Key steps to kick off an AI journey in your current job
Picture by KindelMedia, on Pexels
I have heard many times data scientists frustrated due to the lack of cool projects to work on within their company. Convincing business stakeholders and management to start AI projects can be challenging. While it is not usually the data scientist’s responsibility to think and propose the projects that need to be prioritized, I’ve seen how data scientists together with data managers and product managers can influence the roadmaps and help introduce more innovative and impactful projects.
In this blog post I am going to share some of the steps and strategies that I’ve seen successfully influence the team or company culture towards introducing more innovative ML or AI based projects. Be aware this is not something that happens from one day to another, but a journey in which your knowledge and motivation can help others in your company to think outside of the box and see the potential of ML and AI.
These key steps and strategies for pitching innovation and AI in your company are: raising awareness, inspiring through use cases, finding sponsors & ideas, and prioritization.
1. Raise awareness about AI
The first step is to raise awareness on your organization about what AI can and cannot do. Many people have limited understanding of AI, which can lead both to skepticism and unrealistic expectations.
The end goal at this first step would be to help people around you to gain sensibility about AI. This sensibility can include: what is the difference between ML and AI, what type of problems can I solve with traditional ML (classification, regression, time series…), what new opportunities appear now with GenAI (text generation, image generation, few shot classification…). Some strategies to reach this awareness are:
People around you have already some awareness and sensibility about AI and ML, the types of models that exist, their potential, and how those types of projects work, great! The next step is to start introducing use cases that can inspire projects for your company. These use cases can come from competitors or analogous industries, but also from the general use cases that apply to most companies (user segmentation, client / user churn prediction, selling forecasts…).
Demonstrating how competitors or other companies are leveraging AI can be powerful to illustrate its potential and inspire next steps. When showcasing use cases, you can focus on the problem that use case solves, the tangible benefits it achieved, and make the analogy on how something similar could be applicable in your company. Similarly, for more general use cases, such as user segmentation, it can be interesting to showcase the type of application that could come out of it in your specific company (dynamic pricing, personalization, improved communications…).
If there are already some teams doing competitors analysis (usually User Researchers), make sure they are also taking into account ML / AI features. Help them gain the sensibility on how those solutions might work underneath to enrich further their research and detect AI opportunities for your company.
3. Find your sponsors & use cases
Picture by Fauxels, on Pexels
There is now awareness on what AI is and what types of problems and use cases it can help solve in your company. If you’ve done it right, you should have been able to get some people really excited about all this potential!
This excitement can translate into people coming directly to you to share other use cases, ask questions, or even ask wether something is feasible to solve with AI or not. These are your sponsors: champions within the organization who can support and advocate for AI initiatives. Depending on the size of the company and how big the culture change needs to be, this sponsorship might be close enough to influence decision-making at the highest levels. However, getting to inspire business stakeholders can also be good enough, as they can push to solve their own objectives through AI.
You’ve planted the seeds for AI project ideas to come out. You can now start proposing specific AI solutions for specific company problems or objectives. Thanks to all the previous work on awareness, use cases, and sponsors, these proposals should be now much more well received!
What is most interesting from this step though, is waiting for the use cases to also come to you. Your AI sponsors and other people in the company are now able to link problems and objectives to AI solutions. It might surprise you how much use cases can appear from this direction. The awareness you’ve built will naturally lead to more informed and relevant suggestions.
4. Assessing and prioritizing use cases
At this point you might have been able to collect several ideas of initiative and have the buy-in from the management to dedicate some time to work on them. But how do you decide with where to start? It might make sense to start with the initiative with biggest potential, but predicting the ROI of innovation, and particularly of AI projects, can be challenging due to their inherent uncertainty. However, there are some key points to take into account that can help on that end:
Try to start with exploitative ideas (quick wins), to prove value faster, gain traction and build trust. Once that is managed, maybe you can start introducing explorative ideas (moonshots) that aim longer term & bigger transformation, but also involve higher risks to fail. Balancing a continuous delivery and improvement with moonshots is key to maintain the trust long term while also exploring real innovation.
In a previous post “Starting ML Product Initiatives on the Right Foot”, I deep-dived into how to successfully start with ML initiatives and manage their inherent uncertainty from the beginning.
Starting ML Product Initiatives on the Right Foot
Wrapping it up
Pitching AI in your company is a long term journey, not something that will happen overnight. From my experience, it is important to start generating awareness and education, showcasing use cases, and aligning with sponsors in the right positions. Only then, proposing use cases will listened; even other people might come to you with relevant ideas! Once some use cases have been gathered and there is some bandwidth and buy-in to prioritize some dedication, it is time to focus on strategic problems, quantify well the opportunity and potential, and balance between quick wins and long term moonshots.
We are in a moment in time where everybody is talking about AI. In particular, companies are trying to think about their (Gen)AI strategies and how this new technology will change the business and ways of working. This plays in your favor: it should be a good moment to start introducing this steps, as people are particularly keen to learn, play, and leverage AI.
Pitching (AI) Innovation in Your Company was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Picture by KindelMedia, on Pexels
I have heard many times data scientists frustrated due to the lack of cool projects to work on within their company. Convincing business stakeholders and management to start AI projects can be challenging. While it is not usually the data scientist’s responsibility to think and propose the projects that need to be prioritized, I’ve seen how data scientists together with data managers and product managers can influence the roadmaps and help introduce more innovative and impactful projects.
In this blog post I am going to share some of the steps and strategies that I’ve seen successfully influence the team or company culture towards introducing more innovative ML or AI based projects. Be aware this is not something that happens from one day to another, but a journey in which your knowledge and motivation can help others in your company to think outside of the box and see the potential of ML and AI.
These key steps and strategies for pitching innovation and AI in your company are: raising awareness, inspiring through use cases, finding sponsors & ideas, and prioritization.
1. Raise awareness about AI
The first step is to raise awareness on your organization about what AI can and cannot do. Many people have limited understanding of AI, which can lead both to skepticism and unrealistic expectations.
The end goal at this first step would be to help people around you to gain sensibility about AI. This sensibility can include: what is the difference between ML and AI, what type of problems can I solve with traditional ML (classification, regression, time series…), what new opportunities appear now with GenAI (text generation, image generation, few shot classification…). Some strategies to reach this awareness are:
- Workshops and trainings: these can be organized in-house, or you can also recommend online courses. The second option is usually faster and less expensive; courses like “AI For Everyone” and “Generative AI For Everyone” from deeplearning.ai are always a good start.
- Empower & encourage everyone to use GenAI: this can be done by casually explaining how you leverage GenAI yourself, by sharing images and poems obtained through it, or by challenging why they haven’t used it yet. Try to understand if there are specific concerns that are holding people back (e.g. “I don’t trust it with my own data”), and share tools or techniques that can help mitigate those perceived risks.
- Showcase ML / AI projects: actively participate in your company demos, All Hands, or internal knowledge sharing sessions. You can share ML or AI projects you or your team have already implemented. It is important to ensure the right level of technical details to allow people to follow your presentation, and highlight the project’s potential, impact, and learnings. It can also be interesting to share how these projects differentiate from “traditional software development” or the other type of projects from the company.
People around you have already some awareness and sensibility about AI and ML, the types of models that exist, their potential, and how those types of projects work, great! The next step is to start introducing use cases that can inspire projects for your company. These use cases can come from competitors or analogous industries, but also from the general use cases that apply to most companies (user segmentation, client / user churn prediction, selling forecasts…).
Demonstrating how competitors or other companies are leveraging AI can be powerful to illustrate its potential and inspire next steps. When showcasing use cases, you can focus on the problem that use case solves, the tangible benefits it achieved, and make the analogy on how something similar could be applicable in your company. Similarly, for more general use cases, such as user segmentation, it can be interesting to showcase the type of application that could come out of it in your specific company (dynamic pricing, personalization, improved communications…).
If there are already some teams doing competitors analysis (usually User Researchers), make sure they are also taking into account ML / AI features. Help them gain the sensibility on how those solutions might work underneath to enrich further their research and detect AI opportunities for your company.
3. Find your sponsors & use cases
Picture by Fauxels, on Pexels
There is now awareness on what AI is and what types of problems and use cases it can help solve in your company. If you’ve done it right, you should have been able to get some people really excited about all this potential!
This excitement can translate into people coming directly to you to share other use cases, ask questions, or even ask wether something is feasible to solve with AI or not. These are your sponsors: champions within the organization who can support and advocate for AI initiatives. Depending on the size of the company and how big the culture change needs to be, this sponsorship might be close enough to influence decision-making at the highest levels. However, getting to inspire business stakeholders can also be good enough, as they can push to solve their own objectives through AI.
You’ve planted the seeds for AI project ideas to come out. You can now start proposing specific AI solutions for specific company problems or objectives. Thanks to all the previous work on awareness, use cases, and sponsors, these proposals should be now much more well received!
What is most interesting from this step though, is waiting for the use cases to also come to you. Your AI sponsors and other people in the company are now able to link problems and objectives to AI solutions. It might surprise you how much use cases can appear from this direction. The awareness you’ve built will naturally lead to more informed and relevant suggestions.
4. Assessing and prioritizing use cases
At this point you might have been able to collect several ideas of initiative and have the buy-in from the management to dedicate some time to work on them. But how do you decide with where to start? It might make sense to start with the initiative with biggest potential, but predicting the ROI of innovation, and particularly of AI projects, can be challenging due to their inherent uncertainty. However, there are some key points to take into account that can help on that end:
- Focus on specific strategic pain points or opportunities within the company.
- Use industry benchmarks to estimate success rates and potential revenues.
- Asses potential benefit, but also feasibility and risks.
- Differentiate between exploratory (high uncertainty, long-term) and exploitative (low uncertainty, short-term) projects.
Try to start with exploitative ideas (quick wins), to prove value faster, gain traction and build trust. Once that is managed, maybe you can start introducing explorative ideas (moonshots) that aim longer term & bigger transformation, but also involve higher risks to fail. Balancing a continuous delivery and improvement with moonshots is key to maintain the trust long term while also exploring real innovation.
In a previous post “Starting ML Product Initiatives on the Right Foot”, I deep-dived into how to successfully start with ML initiatives and manage their inherent uncertainty from the beginning.
Starting ML Product Initiatives on the Right Foot
Wrapping it up
Pitching AI in your company is a long term journey, not something that will happen overnight. From my experience, it is important to start generating awareness and education, showcasing use cases, and aligning with sponsors in the right positions. Only then, proposing use cases will listened; even other people might come to you with relevant ideas! Once some use cases have been gathered and there is some bandwidth and buy-in to prioritize some dedication, it is time to focus on strategic problems, quantify well the opportunity and potential, and balance between quick wins and long term moonshots.
We are in a moment in time where everybody is talking about AI. In particular, companies are trying to think about their (Gen)AI strategies and how this new technology will change the business and ways of working. This plays in your favor: it should be a good moment to start introducing this steps, as people are particularly keen to learn, play, and leverage AI.
Pitching (AI) Innovation in Your Company was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
