What do you do if your algorithm ideas are not understood by non-technical stakeholders?

Here's a breakdown of strategies to bridge the gap when non-technical stakeholders are struggling to grasp your algorithm ideas: 1. Focus on Problems and Solutions, Not Technical Jargon The "Why": Start with the core problem your algorithm aims to solve. Express this in terms of business needs, pain points, or potential gains. The "What": Explain what the algorithm does in plain English, focusing on the input it takes, the transformation it makes, and the output or result it provides. Avoid "How": Minimize details on the technical implementation, code, and specific math unless those details are absolutely essential. 2. Employ Analogies and Real-World Examples

Strategies For Breaking Down Complex Algorithms. One strategy for breaking down complex algorithms to non-technical stakeholders is to use analogies or real-world examples that they can relate to. By comparing the algorithm to a familiar concept or process, you can help them grasp the basic idea behind it. Another approach is to focus on the outcomes and benefits of the algorithm, rather than getting bogged down in technical details. By highlighting how the algorithm will improve efficiency, save time, or increase profits, you can make it more tangible and relevant to stakeholders. Additionally, creating visual aids such as flowcharts or diagrams can help simplify complex algorithms and make them easier to understand.

In my experience, by breaking down the algorithm into logical blocks and describing their purpose and function, it becomes much easier to communicate the idea to stakeholders. To get started, split your method/algorithm into separate logical blocks and describe with words three main questions: 1. What is this logical block doing? 2. Why is it needed? 3. How does it work? in 1 sentence Having these blocks and descriptions will help you to easily convey your idea to stakeholders.

Use abstraction and step-wise refinement to explain the algorithm. First, give a high level description of algorithm. Next, go to the next level of description and repeat the process to the desirable level of detail.

Importance Of Communicating Algorithm Ideas Clearly. Clear communication of algorithm ideas is crucial in ensuring that non-technical stakeholders understand the value and potential impact of a proposed solution. When complex algorithms are not clearly explained, stakeholders may fail to see how the technology can address their specific needs or improve their processes. This lack of understanding can lead to skepticism, resistance, or even rejection of the proposed solution. By effectively communicating algorithm ideas in a way that is accessible and relatable to non-technical audiences, stakeholders are more likely to grasp the benefits and make informed decisions. Clear communication also fosters collaboration and alignment.

I've found that using flowcharts helps explain complex algorithms to not only non-technical but sometimes even technical stakeholders, in a simple way. It breaks down the steps and illustrates the process, making it easier for everyone to understand and contribute to the discussions effectively.

For some similar reasons we have gone through several generation of programming languages to encapsulate details into experts of the area and only talk about the interaction of different components (objects) to the users. Encapsulation of details is one of the key ?? points here.

To ensure that stakeholders who are not familiar with technical concepts are fully involved in the process of building an algorithm, we can use flowcharts and diagrams that illustrate the structure and flow of the algorithm. This approach will allow us to work together towards a common goal of simplifying complex technical information. Additionally, using illustrations can help to generate more ideas, constructive criticism, and diverse perspectives, all of which are valuable building blocks for a successful algorithm.

I find that getting stakeholders to buy into algorithmic-based solutions requires them to intuitively understand the problem and the solution. By avoiding complex terms and technical jargon and by using plain language to explain each step of the process, clients get up to speed quickly and attain an intuitive grasp of the solution.

Importance Of Communicating Algorithm Ideas Clearly. Clear communication of algorithm ideas is crucial in ensuring that non-technical stakeholders understand the value and potential impact of a proposed solution. When complex algorithms are not clearly explained, stakeholders may fail to see how the technology can address their specific needs or improve their processes. This lack of understanding can lead to skepticism, resistance, or even rejection of the proposed solution. By effectively communicating algorithm ideas in a way that is accessible and relatable to non-technical audiences, stakeholders are more likely to grasp the benefits and make informed decisions. Clear communication also fosters collaboration.

- Emphasize the business benefits and value the algorithm will provide. - Tie the technical capabilities to key objectives and pain points the stakeholders care about. - Highlight how it will improve efficiency, user experience, profitability etc.

No one cares about the algorithm. No one cares if it is O(n*(log n)^2). People care that the cloud expenses are reduced, or the response time is faster. Talk about the benefits the user/client/management can feel, not about how cool your solution is. Be ready to talk about the algorithm at several level, at progressive levels of metaphor and detail to matchbthe ask, but don't go there, and if appropriate, offer more offline rather than for a larger audience. Be sure of your ideas. If the algorithm is worth it, the benefits will be palpable. Help other feel them, without prevaricating. If you are not sure of the benefits, don't sell them. This may not be the time. Don't snow people with magic. Convince them with data.

From my experience, the most effective method to engage stakeholders is to demonstrate the benefits: reduce costs, enhance user experience (performance), solve issues, etc., provided that the objectives align with their vision and are cost-effective themselves.

This is the only approach that makes sense. Know your audience. Why would you even waste everyone's time trying to explain an algorithm to non-techies. Speak their language; money, time, efficiency...! They don't worry about your amazing algorithm, but only the results it gives. The algorithm is your (techie) job and the other things drive them. A bit of cross communication and visibility helps but trying to do a deep dive into each other's areas is not fruitful at all.

Begin with the problem you’re trying to solve. You need to morph into a storyteller here. Introduce your solution in a commonsensical way, avoid technical details here. Use visuals, White paper or whiteboard come in handy. Provide examples and analogies and solicit feedback to ensure non technical users remain engaged. Connect it to the problem and how it aligns with other parts of the business/organization goals.

Narratives and storytelling are the principal means of modern marketing as stories avoid triggering questions that arise in our minds when seeing or listening to strong statements. Sometimes fictional scenarios are also used. When available, true stories whose conditions ensure validity of the algorithm in achieving some objective and capture most of the behavior of the algorithm are the best basis for honest presentations.

Storytelling always makes the difficult concepts easier to understand. If you combine it with illustrative examples then it is even more satisfying.

By illustrating every facet of the algorithm through real-life examples, it becomes easier to visualize abstract computer science concepts in tangible scenarios.

I often use user stories to normalize the problems my clients have. By showing them that their problems are experienced by many other clients and are the normal result of doing their jobs correctly, they lower their shoulders and are much more open to hearing how these algorithmic solutions can help them.

- Recognize that not everyone needs the same level of technical depth. - Start with the simplest explanation and allow them to ask for more details if needed. - Provide resources and references for them to learn more on their own time.

I have always found drip feeding works well in "Educating" non tech folk! But differential strategies are reqd, horses for courses so.. (1) C-Suite won't give you the time to drip feed without first making instant "soup" great impression and answering their key concerns how does...XYZ add value and how much is it going 2 cost! (2) Having gained their partial or mystified provisional buy in ...the key ?? is simply to deliver. ?? (3) OK for heterogeneous other stakeholders who are non tech or less tech drip feeding ?? works well. (4) The old adage "make a good 1st impression" via short max 10 min stand ups in Agile ....works a does the old Chestnut (5) Win ?? hearts and minds, a wee bit of diplomacy via carefully crafted emails works too

There is a very common saying “ perfection is the enemy of getting it done “ it’s always important to be version 1 up and running to the entire data pipeline , data flow , ml Models , mlOps pipelines can be tested . Even though it’s not the perfect ml / llm solution it’s always advisable to get the version 1 rolled out and get early feedback from customers and stakeholders . There is no to little value of building a perfect solution with no users compared to building a solution with limited feature and iterate over it to pivot the product In the right direction

We can give examples or case studies that illustrate how the algorithm concepts can be used in real world situation. Also we can explain the algorithm’s process using charts, diagrams or presentations.

- Encourage questions and feedback from the stakeholders. - Check for understanding and adapt your explanations based on their responses. - Incorporate their perspective and revise your approach if needed.

Qui que ce soit en face de vous, écoutez les objections, les remarques. Montrez que vous êtes à l'écoute de votre auditoire. Si elles sont justes, n'hésiter pas le dire car vous emmènerez votre auditoire tout au long de votre exposé sans laisser personne qui ne soit pas convaincue quand vous aurez terminé. Pédagogie, patience et bienveillance tout en gardant le cap de votre exposé.

Provide access to rigorous technical reports or generate peer reviewed publications where possible for those interested in knowing the actual technical workings of algorithms. Deeper discussions with the non technical users are only possible one-on-one where there is interest. It is not generally possible to do it with a group as individuals with different backgrounds need different steps of communication.

1) Depending on the knowledge-level (say, class 10 etc.) of the stake-holder, I will simplify the algorithmic description of the problem. 2) Further, if needed I shall scale down the level of the algorithmic tutorial whereas the range of the minimum scale will start from broad overview level and, the maximum scale will end with the full technical description of the algorithm.

It is a sales job. Explain potential benefits in terms the stakeholders will understand, e.g. , problem solved, better performance, lower cost, less risk. Show cost versus benefit. Use graphic where possible. Show examples of use.

Start with the outcome. Align it properly to business objectives. That way you can sell the problem first. Then let them know how the algorithm in hand can create that outcome. Understanding the problem goes a long way in understanding the solution.

I'm very familiar with the challenge mentioned here. I was facing it each day when I was in charge of the transformation of the risk-management department with an AI-based approach because both C-level management and heads of operation teams were not technical enough to understand what and why my team doing, and why they should jeopardize business with algorithms they do not understand. For me, the solution was to foster trust in my team, not exact algorithms and models. So, we moved step-by-step with PoCs, extensive beta-testing, dedicated beta-testing operating team, and double-checks. The real issue is that the non-technical decision-makers do require not comprehension, they require reasons why they should make a bet on the approach