5 days ago
When To Use AI Vs. Human Judgment: A Framework For Tech Leaders
Haider Ali is the CTO of WebFoundr, delivering fully managed digital services with expertise in AI, cloud infrastructure and cybersecurity.
Today, artificial intelligence (AI) use is ubiquitous: Phone users utilize AI for scheduling appointments, kids engage with AI to generate images for a school project and business owners leverage automation tools for new efficiencies.
A recent McKinsey poll indicates that AI is being used in 78% of organizations in some form or another. But although AI excels at scale, speed and data, human judgment still plays an irreplaceable role. The problem is that adoption isn't the same as alignment. Tech leaders must figure out when to capitalize on AI and when to rely on human judgment for innovation and ingenuity.
The benefits of AI are still being discovered, but clear guidance on setting boundaries using human oversight is equally crucial to shaping business futures. The goal of this article is to provide a proactive framework that enables tech leaders and business owners to understand when to utilize AI and when not to.
The Strengths Of AI
AI is exceptionally successful at standard pattern recognition. It's leveraged by industries ranging from healthcare to financial securities. As long as the data being fed into the AI is clean and structured, cybersecurity, diagnostics, communication, marketing and a wealth of other tools are optimized.
The trick is setting repetitive or rule-based tasks rather than creating concept-based parameters. The 2023 reported fraud losses by consumers reached around $10 billion (a 14% increase from 2022). Those numbers are only higher when considering businesses. Although AI can implement advances in fraud detection, prevention and security threats, it must have clean data and complex implementation driven by human insights. The systems must be trained using human knowledge.
And, thus, a continuous cycle occurs: AI is fed clean, usable data to protect core systems better and complete tasks efficiently. As a result, predictive analytics grows based on structured data. Humans can direct this data and utilize the analytics for a broad spectrum of advantages, such as fraud detection, A/B testing and proactive maintenance.
The Limits Of AI
We must remember AI isn't a human mind. It acts as a mirror, utilizing data provided by technicians and experts. Whenever that information is incomplete or not put in proper context, the results are often flawed.
Gender bias was a significant problem of AI-enabled hiring systems from 2018 to 2020. A recent article in ScienceDirect demonstrates how the same bias has infiltrated AI systems like ChatGPT. The social prejudices of the developers and data bleed into the AI structures implemented across modern businesses.
Systems can't (currently) provide context to any task requiring empathy, creativity or social nuance. That's a benefit in some regards with big data analysis, but not so much when a patient is in their sickbed or an office manager needs to hire team members.
Whenever AI encounters fragile social shifts or massive geopolitical changes, it can't adapt as well as humans. The irreplaceable human mind is required because of its understanding of ethics, empathy and adaptability.
The Human Judgment Advantage
There's a strong argument for the mass integration of AI into business processes. It can save an organization money and time by improving inefficient systems and decision making. However, humans must remain at the heart of consequential decisions to ensure responsibility.
Human decision making includes context, experience and moral reasoning. That gets around any concerns over the interpretation of ambiguous inputs or datasets. Intuition and values must guide choices as leaders address unpredictable environments.
A customer service agent can't apologize for a bad client-facing outcome by saying, 'It's the AI's fault.' Accountability must be present from the top down, with real human beings engaging in honest conversations to drive better outcomes.
Around 80% of C-suite executives think that AI will create a cultural shift where teams become more innovative. These tools must be used to augment capabilities, not replace human oversight in critical decisions, especially when public trust is at stake.
A Simple Framework: 'AIM'
Given these concerns, a framework is required to strike a balance between the benefits of AI and the fair use of human leadership and oversight. One such structure is known as AIM. It provides the following adaptable framework:
• Automate repetitive tasks through rule-based systems concerning big data resources (e.g., filtering spam, flagging anomalies and processing invoices).
• Involve humans in AI-assisted decisions. In other words, AI provides recommendations but humans make the final call (e.g., AI-assisted diagnostics, fraud alerts and investment recommendations).
• Manually manage any decisions surrounding ethics, empathy and brand perception so that 'gray zones' (e.g., employee terminations, product recalls and customer escalations) fully involve human tone and context.
The AIM framework isn't rigid for a reason. It can evolve with context and still provide a functional lens for leaders to evaluate how AI can be integrated by task rather than by overarching consequence.
Takeaway
AI isn't going away. But that doesn't mean trusting AI blindly when it comes to making leadership decisions or engaging in broad thinking.
It's better to consider AI adoption a collaborative process instead of a full handoff. AI brings benefits, but humans need oversight to ensure the direction and fed data meet any proposed challenge.
The future isn't AI vs. humans. It's one where AI is used alongside wise human judgment.
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