AI vs Human Tasks: When to Use Each Option

AI and humans excel at different tasks. Knowing when to use each is key to success. AI is great for repetitive, data-heavy, and precise tasks, while humans are better at emotional intelligence, creativity, and complex decision-making. Combining their strengths often leads to better results.

Key Takeaways:

• AI excels at:
  • Processing large datasets quickly and accurately.
  • Repetitive and consistent tasks (e.g., inventory management, quality control).
  • Analyzing patterns and making data-driven predictions.
• Humans excel at:
  • Emotional intelligence and empathy.
  • Creative problem-solving and nuanced judgment.
  • Ethical decision-making and leadership.

Quick Comparison:

AI vs Human Intelligence 2025: Which Will Drive Business Success?

AI vs Human Skills: Core Differences

Understanding the strengths of both AI and humans helps in assigning tasks more effectively. Let’s break down what each does best and why it matters.

What AI Does Best

AI systems shine in tasks that demand speed, precision, and consistency. For example, AI can solve ten mathematical problems in a single minute, while humans may take five minutes to solve just one.

Here are some areas where AI stands out:

• Pattern Recognition: AI identifies subtle patterns in massive datasets that humans might miss.
• Consistency: Unlike humans, AI doesn’t get tired or let emotions affect performance.
• Speed: AI processes data at lightning-fast rates, especially when dealing with large volumes.

What Humans Do Best

Humans bring unique abilities to the table that AI can’t replicate. These skills are especially valuable in situations requiring emotional intelligence, creativity, or nuanced judgment.

As Erik J. Larson aptly describes:

"Think of individuals as tools, whose brains are modules in a cognitive system much larger than themselves – a system that is self-improving and has been for a long time."

Humans excel in the following areas:

• One-shot Learning: Humans can learn from just a few examples, unlike AI, which needs extensive datasets.
• Emotional Intelligence: Humans understand and respond to emotional cues in ways AI cannot.
• Creative Problem-solving: Humans excel at combining diverse ideas to create novel solutions.

Comparing AI and Human Capabilities

The table below highlights how AI and humans differ in key areas:

Real-World Examples

Take Google DeepMind‘s AlphaFold team as an example. To achieve reliable predictions, the AI needed 150,000 initial protein structures and then generated over a million additional predictions. This shows how AI depends on massive datasets, whereas humans can often learn with far fewer examples.

On the flip side, AI’s limitations have been exposed in cases like Amazon’s AI recruiting tool, which developed biases against women candidates despite efforts to ensure neutrality. This problem arose from biases in the training data, demonstrating how AI can unintentionally amplify existing issues.

When AI and humans work together, the results are often better than what either can achieve alone. By letting AI handle speed and precision tasks, and humans focus on creativity and emotional intelligence, organizations can maximize the strengths of both.

How to Choose: AI or Human Tasks

Task Type and Patterns

When deciding between AI and human tasks, start by analyzing the nature of the work and how often it repeats. According to research from MIT Sloan, tasks that are repetitive, rely heavily on data, and require consistent pattern recognition are ideal candidates for AI automation.

Take Walmart as an example. They use AI for inventory management and pricing optimization, which helps them operate more efficiently.

This breakdown shows where AI shines and where humans are essential.

Human Skills Requirements

"Smart organizations will embrace strategic automation use cases. Strategic decisions will be based on how the technology will free up time to do the types of tasks that humans are uniquely positioned to perform."

Humans excel in areas that demand creativity, judgment, and emotional intelligence. Key tasks for human involvement include:

• Building and nurturing client relationships
• Solving unique and complex problems
• Making ethical decisions
• Handling intricate social dynamics
• Leading teams and driving organizational change

Understanding these strengths helps clarify how to balance automation with human expertise.

Resource and Scale Factors

Cost is another critical factor. For example, employing a human customer service representative costs about $37,780 annually plus benefits. Meanwhile, AI chatbot solutions range from $1,000 to $10,000 per month, depending on their complexity.

"It is important to acknowledge that in the customer service representative industry, where human agents are often stressed and overworked, AI is not a replacement for human agents but rather an augmentation for better employee productivity and efficient business operations."

Here are three key resource considerations:

• Implementation: Start small. Introduce AI for basic tasks, monitor its performance, and refine as needed.
• Scalability: AI can handle growing workloads without a proportional rise in costs.
• Cost-Benefit Analysis: Weigh the upfront costs of AI against the long-term efficiency it offers, while recognizing the irreplaceable value of human contributions.

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Tasks Best Suited for AI

Here’s a breakdown of tasks where AI stands out and delivers measurable results.

Data and Routine Tasks

AI is great at handling repetitive, time-consuming, and error-prone tasks. Research highlights how automation improves both accuracy and efficiency in business processes.

Here’s a quick look at how AI delivers in specific areas:

AI doesn’t just excel at routine tasks – it’s also reshaping customer service.

AI in Customer Service

With its ability to process data quickly, AI is transforming customer support by cutting costs and speeding up response times. A survey revealed that 91% of customer success leaders believe AI chatbots are effective for delivering support.

Real-world examples show the impact:

• WaFD Bank: Integrated generative AI to cut costs per interaction by 95%.
• Unity: Used AI to deflect 8,000 support tickets, saving $1.3 million while providing instant responses.

"With AI purpose-built for customer service, you can resolve more issues through automation, enhance agent productivity, and provide support with confidence. It all adds up to exceptional service that’s more accurate, personalized, and empathetic for every human that you touch." – Tom Eggemeier, Zendesk CEO

These examples highlight how AI can deliver faster, more effective, and personalized support.

AI Tools and Solutions

Businesses are increasingly using AI to streamline operations and improve performance. For instance:

• Talkdesk: Helped Memorial Healthcare System reduce call abandonment rates by three times and increase service levels by 30%.
• Carbon Health: Improved clinic answer rates by 40% and cut patient wait times using AI automation.

AI is also being used for tasks like:

• Managing email and text campaigns
• Scheduling social media posts
• Offering personalized product recommendations
• Processing invoices
• Monitoring supply chains
• Organizing documentation

McKinsey estimates that AI could contribute up to $4.4 trillion to the global economy by 2030, with marketing among the areas set to gain the most. With 80% of executives agreeing that automation can apply to almost any decision, AI’s role in business continues to grow rapidly.

Tasks Best Suited for Humans

While AI can handle repetitive tasks efficiently, there are areas where human expertise is irreplaceable. Nearly half of all job tasks still depend on human skills and decision-making.

Leadership and Planning

Human leaders bring qualities to strategic planning and organizational guidance that AI simply cannot match. They excel at managing multiple strategies and adjusting plans based on real-time feedback.

Research from Harvard Business School attributes this flexibility to our "notion of self", which allows leaders to:

• Adjust to change more effectively than AI
• Understand context and shift strategies as needed
• Make decisions informed by personal experiences

This is especially clear in executive decision-making. Studies show that even with the same AI recommendations, executives can differ in their investment decisions by as much as 18%, depending on their individual styles.

Building Business Relationships

Emotional intelligence (EI) plays a critical role in forming strong business relationships. Research by Symphony Talent highlights that companies now prioritize candidates with high emotional intelligence alongside technical abilities.

Here’s where humans have the edge:

"The age of AI doesn’t diminish the importance of human skills – it amplifies it."

• Kermit Randa, CEO of Symphony Talent

These emotional skills not only strengthen relationships but also help in making complex, high-stakes decisions.

Complex Decision Making

Human judgment is crucial when ethical considerations and complex social contexts come into play. This is particularly true in scenarios involving:

• Risk management and accountability
• Interpreting intricate social dynamics

Research shows that combining human judgment with AI insights leads to significantly better decision-making outcomes.

"Combinations of humans and AI work best when each party can do the thing they do better than the other"

Organizations can strengthen these skills through targeted training programs that focus on:

• Self-awareness and emotional regulation
• Motivation and empathy
• Communication and social skills
• Risk perception and critical observation

Combining AI and Human Work

According to McKinsey’s 2024 report, 72% of organizations have incorporated AI into at least one function. To make this work, businesses need a clear plan that leverages both AI’s capabilities and human expertise.

Team and AI Integration

Bringing AI into the workplace effectively requires open communication and careful planning. Automating tasks can save employees an average of 240 hours per year, with company leaders estimating potential savings of up to 360 hours annually.

Here are a couple of strategies to consider:

• Start Small and Scale: Begin with pilot projects to test AI’s potential. For example, PepsiCo found success by involving their teams early in the process.
• Focus on Outcomes: Shift metrics to prioritize results over effort. This helps employees see AI as a tool to enhance their work, not replace it.

"AI isn’t inherently supposed to replace humans – it’s more about extending human capabilities. Still, there are some cases where AI replaces humans, but nowadays, AI implementation is more characterized by collaboration between humans and AI." – Katerina Merzlova, Head of Sales and Marketing, SumatoSoft

Once teams and AI work together effectively, maintaining strong oversight becomes essential.

Managing AI Systems

Human oversight is critical to ensure AI systems deliver value. While AI offers speed and efficiency, human judgment remains key. Cisco‘s AI Readiness Index highlights a gap: although 97% of organizations feel the pressure to integrate AI, only 14% feel fully prepared.

"A lot of people are excited about new technology, others are fearful, and a lot of people fall in between. Talking openly about use cases, experiences with AI, pitfalls, risks, etc. helps with everyone’s comfort level, but also in ensuring that people are taking steps to minimize risks. As powerful as AI is, human-to-human communication and judgment will remain key in ensuring that AI is used safely, efficiently, and accurately." – Owen Wolfe

After setting up systems, the next step is preparing staff to use AI tools effectively.

Training for AI Integration

For AI to succeed in the workplace, employees need proper training and support. Research shows that 43% of professionals use AI tools for work, but only about a third have informed their supervisors.

To ensure smooth adoption:

• Use strong security measures like encryption and regular audits.
• Offer hands-on training sessions to build familiarity with AI tools.
• Appoint team members as AI champions to guide others and solve problems.

"The most significant key ingredient for success? Get your team to explore and play with these tools. The more they experiment, the more intuitive understanding they’ll develop of how these technologies can enhance their work." – Todd McLees

The goal is to create an environment where humans and AI work together, each amplifying the other’s strengths.

Conclusion

Integrating AI into the workplace is all about aligning tasks with the right set of skills. Research shows that while AI thrives in handling data-heavy tasks, blending it with human expertise leads to better results. This combination highlights how AI and human abilities can work together rather than against each other.

Even though many tasks can be automated, very few jobs can be fully taken over by AI. As Jacqui Canney, Chief People Officer at ServiceNow, puts it:

"In an augmented workforce, the traditional boundary between humans and machines disappears. This will require CHROs to take on a new and significantly expanded role of managing the joint performance of humans working more closely with smart machines"

To turn these ideas into action, organizations should focus on three key areas:

1. Strategic Implementation
   Instead of just reallocating tasks, companies need to rethink and redesign their processes. Right now, only 36% of large global companies report taking steps to adapt talent practices for a human-machine workforce. This shift demands thoughtful planning and regular reviews.
2. Workforce Development
   Despite the growing need for AI integration, only 5% of leaders strongly believe they are investing enough in reskilling their teams. Companies must prioritize training programs and create clear paths for effective collaboration between humans and AI.
3. Ethical Considerations
   Strong oversight frameworks are essential. Leaders should implement regular AI audits and ensure human judgment remains central in critical decisions. As Professor Matthias Klumpp from the University of Göttingen explains:

"There will also be many scenarios and uses in the future where mixed teams of robots and humans are superior to entirely robotic machine systems. At the least, excessive fears of dramatic job losses are not justified from our point of view"

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