How can you identify the most feasible AI opportunities?

Actually I believe that it depends on the situation. You can start with a problem definition e.g. if the company/a department/a team has done a SWOT analysis, but sometimes it's very useful to simply start from what data is available and "untapped" today in the organisation to keep a complete open mind as to which opportunities could then be addressed by using an AI based on that data.

In my experience defining the problem is the most difficult, because people tend to think for the solution even before knowing the exact problem they try to resolve. I would say think about the problem and the following questions: - why it is important? - is it of strategic importance and if yes/no, why? - how the future will look like when the problem is resolved? What is the picture of success? And very important - the solution is coming after defining the problem. It could very well be that you dont need AI to resolve the problem, the solution might be way more simple.

Extend problem definition beyond immediate concerns. Consider the broader ecosystem, potential ripple effects, and interconnected challenges.

Think of a complex issue where there are multiple options for a solution.Then add the human overlay of judgement based on a life-time of experience which AI lacks

Greatest Russian physiologist Ivan Pavlov proposed that conditional reflexes signal concrete reality surrounding human and life being. Once we have abilities to analyze psychological response in real time together with chemical genetics, it woud open a new window for a health care, a longevity and medical revolution.

I wish people would stop treating AI like something other than what it is: efficient application of statistical methods via computation. It's not magic.

Establishing clear data ownership, access controls, and privacy measures ensures ethical data usage and compliance with regulatory requirements. It is essential to consider how data will be managed, protected, and accessed throughout the AI project's lifecycle. Further data quality must not be overlooked. While the quantity of data is significant, its quality is equally crucial. Accurate, complete, and consistent data is the bedrock upon which reliable and effective AI models are built. Data cleaning, normalization, and transformation may be necessary, and these preprocessing steps should be factored into your project plan.

Explore how machine learning, natural language processing, and computer vision can complement each other to create a more robust and versatile solution.

Exploring potential AI solutions is a crucial step. This involves investigating existing and emerging AI techniques and tools, assessing their performance, accuracy, complexity, and cost. A balanced evaluation of the benefits and risks of each option is vital. Additionally, examining the compatibility of these solutions with existing systems ensures seamless integration. This exploration is pivotal for identifying the most effective AI opportunity, aligning it with the specific needs of the problem and the broader organisational goals, thereby optimising the likelihood of successful implementation and realising tangible benefits.

From my experience the best way to communicate the value to stakeholders & partners is the financial impact of the solution on the company bottom line on short and long term.

The best results of the AI solution will be with actual data.. generated by actual end users. To ensure that the AI is learning the right things, get the end users to be a part of the test run.. give them incentive to he as genuine as possible and have the AI process it. This will give better insights on what the AI solution can do, what is the value addition and what needs to be improved. These insights can selectively be shared with the stakeholders to show them the outcomes that AI can deliver and how these outcomes add value. This makes it easier to get everyone on board

Planning the implementation is the pivotal final step in realising your AI opportunity. This involves assessing necessary resources, skills, and defining clear timelines, milestones, and deliverables. Establishing roles and responsibilities within the AI team and among partners is essential for fostering collaboration and ensuring smooth execution. Developing strategies for integrating, deploying, and maintaining the AI solution in your operational environment is crucial. Through meticulous planning, you optimise the efficiency and quality of your AI opportunity, aligning it with organisational goals, and ensuring it delivers enduring value, thereby making the investment worthwhile and impactful.

Identifying the most feasible AI opportunities for your organization or project requires a strategic and structured approach. To begin, it's essential to clearly define your objectives. What specific goals do you want to achieve through AI? Having a precise understanding of your organization's mission and vision will guide your search for feasible AI opportunities. Next, conduct a thorough needs assessment within your organization or project. Identify the specific challenges or needs that AI could potentially address. Consider areas where automation, data analysis, or predictive capabilities could enhance efficiency, productivity, or decision-making processes.

In summary Identifying the most feasible AI opportunities involves several steps: 1. Problem Identification 2. Market Research 3. Data Availability 4. Technical Feasibility 5. Resource Assessment 6. ROI Analysis 7. Ethical and Legal Considerations 8. Proof of Concept 9. Scalability 10. Risk Analysis 11. Feedback and Iteration 12. Sustainability By systematically considering these factors, one can identify the most feasible AI opportunities and increase the likelihood of a successful AI initiatives.