Over the last four years, interviewing hundreds of AI researchers and AI enterprise leaders, we’ve consistently heard the same frustrations about AI adoption said time and time again.

“Culture is hard to change.”

“Leadership doesn’t know what they’re trying to accomplish.”

“Nobody knows what to do with these data scientists we’ve hired.”


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Implementing AI To effectively leverage generative AI in your business, it’s crucial to identify specific areas or challenges where AI can make a difference.

Identify Business Needs:

  1. What are the core functions of your business and the primary challenges faced in each?
  2. Which of these challenges, if solved, could lead to a significant positive impact on efficiency, revenue, or customer satisfaction?
  3. What is your current IT and technical infrastructure?
  4. Are there systems in place that could support AI integration, such as cloud platforms or high-performance computing resources?

Find the Correct Use-Case:

  1. Are there tasks within your business that are repetitive, time-consuming, or prone to human error?
  2. Which business processes involve significant manual input or decision-making based on large datasets?


Gather Data :

  1. What types of data (e.g., transactional, behavioural, operational) does your business generate, and how is it currently stored and managed?
  2. Are there existing data analytics or machine learning models in place? If so, what are their primary functions?


Choose the Right Tools:

  1. What AI features align with your business needs?
  2. How would you rank these factors for AI tool selection: ease of use, scalability, cost, support, versatility, integration.
  3. Does your team have AI expertise? If not, are funds set for external consultation or hiring?


Pilot the Implementation:

  1. If you were to pilot an AI solution, which specific business function or process would you target first and why?
  2. How do you envision the integration of the AI solution with your existing systems? Are there potential challenges or roadblocks you foresee?

Evaluate & Iterate:

  1. How would you define success for the AI implementation in terms of ROI, efficiency gains, or other metrics?
  2. Are there mechanisms in place to measure and track these metrics over time?

Continuously Evaluate and Update:

  1. What feedback mechanisms will you have in place for your organisation to report issues or suggest improvements?
  2. How will you stay informed about the latest advancements and updates in AI technology relevant to your solution?
  3. In case of significant updates or changes to the AI solution, what’s your strategy for communicating and training your team on these updates?


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 Zsystems AI Team