights. While AI cannot replace human judgment, combining its capabilities with human oversight ensures more efficient and effective problem-solving while working within its limitations.

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Summary

How can AI assist in solving complex problems?

AI helps by handling data-heavy tasks, retrieving information, analysing trends, and automating processes. It can assess branding, research trends, and generate data-driven insights, while humans provide strategic thinking and emotional intelligence. AI enhances efficiency but requires structured human guidance to ensure relevance and alignment with objectives. Effective AI use involves understanding its limitations and ensuring human intervention where necessary.

What steps should be followed to effectively collaborate with AI?

To collaborate effectively with AI, start by thoroughly defining the problem, identifying complexities, and structuring it into smaller tasks. Next, assign tasks based on AI’s strengths in data processing, automation, and pattern recognition, while humans handle creativity, emotional intelligence, and strategic thinking. Use prompt engineering to refine AI-generated responses, ensuring clarity and relevance. Manage token limits to prevent truncation or loss of context. Continuously evaluate AI’s outputs, verifying accuracy, adjusting prompts as needed, and incorporating human oversight to align results with business goals and contextual nuances. Effective collaboration combines AI’s efficiency with human judgment for optimal outcomes.

How can prompt engineering improve AI’s effectiveness?

Well-structured prompts help AI focus on relevant aspects of a problem while avoiding areas where it struggles, like emotional nuance or subjective creativity. Providing examples of desired responses, requesting clarifications, and ensuring AI acknowledges all aspects of the query can enhance its output. Effective prompting reduces misinterpretations and improves the quality of AI-generated insights.

Why is human oversight necessary when working with AI?

AI lacks contextual understanding, emotional intelligence, and true creativity. Humans must refine AI outputs, ensuring they align with strategic goals and nuanced considerations. By reviewing AI-generated insights, adjusting responses, and incorporating human judgment, users can optimise AI’s contributions while mitigating its limitations, resulting in more accurate and effective solutions.

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Maurice Tony Ewing

Maurice Tony Ewing is a seasoned professional in training, consulting, and executive coaching, having advised and trained thousands of executives and managers across over 60 countries. With a background in banking and board membership, Maurice runs his own consulting firm, Conquer Risk, and teaches data analytics at Columbia University. His most notable accomplishment is leading the data science team that created the world's first mobile-lending platform, which was crucial in Google's creation of "Bebapay" and the NFC payment and lending process. Since then, Maurice has helped companies launch digital initiatives and build strategic, governance, risk, and compliance frameworks. He aims to bring his practical experience and technical knowledge to these learning streams to assist others in learning.

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