Successful businesses have goals figured out with roadmaps and contingency plans for years to come, these goals support the mission and vision of the firm and are usually unique. The first thing is to see how AI can help your organization achieve these business goals quickly and more efficiently.

2. Readiness for AI:  

Assessing the prevailing technology landscape and skills of existing employees would help identify areas where improvement is required. Many organizations have written objectives and technology initiatives without the technical knowledge to implement them. It’s no surprise that 65% of artificial intelligence initiatives fail (Gartner, 2023). 

3. Invest in a Team That Works Across Domains:

However acute, these resources will become the key to the success of an AI program. Investing in training or hiring resources with data sciences, machine learning, and API-based AI skills is vital. Doing only that won’t be enough as you’d need to develop cross-functional teams where business people can collaborate with data engineers to implement these initiatives. 

4. Quality Data:  

After the first three milestones, you are in a prime position to select and operationalize use cases, although quality data is required for successful execution. Weak AI also known as narrow AI is dependent on size and quality of data. 

5. Data Governance + Ethical AI :

Of course, a solid data governance mechanism ensures accuracy, ethical controls, and security. An added feature that we feel should be a part of overall governance with AI has to do with its Ethical and responsible use while avoiding biases and following data regulations of the region.

6. Correct AI Technology:

While it works wonders for large data sets, an AI use case for one business may not be suitable for another. However, some Narrow AI implementations can be used by multiple organizations in marketing, customer services, fraud, and financial domains. Your business and Ethical considerations will determine how you can use data with available AI platforms and capabilities.

7. Start Small and Scale Steadily:

Testing waters before full adoption is always the way to go when it comes to technology investments. It is recommended to run tests and pilot projects before betting big on AI.

8. Continuous Learning:  

The creatives will identify opportunities to embed AI into business functions, to continue to leverage upcoming innovations, the firm should inculcate a continuous learning environment and encourage experimentation.    

9. Monitor and Evaluate:  

Develop KPIs to measure the impact of AI, with the willingness to change or adjust based on outcomes and results.   

10. A Culture of Innovation:    

Technological advancements that don’t support business growth are useless and yield no value for shareholders. Encourage innovation on both sides of the aisle, bring tech and business together to maximize the impact of innovation, encourage retakes and mistakes, learn from them, and keep up to date with what’s happening in the AI space.