Working with AI vendors can be challenging, especially when it comes to meeting deadlines. AI projects often involve complex and uncertain tasks that require frequent communication, feedback, and adjustments. How can you ensure that your AI vendor delivers on time and meets your expectations? Here are some tips to help you manage deadlines with AI vendors.
Before you start working with an AI vendor, you need to have a clear and realistic idea of what you want to achieve and how you will measure the results. Define the scope and goals of your AI project, including the data sources, the use cases, the outputs, the success criteria, and the timeline. Make sure that your AI vendor understands and agrees with your vision and expectations, and that they have the resources and expertise to deliver them.
I don’t fully agree with this. Deploying AI is unlike traditional projects… the tech is moving so fast what you need are very clear ‘guard rails’ and ‘principles’. I always start with the purpose, typically a commercial objective. This becomes the guiding compass point. Agree work principles: agile stage lengths, quality hurdles, team ways of working and culture, key measures. Ideally build a set of objectives, and supporting elements that build them (a backlog) but keep this under both frequent review and reprioritisation. And learn, learn, learn, adapt, learn some more.
An example I have seen is, none of the AI vendors have a product which can fit all our needs. Key is co-development and there arrives the challenges as Sky would be promised. It’s essential not to get carried away, stick to your needs but have vision on the horizon. Start with small expectations / use cases and how it could be implemented to operations, once PoC is successful, define scope, goals and success KPIs and commercials based on delivery and not upfront. Be crisp on scope, no room for imaginations. Maintain backlog.
Aligning with an AI vendor that deeply resonates with your values and understanding is crucial. In AI project management, success hinges on blending visionary foresight with pragmatic execution. Start with a clear, purpose-driven approach, aligning AI goals with broader business objectives. Embrace agility and adaptability, as AI's dynamic nature demands a flexible, iterative process. Ethical considerations and vendor expertise are paramount. Focus on collaborative development, starting small and scaling with success. Regular evaluation, risk management, and continuous learning are key. Effective AI project management isn't just about technology; it's about integrating these principles to innovate responsibly and effectively.
Defining scope and goals is the cornerstone of successful AI projects. Before engaging a vendor, clearly articulate your vision: Data Sources: Identify data required to train and operate your AI. Use Cases: Define specific applications and intended value creation. Outputs: Determine desired outputs and how they will be used. Success Criteria: Establish metrics to quantify and measure project success. Timeline: Set realistic timelines for development, deployment, and evaluation. Mutual understanding and agreement with the vendor on these elements are crucial. Ensure their resources and expertise align with your project's specific needs. By meticulously defining scope and goals, you lay the foundation for a successful AI journey.
On thing I've found very helpful is to draft success criteria aligning with goals & vision, and clearly define the data source needed to train the model.
Communication is key to managing deadlines with AI vendors. You need to establish a regular and effective communication channel with your AI vendor, where you can share updates, feedback, issues, and questions. You also need to set clear and consistent expectations for the frequency, format, and content of the communication. For example, you can use email for status reports, video calls for demos, and chat tools for quick queries. You should also document and track the communication history, so that you can avoid misunderstandings and conflicts.
Regular and clear communication underpins successful AI vendor partnerships. Establish a dedicated channel for: Sharing Updates: Progress reports, challenges, and next steps. Providing Feedback: Addressing concerns and suggestions for improvement. Raising Issues: Timely notification and resolution of problems. Asking Questions: Seeking clarification and requesting support. Define communication expectations: frequency, format (email, video, chat) and content. Document the communication history to maintain transparency and avoid misunderstandings. Effective communication fosters trust, ensures alignment, and facilitates timely course correction, ultimately leading to successful AI projects.
A project in AI is something like moving North by looking at the pole star. We know we are in danger here, and something good will come when we reach somewhere north. What exactly is not clear, and the road to reach it is unclear, unchartered and to be self made. Every now and then we look up and see that we are going in the right direction. We look back and see progress.
??LinkedIn Top Voice | PhD in Innovation | Business Scientist | Healthcare Analytics
1st We should Speak the AI-Language: While technical jargon is inevitable, strive for clear and concise communication. Avoid ambiguity and focus on the "why" behind your requests. Explain how deadlines impact your overall strategy, and frame questions in a way that encourages the vendor to leverage their AI expertise to offer solutions. As 2nd we need to Build a Communication Cadence: Don't rely on sporadic check-ins. Establish a rhythm of communication that suits your project's needs. Whether it's daily stand-up meetings, weekly progress reports, or bi-weekly deep dives, ensure consistent touchpoints to stay on track and address potential roadblocks early.
Establish a regular and effective communication channel with AI vendors. Set clear expectations for frequency and format, using email for status reports, video calls for demos, and chat tools for quick queries. Document and track communication history to avoid misunderstandings and conflicts.
In addition to establishing regular and effective communication channels with your AI vendor, it is important to consider their contribution to the communication process. Ensure that your AI vendor is proactive in providing updates, addressing feedback, and resolving issues promptly. They should actively participate in the communication channels and demonstrate a willingness to collaborate and address any concerns or questions you may have. By fostering a collaborative and transparent communication environment, you can enhance the efficiency and effectiveness of your AI project.
Another important aspect of managing deadlines with AI vendors is to monitor and evaluate the progress of your AI project. You need to have a clear and agreed-upon process for reviewing and testing the deliverables, as well as for providing and incorporating feedback. You should also have a set of key performance indicators (KPIs) and milestones that you can use to measure the quality and timeliness of the work. You should also be prepared to deal with any delays or changes that may arise, and to negotiate and adjust the deadlines accordingly.
Proactive monitoring and evaluation are crucial for successful AI projects. Collaborate with your vendor on a well-defined process for: Deliverable Review & Testing: Assess functionality, accuracy, and adherence to specifications. Feedback Incorporation: Provide constructive feedback and facilitate timely incorporation. KPI Tracking & Milestone Monitoring: Measure progress against defined performance indicators and key milestones. Prepare to adapt to unforeseen circumstances: Addressing Delays: Identify and address causes of delays collaboratively. Negotiating Deadlines: Negotiate revised timelines based on justifiable factors. Through active monitoring, rigorous evaluation, and flexible adaptation, project progress stays on track.
Unlike traditional development AI requires many iterations across data and the inferences need to be grounded and checked time and again. So monitoring the progress of a project is crucial
??LinkedIn Top Voice | PhD in Innovation | Business Scientist | Healthcare Analytics
Don't fall into the trap of generic KPIs. Tailor your metrics to your specific AI goals. Are you aiming for increased revenue? Improved customer satisfaction? Define success metrics that align with your business objectives and translate them into concrete, measurable data points. Think accuracy, efficiency, cost reduction – metrics that tell the true story of your AI's impact.
For monitoring and evaluating AI project progress, it's crucial to set KPIs and milestones. Regular check-ins, clear feedback channels, and flexibility in adjusting to new developments ensure project alignment and success.
In my experience, it's common that when integrating disruptive technologies like AI, all executive levels have their eyes on the progress of these projects. This isn't just because of the high economic investment, but also due to the potential business impact such a solution can represent. It's important to establish milestones throughout the project to verify the partial functionality of the solution, primarily to encourage a cycle of continuous feedback that enhances the solution in the early stages of implementation.
Working with AI vendors can also involve various risks and challenges that can affect the deadlines. For example, you may encounter data quality issues, technical difficulties, ethical concerns, or legal constraints. You need to identify and assess the potential risks and challenges that may impact your AI project, and to have a contingency plan for mitigating and resolving them. You should also communicate and collaborate with your AI vendor to address any problems or obstacles that may arise, and to find solutions that work for both parties.
In navigating the landscape of AI projects with vendors, consider it as a dynamic collaboration. Challenges can be viewed not just as roadblocks but as opportunities for innovation. By fostering a perspective that embraces adaptability and creative problem-solving, you can turn obstacles into stepping stones toward achieving the goals of your AI project. Communication and collaboration then become not just processes but key elements in the shared journey toward success.
AI projects inherently involve potential risks: data quality issues, technical hurdles, ethical considerations, and legal restrictions. To safeguard against these, implement: Risk Assessment: Identify and analyze potential threats and their impact. Contingency Planning: Develop proactive strategies to mitigate and respond to risks. Open Communication: Foster clear and transparent communication with your AI vendor. Collaborative Resolution: Work together to find solutions that address challenges and ensure project success. By proactively managing risks, you can navigate unforeseen obstacles, minimize project delays, and secure the successful implementation of your AI vision.
In addition to identifying and managing risks and challenges, it is important to consider the contribution of your AI vendor in mitigating these potential issues. Your AI vendor should actively participate in risk assessment and provide insights and expertise to help identify and address potential risks and challenges. They should also be proactive in suggesting solutions and implementing measures to mitigate the impact of these risks on the project timeline. By collaborating closely with your AI vendor, you can effectively manage and overcome the risks and challenges associated with your AI project, ensuring successful delivery within the set deadlines.
To manage AI vendors effectively, establish clear goals, maintain open communication, and create robust contingency plans. Regularly review deliverables for quality, address ethical and legal compliance, and ensure agility to adapt to new challenges.
There are many open ended risks when it comes to AI projects. We should be very careful about its assurances. As it is an evolving subject we need to nurture it and at the same time we should approach this with pragmatic caution.
Finally, managing deadlines with AI vendors also means celebrating and learning from the outcomes. Once your AI project is completed and delivered, you should acknowledge and appreciate the efforts and achievements of your AI vendor, as well as your own team. You should also conduct a post-mortem analysis of your AI project, where you can review the results, the feedback, the lessons learned, and the best practices. You should also use this opportunity to improve your future collaboration with your AI vendor, and to explore new ways to leverage AI for your business.
Upon project completion, acknowledge and celebrate achievements of both teams. Conduct a post-mortem analysis to: Review Results: Evaluate project success against goals and expectations. Gather Feedback: Seek insights from stakeholders and the AI vendor. Extract Lessons Learned: Identify areas for improvement and best practices. Plan for Future Collaborations: Utilize learnings to refine future AI project management approaches. Explore New Opportunities: Leverage AI insights to discover new value propositions and business opportunities. Through celebration and continuous learning, you can cultivate a culture of innovation and drive ongoing success in your AI journey.
??LinkedIn Top Voice | PhD in Innovation | Business Scientist | Healthcare Analytics
Share your learnings! Document your AI journey, the successes, the challenges, and the invaluable lessons learned. Present it at conferences, publish it in industry publications, or even create training materials for your team. By sharing your knowledge, you not only inspire others, but also contribute to the broader AI community, paving the way for a future where deadlines are not hurdles, but stepping stones on the path to collective AI success.
An AI project is never finished. It is ongoing. Do celebrate your successes from time to time and share your experiences both internally and also with the outside world in the form of expert blogs, case studies, or articles. Especially share your successes with the investors and all stakeholders.
Celebrating wins along the way isn't merely symbolic but it's essential in maximizing stakeholder value to ensure further investment and connected shared collaboration cross-functionally. Since this is an emerging tech, scorecarding or even baking-off against competitive AI vendors will help maximize learnings and also the correct technical solution.
All das ist relevant und gilt nicht nur im KI Kontext sondern grunds?tzlich bei jeder Initiative, Projekt, etc. Ohne lebenslanges Lernen (mindestens bis zur Rente ??) gibt es ja keine Entwicklung.
I believe team managers leading as customers to projects driven by vendors should have technical leadership while driving projects. It is critical to define scope, review design elements to select the right architecture models and overall track projects keeping technical risk mitigation in focus.
Well, here's a thought. With all the noise around AI, it is critical to not forget the importance of a human touch. It is us, after all who drive the technology to provide better results. Let me give a small example. We recently wanted to create a video using only AI. AI was a good starting point, with it helping us to create the framework, get the correct stills. It ultimately boiled down to our choice of selection that could actually the bring the best result. We after all make the final decision, which any technology cannot supersede.
Perhaps! AI possibly can take over human speed but not the accuracy. When implementing AI, it’s evidential that a machine is trained to think like an artificial brain to behave how a human would in a particular situation, in cases where the machine encounters events like black swan it becomes impossible to solve such problems in a given time; which potentially leads to failure.
Managing deadlines with AI vendors requires a proactive and communicative approach. Clear and concise communication channels should be established from the outset, ensuring all parties have a mutual understanding of project expectations and timelines. Realistic deadlines should be set, considering the complexity of AI projects and potential unforeseen challenges. Regular progress checks and milestones should be defined to track the project's trajectory. Flexibility is also crucial, as unexpected delays might arise, necessitating contingency plans to mitigate risks and minimize disruptions.
Flexibility & Adaptability: Contingency Planning: Develop contingency plans for potential risks and challenges that may impact deadlines. This could include identifying alternative data sources, backup systems, or additional resources if needed. Regular Reviews: Conduct regular project reviews to assess progress, identify any deviations from the original plan, and adjust strategies as necessary. This ensures adaptability to changing circumstances and maintains project goals. Openness to Change: Be prepared to adapt to unforeseen challenges and changes in the AI landscape. This may involve adjusting expectations, timelines, or even project scope to ensure the best possible outcome.