AI systems can offer powerful solutions to complex problems, but they can also raise ethical and social concerns for your clients. How can you help them understand the value of your AI system and address their potential doubts and fears? Here are some tips to communicate effectively and build trust with your clients.
Before you present your AI system, make sure you understand the problem that your client is trying to solve and how your system can help them achieve their goals. Use clear and simple language to explain how your system works, what data it uses, and what outcomes it produces. Avoid jargon and technical details that might confuse or overwhelm your client. Focus on the benefits and value of your system, not the features and functions.
Technology is never the answer to the problem. Architecturing the solution with different technology enablers like AI, IoT , AR/VR , IoT ++ will define the success of the solution. During the architecting phase one should be able to define and grossly estimate the value of AI to solve the problem how efficiently and quickly. This should help the clients embrace the tech to solve their problems. Also its super important for the AI solution providers to set the expecting around ROI, AI infrastructure requirements and CI/CD for the same. Never oversell: AI is an emerging tech and it can already do a lot of wonders but could avoid placing AI as the solution for anything and everything unless proved.
To ensure the success of any AI project, it is crucial to secure buy-in from the CEO as a starting point. Additionally, it is important to gather key stakeholders such as department heads, finance, HR, technology experts, vendors, customers, and a business strategist. Bringing together this diverse team of individuals enables effective collaboration and the development of optimal solutions. By adopting a team approach, organizations can ensure seamless implementation of AI projects and provide everyone involved with the opportunity to reap the benefits.
Effective communication is the key to connecting technical and non-technical professionals in today's fast-paced world. When we explain technical concepts by showcasing their problem-solving potential, we simplify complex ideas and highlight their real-world significance. This approach fosters understanding, sparks productive discussions, and aids organizations in making decisions that align with their goals. This skill is especially valuable in the AI domain, where translating theory into practical solutions can truly set you apart. Clients are mostly not interested in AI algorithms but in how AI can be applied to solve their business problems.
My process: --Case Studies: I share case studies demonstrating how our AI system has solved similar problems, emphasizing measurable results. --Interactive Demos: Where possible, I provide demos that allow clients to see the system in action, addressing their specific pain points. --Client-Centric Benefits: I highlight benefits directly relevant to the client's industry, such as time savings in data processing or enhanced accuracy in analytics. --Relate to Their Workflow: I discuss how our system integrates with their existing workflow, minimizing disruption while maximizing efficiency. --Success Metrics: I outline the key performance indicators we’ve seen improved in similar deployments, providing a clear picture of potential returns.
?? How do you help clients understand the value of your AI solution? - Aim to understand your client’s pain points and the underlying root causes - Explain, in everyday language, how your AI solution can address their pain points. - Share examples of other clients who had similar pain points that you helped with your AI solution. Focus on why it’s valuable first and foremost and not the what - number of data points it was trained on, time spent training it etc. Example of the "Why" We created this solution to protect your network from bad actors so your clients can trust their data is safe. ?? Like to support this
One of the best ways to convince your client of the value of your AI system is to show them the results and the impact of your system on their business or social issue. Use concrete examples, case studies, testimonials, or visualizations to illustrate how your system can improve efficiency, accuracy, quality, or customer satisfaction. Highlight how your system can help your client save time, money, or resources, or how it can create new opportunities or innovations.
You also need to be completely transparent before you start as to what the costs of the AI solution are. Many clients will be surprised at the ongoing cost of operation of an AI model when it is implemented properly, i.e. with ongoing monitoring, evaluation, and retraining. In a regulated environment, with Model Validation on top of Model Maintenance, these costs add up and can also lead to models being unresponsive as changes are held up in approval queues.
I focus on demonstrating tangible results and impacts. This involves presenting detailed case studies that showcase how our AI system has significantly improved efficiency, accuracy, and quality in real-world scenarios. I also share testimonials from satisfied clients, highlighting how the system addressed their unique needs and challenges. Visual comparisons, like before-and-after scenarios, effectively illustrate the improvements brought about by our AI system, particularly in enhancing business processes. Emphasizing the time and cost savings achieved through our system helps in quantifying its benefits. Showcasing any innovative features or capabilities of our system that have led to new opportunities for clients plays a key role.
Many projects may also have a cold-start problem, where there may not be enough data initially to fully showcase the power of an AI solution. In such cases, it is important to clarify that the performance of the initial prototype is not reflective of what can be achieved. It is important to educate the management about the need for initial investment into collecting data, beta launching a product, learning & improvising, and the potential benefits it can provide to the organization later on when its ready for a full release.
The value of the AI system has yo be measures in the business context. Examples include: - % of increased revenues - % of decreased costs - quantified risks avoided - % improvement of customer services
For a successful long term AI deployment, it's super important to start small and have quick wins. AI solutions demand a different breed of hardware and skillsets. This would incur investment or funding. To have a successful small win it's important to identify the use case where you feel AI can make a huge impact and build the POC. ROI metrics captured in general highlight - manhours saved, - increased efficiency - prediction and solving problems which was impossible without AI And ++
Your client may have some concerns or questions about the risks and the challenges of using your AI system. They may wonder about the reliability, security, or fairness of your system, or how it will affect their employees, customers, or stakeholders. Be honest and transparent about the limitations, uncertainties, or trade-offs of your system, and how you plan to mitigate or overcome them. Explain how you follow ethical and social principles and standards in designing, developing, and deploying your system, and how you monitor and evaluate its performance and impact.
As is often the case, the most important thing is transparency. You must explain which AI models are used, how they have been trained, where they are deployed, whether they are learning from the data, and how this learning will be used. While most generative AI models offer a very low level of explainability, you can compensate by explaining how these generative models are used by your application, and what you are doing to reduce the risks of hallucinations, bias, or intellectual property infringement. When doing so, you must strike a delicate balance between offering as much transparency as possible, and protecting your own IP. Start by encapsulating some logic into black-box components and having solid End User License Agreements.
I ensure to address their concerns about risks and challenges transparently. I openly discuss the system's limitations, such as certain areas where the AI might need more data to improve, or scenarios where it may require human oversight. Addressing security concerns is paramount; I explain the measures in place to protect data and ensure the reliability and fairness of the system. This includes discussing how our AI algorithms are designed to avoid biases and ensure ethical decision-making. I also acknowledge the potential impact on their employees, customers, and stakeholders. For example, I explain how we aim to complement human work rather than replace it, and how our system can enhance customer experiences.
In critical systems, it is prudent to deploy AI solutions as an assistive option to the human-in-charge rather than completely allowing AI to make decisions and take actions. Critical systems by nature may have far reaching consequences when faced with previously unseen/novel scenarios. Explainability then becomes a key issue for the human-in-charge to trust and use the AI system. This line of research is still in its nascent stages and therefore adequate risk management techniques need to be kept in place. Risk-management measures may involve limiting the probability and impact of worst-case scenarios, having early-warning systems to detect potential bad things happening and taking corrective measures, alerting chain of command, etc.
Design AI system that client can trust by following best practices like: ?? Follow human centric design to earn client's trust. Assist or augment before automation. ?? Design AI system with Observability and Explainability. Setup monitoring with actionable alerts using logging and tracing tools. ?? Fairness is measurable and can be monitored. Design system using fairness metrics across all segments and rare classes. ?? World Changes, Model Decays. Mitigate risk by monitoring & following retraining policy. ?? Build Responsible AI into system, mitigate informational and behavioural harms. ?? Build Legal compliant system by Annonimizing Data, Encryption, Access Control. ?? If possible Build Robust and Scalable system on Cloud.
No AI solution is 100% precise. Being upfront about the limitations of the system with your clients can help mitigate any risk involved. Rather than making them see "more" value, make them see the "right" value of the AI solution.
Another way to help your client understand the value of your AI system is to involve them and their users in the process of developing, testing, and implementing your system. Ask for their feedback, suggestions, or preferences, and show them how you incorporate them into your system. Encourage them to try out your system, experiment with different scenarios, or ask questions. Make them feel part of the solution, not just the recipient.
Involving clients and users directly in the development and implementation of our AI systems is a strategy I find incredibly effective. By engaging them in the process, we can gather valuable feedback, which is crucial for tailoring our system to meet their specific needs. We invite clients to provide suggestions and preferences, demonstrating how their input directly influences the system’s development. We encourage clients and their users to interact with the system, explore different scenarios, and ask questions. This hands-on experience allows them to see the practical benefits and capabilities of our AI solution firsthand.
Your biggest nightmare while developing AI systems can stem from not incorporating people with industry specific knowledge. Involve the clients during feature engineering and model evaluation to build a cycle of continuous improvement and bring out the potential of your system.
The most practical step to ensure your client understands the value of your AI solution is to involve the client and the users of the AI system you're building, from the inception of the project, right till the end. That's a foolproof way to know what you're building is something they actually want and will use. Also, this strategy helps in getting early feedback and helps everyone invested in the success of the AI system, as much as you. Win-Win!
Communicate often and clearly. Avoid creating unrealistic expectatins. Get feedback early. Sometimes you might think they understood what you said, but they didn't. Especially if the stakeholders aren't used to Advanced Statistics. Find ways to make them experience your product or feature in a familiar setting. Build a quick prototype with toy data - or if available, a small subset of real data - that gets updated iteratively. Create a simple but relevant UX that stakeholders can access to "play around". But also be careful not to share too much, as features that are too experimental might hurt confidence if things go wrong.
Client involvement can turn skeptics into advocates. Use agile methodologies to involve the client in the development process. Regularly update them on progress and setbacks, and solicit their feedback. This co-creation approach not only makes the client feel valued but also helps you tailor the system more closely to their needs. It's a win-win situation that fosters trust and collaboration.
Finally, you can help your client understand the value of your AI system by providing them with support and training. Make sure they have access to documentation, tutorials, or guides that explain how to use your system effectively and safely. Offer them technical assistance, maintenance, or updates as needed. Train them on how to interpret, analyze, or communicate the results and the impact of your system. Help them build their confidence and competence in using your system.
Offer continuous assistance and troubleshooting. Just as a personal fitness trainer guides you through challenges, provide a support system to address any issues or queries. This helps in maintaining the system's health and performance. Conduct training sessions to empower clients and users. Think of it as teaching someone to use a new gadget. Training ensures that users are not only aware of the system's capabilities but also proficient in utilizing them effectively. This reduces dependency and enhances the overall user experience. Together, support and training create a collaborative environment where clients feel empowered and confident in maximizing the potential of the AI system.
Providing comprehensive support and training is essential for helping clients appreciate the value of our AI system. I ensure they have access to clear and user-friendly documentation, tutorials, and guides. These resources are designed to help them understand not just how to use the system, but also how to leverage it effectively for their specific needs. Technical assistance is readily available to address any issues or questions they might have. We also keep the system up-to-date with regular maintenance and updates, ensuring it stays relevant and efficient.
-Implement a "Train the Trainer" approach: Equip pivotal staff members within the client's team to become internal trainers. This strategy lends sustainability to the system's adoption process. -Role-Specific Training Programs: Develop training curricula uniquely tailored to the various job functions within the client's organization, guaranteeing that users grasp the facets directly pertinent to their roles.
Continuous client training is more important than one time training when it comes to intelligent systems. Once the system is live, offer knowledge sharing sessions on how the system is evolving. Even if there is no change in the model per se, the system results can vary with time due to reasons such as data draft. Hence, having checkpoints to provide continuous support and training helps the customer to always be up to speed and understand the value of the AI system.
Post-implementation support can be a deal-breaker. Offer a comprehensive training program that caters to different learning styles—videos, webinars, written guides, etc. Also, provide a dedicated customer success manager who can address queries and issues promptly. This ensures that the client not only understands how to use your system but also feels supported throughout their journey.
Customers value the aptness of your AI solution to their business. Compare and contrast AI methodologies from both technical and business perspective and share what your solutions brings uniquely to the table. This might be higher accuracy, better performance or minimal data and resources needed for training. Sharing data-backed comparisons with the customer can make them understand how well your system serves their use case.
Measuring ROI (Return on Investment) : Clearly illustrate how the AI system contributes to the client's bottom line, whether through cost savings, revenue generation, or efficiency improvements. Tangible metrics, such as increased sales, reduced operational costs, or enhanced customer satisfaction, provide a concrete understanding of the system's value. This not only reassures the client but also establishes a basis for ongoing collaboration and investment.
Iterate and Evolve Two crucial points to consider: 1) Highlight that your AI system is not static, it evolves to meet changing needs and advancements. 2) Assure clients that you have a roadmap for continuous improvement, incorporating feedback and staying abreast of technological advancements. This not only ensures sustained value but also positions your AI solution as a dynamic and adaptive asset for the long term.
Focus on the human aspect of the technology. While AI systems are talk of the town, there is also much apprehension that AI will lead to some roles being eliminated quickly. When designing AI systems and speaking to clients, we need to address how we are creating productivity tools and not trying to replace human creativity. AI systems can at best augment current work process. This assurance will help in faster adoption of AI technology.
This whole article is misaligned due to the title and consistent use of "your system". It will almost always be sub-optimal to install a pre-designed recipe. Customisation is usually required. Hence, "your solution" would be preferable to "your system" in all instances of this article.