How do you manage AI project opportunities?

First, I usually say that an organization should identify the potential AI use cases in order to evaluate the cost benefit and the ones that are worth pursuing as AI projects.

A great measure to use is the "added value" metric. You can use this metric to determine if your AI project is worth pursuing but you can also use yhis metric to rank the different AI opportunities and priorities AI project.

One thing I've found helpful in assessing the feasibility of an AI project is determining the cross functional "value add" from an AI project so that there's a "buy in" from a wider group of stakeholders.

Here's how to ensure success: 1?? Clear Objectives: Align business goals with AI capabilities for focus. 2?? Data Prowess: Gather, clean, and use quality data to avoid biases. 3?? Expert Dream Team: Collaborate with AI specialists and domain experts. 4?? Risky Business: Identify and mitigate potential risks. 5?? Continuous Learning: Adapt and improve AI solutions iteratively. 6?? Ethics and Privacy: Prioritize user trust and data compliance. 7?? User-Centric Approach: Understand and improve based on user feedback. 8?? Regular Evaluation: Assess progress against milestones. 8?? Regular Evaluation: Regularly assess the project's progress against set milestones, making necessary adjustments to stay on course.

Is usually good to make a small workshop with the business stakeholders to figure out which projects are worth pursuing and which are better left for a later stage.

Don't Forget the User. Often we are seeing AI implementations that are well thought and planned but forget the end user. Sometimes a new searching tool or AI assisted system really is not as useful as other implementations. Keep a clear understanding of empathy for end users and a project will see greater success.

The scope of the AI project has to be in alignment and support to the business strategy. We need to include resources and capabilities in the scope as the biggest obstacle to adopting AI is the lack of AI talents.

Even before thinking about algorithms, models, and solutions, the scope should start by a thorough understanding of user needs. Starting from a clear definition of the problem you want to solve you can design a solution architecture, and scope, which will tackle the need and which can be as fine grained as you need it to be. Many projects get stuck because needs are not clearly understood and expectation gaps arise.

Start with a Pilot Project - Start small, manageable project that you can use to learn and iterate. This will help to avoid making costly mistakes and will give you a better chance of success. AI projects require a team of experts with different skills and experience. It is extremely important to ensure a team that includes data scientists, engineers, and business experts.A strong team with a diverse set of skills will be better able to overcome challenges and deliver results.

Just be sure to use an agile methodology fit for AI model iterations. Many times agile methods don’t have the flexibility to create models and do constant experiments

Working in sprints is critical for any AI based project, with how rapidly the advancements of AI technology the adoption of a specific tool, algorithm, or system. Having clear epics and tasks are standard practice, however the critical step that most agile teams are missing is research stories. Allowing time for developers and leaders to research and understand a open source or closed source tool is vital to any long term success.

**Tip: Embrace Agile with Asynchronous Communication ??** To excel in AI projects, adopt an agile system with asynchronous communication. This empowers teams to adapt swiftly, focus on deep work, and accommodate remote collaboration. Balance synchronous check-ins with asynchronous updates for efficiency and transparency. Embracing this dynamic approach fuels innovation and drives success!

Do not adopt an agile methodology. Apply the methodology that is fit for the specific purpose. If that is agile, go for agile. Utilize all the reasonable elements of MLOps, ensure coding standards be in place, provide a 'finite' development platform, build solid pipelines, make use of the automation potential, ensure sustainability. Finally, make sure that involved leaders have real, relevant competence to understand content and assess value vs. cost. Prepare for potentially sensitive HCM topics.

This is an important phase to measure the outcome.. Keep ur change management process ready if needed for minor changes and discuss the plan with stake holders.. Especially when u handle real data in production, you will have more categories of data..

Utilize MLFlow, AirFlow (etc.) to automate some of these recurring tasks. Beyond technical KPIs, do monitor (or provide support to easily watch) overall profitability, as maintaining such solutions might generate more expense than the revenue they drive, eventually.

It is crucial to keep everyone updated on the progress of your project and make sure that they are aware of any challenges or risks. This will help to build trust and confidence and will make it more likely that your project will be successful. Most importantly, things don't always go according to plan in AI projects. We need to be prepared to change course if necessary. This would mean changing the problem statement, data, algorithms, team as required.

Monitoring and evaluation need a robust human-in-the-loop approach to enable human verification and corrections to ensure data accuracy. With this approach, the focus should be on running a small pilot before implementing an organization-wide solution.

Remember to apply your new learnings to previous projects, too. As I write new LLM prompts and research new trends or strategies, I've been able to make quick updates to past work that improves the output tremendously. I try to keep moving forward - this space is changing fast. But I also make sure I don't forget to check if I can add wins by continuing to iterate where it's feasible.

Having worked on one of the contracts management system using AI, we realised the same solution can be scaled for various clients with a minimal tweak in the overall solution design. This is really helpful as it drastically reduces the lead time and that creates the difference.

As a best practice, We should continuously focus on evaluation of Leverage from Algorithms, Data from AI Project. Creating and Maintaining University - Knowledge Base of Lessons Learnt and Best Practices will be a great resource for future AI Projects. Induction processes should include University - Knowledge Base induction as Crucial. Given AI is rapidly evolving, Establishing a Framework for Continuous Learning, gaining insights on trends, best practices from successfully executed AI Projects and adopting them is crucial.

From what I have seen, the onset of a project does not always deliver the exact outcomes expected within the Generative AI space. However, in the Analytical AI space we typically see the exact outcome that was created at onset of the project. Why? Analytical AI have a much tighter probability of outcome and implementation metrics where Generative AI is still quite fluid in implementation.

Data Management and Privacy are critical aspects of any AI project. Proper data management ensures the availability, quality, and security of data, which is the foundation for building reliable AI models. Privacy safeguards protect users' sensitive information, fostering trust between businesses and customers. When implementing AI initiatives, prioritizing data management and privacy is essential to uphold ethical standards and comply with regulations. #AI #DataManagement #Privacy #Ethics #Trust

AI projects can take time to be successful. We shouldn't expect to see results overnight. We need to be patient and persistent so that eventually we can achieve our goals. AI systems can have a significant impact on people's lives, so it is important to make sure that they are used responsibly. Some of the key ethical considerations for AI projects include: Bias - I systems should not discriminate against any group of people. Transparency - People should be able to understand how AI systems work and make informed decisions Accountability - There should be a way to hold people accountable for the decisions made by AI systems.