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When should one think of AI?

1. When should one think of AI?

We have covered the fundamentals of what makes an effective AI strategy. It is time to start designing one now. Let's build a perspective to develop successful AI solutions.

2. Separate hype from reality

Harvard Business Review has stated that, “AI hype can lead to overly high expectations, which could be misleading and can cause a high failure rate for ML business deployments.”

3. Separate hype from reality

Hence, it is essential to separate the hype that views AI as magic from reality and understand when and how to think of AI properly.

4. Traditional vs AI software development

To strengthen this understanding, let’s focus on unique considerations for strategizing AI implementation compared to traditional software. Traditional software follows predefined rules and has logic embedded in the code. Hence, its response is code-driven and deterministic, producing the same output for given inputs. On the contrary, AI systems do not need explicit instructions through code and are data-driven. They learn from the shifting data patterns and generate probability-based output. This is why a traditional software system such as a tax calculator produces the same output for a given input, while a given input prompt to an AI-powered system like ChatGPT might return a different result each time.

5. Traditional vs AI software maintenance

Maintenance for traditional software generally involves bug fixes and feature additions. However, AI models require extensive maintenance effort to keep refining with new data to ensure quality predictions.

6. Traditional vs AI software implementation

An effective AI strategy goes beyond the traditional software implementation lifecycle and includes a comprehensive understanding of the data landscape, starting from data acquisition,

7. Traditional vs AI software implementation

preprocessing,

8. Traditional vs AI software implementation

model training,

9. Traditional vs AI software implementation

deployment,

10. Traditional vs AI software implementation

and monitoring.

11. Additional strategic considerations for AI

To summarize, the continuously evolving nature of AI puts additional emphasis on model monitoring

12. Additional strategic considerations for AI

and error analysis

13. Additional strategic considerations for AI

to ensure the iterative model development.

14. Additional strategic considerations for AI

Further, it demands a closer look at everything associated with data, such as its quality, availability, and privacy.

15. Additional strategic considerations for AI

It should be well-represented to promote fairness and ensure transparency

16. Additional strategic considerations for AI

to facilitate model interpretation and validation.

17. Key business drivers

Now that we have the background on the distinction between AI and traditional software let's discuss the key business drivers to commence AI transformation. Firstly, it is important to know the stakeholders who are prioritizing this initiative.

18. Key business drivers

What else has taken a back-seat to advance this project?

19. Key business drivers

What teams, business units, or processes

20. Key business drivers

benefit from this initiative versus the extent of impact if it is left unsolved?

21. Key business drivers - alternatives

How long has this business problem been in existence? Is it identified recently? Is there an alternative, maybe a non-AI solution, such as simple business rules? How confident are we about the proposed solutions?

22. Key business drivers - dimension

Determining whether an AI solution caters to all business units, called horizontal, or addresses a specific vertical is integral to assessing its impact at scale.

23. Key business drivers - problem complexity

Is this problem complex and challenging? Is it a new technology that needs additional steps and processes before we officially kick-off with development? Are there any unforeseen challenges that could impede the progress?

24. Fear of automation

Great, we have discussed the hype and design thinking of AI strategy. The fear of losing jobs from advanced algorithms such as Generative AI is real. McKinsey reports that the future of work will have lesser demand for basic cognitive and manual skills, such as ones that involve a high share of repetitive tasks, data collection, and elementary data processing.

25. Change management

This instills fear of losing jobs to AI and requires effective change management to counter this.

26. Change management

It can be done by promoting AI literacy

27. Change management

that only redundant tasks are automated, not all jobs.

28. Change management

AI paves the way for more meaningful work streams and job creation.

29. Change management

Upskilling teams to prepare for shifting market dynamics and technological advancements.

30. Let's practice!

Ultimately, being quick to learn and adapt is key to thriving in the evolving AI landscape. Let's practice.

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