Artificial intelligence (AI) is transforming every industry and creating new opportunities for innovation and value creation. But how can you identify the most feasible AI opportunities for your organization or project? Here are some tips to help you evaluate the potential and viability of AI solutions.
The first step is to clearly define the problem you want to solve with AI. What is the current situation, the desired outcome, and the gap between them? What are the key metrics and indicators that measure the problem and the solution? How does the problem align with your strategic goals and priorities? By defining the problem, you can narrow down the scope and focus of your AI opportunity.
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.
The next step is to assess the data you have or need to solve the problem with AI. Data is the fuel for AI, so you need to consider the availability, quality, quantity, and diversity of your data sources. How relevant, reliable, and representative is your data for the problem? How much data do you have or need to train and test your AI model? How can you access, collect, store, and process your data securely and ethically? By assessing the data, you can determine the feasibility and scalability of your AI opportunity.
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.
Establish a robust ethical framework for data governance. Define clear guidelines on data collection, usage, and storage, emphasizing transparency and user consent.
Quality before quantity... one can have loads of data that is not necessary important in the context of the defined problem. This makes the "available" data pool small, making it harder to train the AI to make proper decisions. It is also important to feed in the proper "differentiators" and "commonalities" to ensure the algorithm is picking the right cues while arriving at a conclusion. We don't want the system to put snow dogs and wolves in the same category ??
Evaluating your available data is fundamental in AI. You must scrutinise its relevance, reliability, and representativeness for the problem you're tackling. Assessing the quantity and quality of data is essential, as it underpins the training and performance of your AI model. Consideration must also be given to the secure, ethical collection, storage, and processing of data, ensuring compliance with applicable regulations. Thorough data assessment illuminates the feasibility and scalability of your AI project, ensuring the initiative is built on a solid, ethical foundation, preventing pitfalls, and affirming that your investment is poised to yield significant value.
The third step is to explore the possible solutions that can address the problem with AI. What are the existing or emerging AI techniques and tools that can help you solve the problem? How do they compare in terms of performance, accuracy, complexity, and cost? What are the benefits and risks of each solution? How do they fit with your existing systems and processes? By exploring the solutions, you can identify the most suitable and effective AI opportunity.
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.
Expensive is not always best and cheap is not always faulty. With the myriad options available, one should check which model is best suited to the problem at hand, and more importantly, which approach should be taken to arrive at the desired outcome. Based on the data at hand and the optimization of the algorithm, narrow down on the most suitable opportunity.
The fourth step is to validate the value that your AI opportunity can deliver. How can you measure and demonstrate the impact and outcomes of your AI solution? How can you test and validate your assumptions and hypotheses with real users and customers? How can you iterate and improve your AI solution based on feedback and data? How can you communicate and share the value of your AI opportunity with your stakeholders and partners? By validating the value, you can increase the confidence and adoption of your AI opportunity.
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
Validating the value of your AI initiative is vital. This encompasses measuring impacts, testing assumptions with real users, and iterating the solution based on feedback. Effective communication of the AI’s benefits to stakeholders and partners is crucial, enhancing support and adoption. By demonstrating how the solution aligns with organisational goals and yields tangible benefits, you build confidence in its value. This thorough validation process reinforces the effectiveness of the AI, fostering trust and ensuring the initiative’s success and sustainability, thereby proving the investment worthwhile and aligning it with the broader organisational objectives.
The fifth step is to analyze the risks that your AI opportunity may entail. What are the technical, ethical, legal, and social risks that may arise from your AI solution? How can you mitigate or prevent them? How can you monitor and manage them throughout the lifecycle of your AI solution? How can you ensure the transparency, accountability, and trustworthiness of your AI solution? By analyzing the risks, you can reduce the uncertainty and challenges of your AI opportunity.
The final step is to plan the implementation of your AI opportunity. What are the resources, skills, and capabilities you need to execute your AI solution? What are the timelines, milestones, and deliverables of your AI project? What are the roles, responsibilities, and collaborations of your AI team and partners? How can you integrate, deploy, and maintain your AI solution in your operational environment? By planning the implementation, you can optimize the efficiency and quality of your AI opportunity.
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.