How do you manage client impatience when AI project outcomes take longer than expected?

For AI project to be successful (On time, On Budget, Expected Outcomes) following are pre-requisites 1. All stakeholders should have understanding of AI fundamentals. 2. AI success is directly proportional to quality of data. If data sets are not ready to train, test and validate, one should not kick off AI projects. Experimentations / Explorations as are ok to test the waters, but no serious commitment on time, budget should be provided. 3. AI outcomes - Definition of predicted KPIs, classifications etc. should be established prior(in other words Definition of Done). AI outcomes are hardly in black and white, it is always in a range, and a continuous iterative process to improve accuracy of outcomes to desired levels.

Para gestionar la impaciencia de los clientes en proyectos de IA, establece expectativas claras desde el principio y comunica transparentemente las complejidades del proceso. Explica la importancia de cada fase, sé honesto sobre posibles retrasos y proporciona una hoja de ruta con hitos clave. Además, mantener a los clientes informados con actualizaciones regulares mejora la transparencia y la satisfacción.

It is important that you clearly define project scope and if the client adds to the scope, be clear of the additional time that will be likely to take. Ensure you are clear about how long the data wrangling phase is likely to take and give a range for the time required based on what was found in discovery. Explain how the model training phases can require multiple iterations and each takes time. Provide ranges of time for development. Allow time in the schedule to integrate client feedback and amends but be clear on timeline changes that can be impacted with amends. Provide clients with regular updates (we do daily team meetings and weekly client updates) that incorporate any delays and how they are impacting the time line.

Any project is divided into multiple phases..each phase has its own issues and timeline. Some of these phases may require more time and can lead to potential delays. So before starting setup clear expectations and a detailed roadmap with key milestones and communicate this to the stakeholders. At the end regular updates and transparent communication would ensure trust and collaboration with the client.

Setting expectations are the first step. But clear, consistent, and frequent communications will make your project a success with your stakeholders. Using a framework like a RACI (responsible, accountable, consulted, informed) chart and a communications plan will help you immensely on who, what, and when on your communications of the project progress.

I recommend providing the client with weekly updates outlining what has been accomplished that week, any challenges or blockages and why, and the impact that may have on the schedule.

Clients should not care about technical details. If you do and are transparent about the possibilities there is no difference working with AI or other techniques.

Constant user testing in every stage helps. At every stage of the project, we can have different scales of user testing. This helps build confidence in the product and rally the stakeholders.

1. Even if there are no significant advancements, updating the client on the project’s status keeps them in the loop. 2. Mark and celebrate milestones, both big and small. 3. Acknowledging the completion of project phases or goals helps demonstrate progress and builds positive momentum. 4. Whenever possible, show tangible benefits or results from the work completed so far.

I've been hearing about educating your clients for 16 years. You don't have to do that. You have to educate yourself on their needs, if you can't match them do not promise it.

I heard somewhere that , as a consultant you should know more about your client than they themselves. So educate your client by sharing successful case studies to highlight the potential benefits and set realistic expectations. Provide educational resources and keep clients updated on the latest trends and your expertise. Ensure that clients understand each phase of the project and feel informed about progress and challenges.

1. Occasionally provide educational content that helps the client understand AI and machine learning more deeply. 2. Consider offering workshops or seminars if the project is long-term or particularly complex. 3. When issues arise, involve the client in brainstorming solutions or making decisions on how to proceed.

Empower your clients with knowledge. Break down complex AI concepts into simple, digestible pieces. Explain why certain stages take time, such as data preprocessing or model training. Show them examples of similar projects and how patience paid off in the end. Use resources like webinars, articles, and case studies to educate and engage them.

Your biggest challenge is breaking down the complex concepts of AI into digestible information that clients can understand and appreciate without feeling overwhelmed. Solution: Simplify your explanations and use analogies that relate to their industry. Regularly share insights on data quality, model training, and testing processes. Highlighting successful case studies can reassure clients of the potential outcomes. A well-informed client is more likely to grasp the project's intricacies and show patience throughout the development process. For further insights and strategies, check out "AI Superpowers" by Kai-Fu Lee.

A alternative to interim solutions: What I have observed is that today, A lot of service providers are working on POCs and pilots. clients are considering GenAI as a fad and are reluctant to spend on a full fledged project. This type of approach can be used in any type of project While working on the main AI project, consider starting with a proof of concept or a pilot project. These are quicker to implement and can give you immediate insights and benefits. They act as simpler versions of the final product, so you can see some real results early on. This way, you get value right away, and it will show commitment to success, which can help ease any impatience and build confidence of clients in the project.

Once again, just do not offer things that are not possible to earn some quick cash. That will only prove your lack of commitment with the client and delivering only the greatest work.

1. Focus on rapid development frameworks and tools to create interim solutions. 2. Employ agile methodologies to manage the development of interim solutions. 3. Plan how the interim solutions will integrate with or transition into the main AI solution. 4. Use the feedback to iteratively improve the interim solution

Sometimes, offering a quick win can soothe impatience. Identify and implement interim solutions that can provide immediate value while the main project continues. These could be smaller, less complex features or insights derived from early data analysis. Think of it as giving them a "sneak peek" into the future benefits.

I'm afraid I have to disagree with delivering an interim solution. To manage client impatience in AI projects, it's better to prioritize features and deliver incremental, valuable iterations. 🛠️ Incremental Development: Focus on developing and delivering the final solution in manageable, valuable increments. 📅 Feature Prioritization: Prioritize key features to ensure each iteration adds meaningful value. 🗣️ Client Agreement: Ensure clients are fully on board with the approach to manage expectations. 📈 Regular Updates: Provide regular updates and demos to show progress and maintain client engagement. 🤝 Commitment to Success: Demonstrate your commitment to delivering a successful final product step by step and build trust.

1. Consider setting up cross-functional teams that include a mix of data scientists, data engineers, project managers, and domain experts. 2. Track performance against key metrics such as milestone completion, quality of output, and resource utilization rates. 3. Be prepared to reallocate resources dynamically. 4. Keep stakeholders informed about any changes in scope or project timelines.

Optimize your team's workflow to ensure efficiency. Prioritize tasks that directly impact the project's progress and keep the client informed about resource allocation. This helps in setting realistic expectations about what can be achieved within a given timeframe. Use project management tools like Trello or Asana to keep everyone aligned and transparent.

Some projects have research and development phases, others don't. While you don't lie about capabilities and possibilities you will be safe. Clients shouldn't be flexibles if you promise something that you could not deliver.

Flexibility is one of the golden gooses that I found in Agile, this concept applies to every project, but mostly in AI projects that is easier to be in front of changes, challenges and complexities not visibles at the start.

Flexibility is crucial. If delays occur, reassess and adapt the project plan. Involve the client in these discussions, making them feel part of the decision-making process. Show them how adjustments will lead to a better end product. It's like navigating a ship – sometimes you need to change course to avoid a storm and ensure a safe arrival.

The main challenge here is managing the unpredictability of AI projects and ensuring both you and your clients remain flexible when things don't go as planned. So your solution is to stay agile and ready to pivot strategies if initial approaches aren't working. Communicate the need for flexibility to your clients and encourage them to adapt their expectations as needed. Regularly update them on changes and the reasons behind them. This adaptability helps manage delays and keeps the project on track. Consider reading "Agile AI" by Carlo Appugliese.

I can't say it enough: Use an Agile project management approach. Continuously add new features, discuss priorities, and deliver each iteration focused on what the customer needs most to achieve their goals. This ensures ongoing value, transparency, and alignment with client expectations, fostering trust and satisfaction throughout the AI project journey.

When pitching your solution, give clients more than 1 option, explaining the pros and cons, and likely timeline of each. If they select the most labour-intensive option, make it clear from the start that, while it will give them the added benefits they are looking for, this comes at a time cost. This will help set expectations right from the outset. If there are delays, remind them that while the chosen approach is the most labour-intensive, it should yeild the results they were looking for - reiterate its benefits over the alternatives that were discussed during the pitching phase.

The emergence of Artificial Intelligence and its potential benefits have inspired many organizations to dive in head first in hopes of being the first to market, the first to claim the spoils of this race to the top. Clients need to remember that AI needs to be treated just like any other technology. Quality Control, Risk Management, Data Quality....everything is applicable, even more so than with other technology solutions because AI has the potential to influence human lives, sometimes irreversibly. So ensuring the right application of any AI and proper management of data sources feeding decision algorithms is critical.

Empathy: Understand and acknowledge the client's frustration. Show empathy and reassure them that their concerns are valid. Showcase Milestones: Highlight every achieved milestone, no matter how small, to keep the client's morale high. Be Honest: If things are not going as planned, be honest. Transparency builds trust, even if the news isn't always positive. Client Involvement: Involve the client in parts of the process where they can contribute or give feedback. This can help them feel more in control and less impatient.

Anything morale or cultural are situations that should not be allowed to fester for long. Nip it in the bud, clearly communicate the situation to everyone, and maybe it's time to have the difficult conversation on what's delaying the project. Expectations were probably not aligned to begin with, and now's the time to do that. Also pulling the plug should be on the table if precious resources are on the line.

Managing client impatience when AI project outcomes are delayed involves transparent communication, setting realistic expectations, and highlighting milestones. You build trust and manage their expectations by keeping clients informed about progress and setbacks. Break the project into achievable milestones and provide regular updates to demonstrate ongoing progress. Offering alternatives or interim deliverables can also reassure clients and show the value of the work being done. Address concerns empathetically, manage scope changes carefully, and emphasize the project's long-term benefits. Learn from client feedback to improve future project estimations and communication strategies.