Irreplaceable Human Intelligence

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Advancements in artificial intelligence (AI) have created many opportunities for companies to augment resource gaps. Generative AI has captured every business leader’s attention. Well-designed and trained models are capable of creating solutions that humans might not think of. They could distill insights from massive volume of data quickly.

Does that mean AI could replace human intelligence? It certainly is a possibility one day. However, humans remain in the driver’s seat in orchestrating how AI is deployed.

AI strategy

Successful AI deployment builds on identifying how a company could use AI to improve business outcomes. Managers and senior leaders determine where to invest.

A chatbot minimizes the need for live agents and improves the response time to customer inquiries. ChatGPT saves staff time in drafting marketing emails and reports. Better customer experience and time savings from efficiency have different benefits and returns.

Apart from developing the AI strategy, humans design how the solution would work. They create the business rules and use cases so AI could deliver optimal results.

Data implications

AI technologies need data to run the algorithms. Hence, data quality impacts the next task to be carried out and the output.

For generative AI, excluding certain data could create output based on an incomplete picture. Data that have been compiled for specific purpose might have embedded assumptions and unintended bias skewing the output.

Human intelligence is needed to understand source of data and what is used in order to identify problems. Algorithms simply use what humans determine to feed into the model.

Critique of output

Humans gain experiences from their personal and professional interactions every day. These experiences become perspectives that are incorporated into decision-making. It is difficult for an algorithm to emulate these experiences.

An AI model follows rules built into its design, the output generated is based on the data provided. Given the potential biases inherent in the data, it is critical for humans to challenge the output.

This does not imply that AI is not good at what it is designed to do. However, it is wise to have humans look at the output, evaluate its applicability, and adapt as necessary.

Change management

The most common change management practice for technology adoption is training. While training might be adequate to help employees with learning the new tools, buy-in and mindset alignment require the human touch.

Uncertainties around job loss, changes in roles and scope of work generate anxiety for employees. Some employees are ready to adjust while others could be apprehensive about change.

Proper change management includes changing beliefs and behaviors. Leaders need to invest time to communicate, built trust in-person on why there is an urgency for change and how they will support employees through the transition.

Technologies can perform tasks that humans used to do. Though technologies can do them faster with better accuracy, and likely more economically, there is work where human intelligence remains irreplaceable.

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