How do you communicate AI value proposition to clients?

Understanding the client's pain points might be better described as "finding" the customers pain points. AI is such a big term, and there is still some education to be done (in most cases), so it's wise not to assume the client has a firm grasp on their pain points, many of which may not have anything to do with technology. As with any technology, alignment of goals is a great place to start the conversation. Engaging appropriate key stakeholders is always wise to ascertain pain points, as well as to identify potential risks for the client. By engaging with stakeholders throughout the business, gaps and opportunities often come to the surface.

Before you start any work you need to assess what the client's goals and pain points are. This is where you have a project plan in place. This will give you a structure to narrow down the business requirements for the client.

To effectively convey the AI value proposition, it's crucial to grasp your client's goals and challenges. Tailor the message to address their specific pain points, using real-world examples in their industry. Showcase AI's adaptability, emphasizing long-term benefits, and foster open communication. Present it as a strategic partnership with a clear implementation roadmap, ensuring a seamless integration. By aligning AI solutions with their objectives and demonstrating tangible outcomes, you establish the technology as a catalyst for achieving their business goals.

La Inteligencia Artificial es una tecnología de propósito general que puede ser aplicada a infinidad de casos de uso, por lo que es crucial identificar las áreas donde puede tener el mayor impacto y agregar valor. Por tanto, es importante reflexionar sobre cómo y dónde vamos a aplicar la Inteligencia Artificial a nuestro negocio. Para para ello debemos: - Entender qué es y qué puede hacer la IA - Analizar nuestro negocio para entender los puntos críticos donde aplicarla - Dise?ar una visión - Priorizar los Quick Wins

While AI is the need of the hour, it isn't easy to convince clients of its benefits. The biggest challenges still remain data privacy and false positive/negative results. The key is to start with a limited scope and showcase the benefits of AI-powered solutions in terms of SLA reduction, incident closure, etc. Then, one can accordingly increase the scope.

Many clients are skeptical when it comes to changing existing workflows, especially when AI introduces new challenges and risks to which they have not yet been exposed. We can build that trust by running AI projects separately and in the background of their ongoing projects. In legal tech, showing lawyers a piece of evidence that they missed but the AI caught usually convinces them that existing workflows are not perfect and that AI helps to mitigate the risk of missing vital evidence for court.

It's crucial to tailor AI solutions directly to a client's specific challenges and problems to ensure relevance and capture interest. Demonstrating how AI can address their unique & individual pain points and enhance their operations can be effectively achieved through a tailored demo or showcasing credentials highlighting past successes. This approach not only makes the potential impact of AI tangible for the client but also underscores the direct value contribution of AI in solving their problems. Using specific examples and case studies during these demonstrations further illustrates how AI can enhance efficiency, productivity, and overall business outcomes.

Latest trending topic in IT-Software or overall technology space is Artificial Intelligence (LLMs). LinkedIn have recently added these AI generated conversation starters topics (each having multiple collaborative articles), which might have been identified via vast database of top hashtags/ keywords by LinkedIn content itself and otherwise. Also the framework of each collaborative article is also generated by AI, and shares sample solutions to each stage of solving the problem presented. This readymade framework of topics, and for identified topics such initial framework is a great aid to initiate conversations in such multiple-skill crowd meeting. Also this practical exercise helps everyone participating relate to the AI benefits.

This is very much the case for any and all solution that are put forward, not just AI. Putting a keen focus on the outcomes for the customer that revolve around their specific challenges is always going to be better than "AI good...got clever technical bits, you want see feature"

The old "MBE" management style: senior levels are called out when something arises that is out of the ordinary. In the new AI era we envision Augmented Management By Exception: it is any human employee that shall intervene when a machine reaches its limit or detects an anomaly in a process. Even more: those standard processes that in the past were established only by human experts, are now also patterns that machines identify and alert on deviations from the "norm". Data driven organisations will benefit of AI much more as it will augment employees capacity to deal with day-to-day problems autonomously and drive improvements. Augmented Management by Exception concept focuses human talent in real value added tasks that enrich our work.

The obvious challenge of course in this example is that to create a common framework / topics / questionnaire for a diverse crowd, the language and sample solution shared by AI become too generic. This can also be exhibited by taking example of another topic(say 'Customer Relationship Management'), where out of a pool of total around 40 questions on it (collaborative articles), many would clearly show very trivial or non-relevant to the subject matter experts (CRM in this case). Also the much more relevant common problem statements totally missing in list is also sharing the limitations of AI, and risks of using such list as comprehensive by a newbie.

Addressing challenges and risks transparently is key. Openly discussing potential pitfalls or concerns ensures a realistic understanding. This approach fosters trust and allows for collaborative problem-solving, emphasizing the importance of ethical and responsible AI deployment.

There should be transparency regarding the challenges and risks of AI, emphasising the balance between technology and human oversight. AI solutions should align with existing operations, ensuring smooth adoption without disrupting workflows or culture. Adding value, not replacing it. Recognising the importance of human judgment, underpinning that AI should complement, not replace, human decision-making, ensuring that outcomes are always subject to expert review and intervention. By proactively addressing these aspects, you hope to build trust and showcase commitment to responsible AI deployment, balancing innovation with the invaluable role of human expertise.

AI comes with various risks and challenges that need to be handled. - AI models can inherit biases from training data, leading to unfair outcomes. Regular audits, diverse datasets, and fairness-aware algorithms can help - AI systems can raise ethical dilemmas, such as job displacement, decision-making accountability, and unintended consequences. Ethical frameworks and interdisciplinary teams can guide responsible AI development - Ensuring AI systems are reliable and transparent is crucial for building trust. Rigorous testing, explainability, and model monitoring can enhance reliability and transparency - Adhering to regulations is essential to avoid legal consequences. Compliance checks and staying updated on regulatory changes are key

Again, I believe you could remove "AI" from this part of the article and it would be completely relevant for any other project. Having the ability to showcase a plan and your competence in that specific area is a key part of credibility, but make it collaborative...Client hate having a load of other people you have done work for thrown in their face. Keep it simple. From a phycological perspective, only those that are not confident in themselves feel the need to over-prove their value and clients will pick up on that.

As mentioned in example of challenges/ risks/ limitations with the initial AI generated content only on a topic, it gives clear indication that human AI experts involvement & guidance is required before using such frameworks for say commercial usage. This happens by way of experts contributing by their real world expertise on the subject, like one shared by an alumni on topic 'Customer Relationship Mgmt.' articles. If the contributions are actually valuable to others, chances are the contributor may even get adjudged at 'Top Voice of CRM'. Also in general for LinkedIn itself, all these contributions including this one would help them to review and update their model periodically, and make the AI generated content more useful!

Demonstrating our expertise and capabilities is essential. Showcasing successful AI implementations and case studies builds credibility. Highlighting the proficiency of the technical team and the proven track record reassures clients of ability to deliver tangible value.

Emphasize that you have a clear and realistic plan, timeline, and budget for the AI project. Utilizing references and case studies can effectively present your past success stories and validate your value. This will build confidence in your work and provides evidence of the competence as a reliable partner for AI initiatives.

To demonstrate our expertise and capabilities as technology consultants, we emphasize our track record of successful AI implementations and our deep understanding of client needs. Trust me, there is nothing works better than case studies and success stories to impress the client. We showcase case studies, testimonials, and project portfolios that highlight our ability to deliver high-quality solutions that meet or exceed expectations. Not to miss, we also engage in open and transparent communication with clients, actively listening to their concerns and providing clear explanations of our proposed solutions.

For the same example in sequential questions response above by this user (Alumni Meet conversation starter, and meeting the Goals), an activity maybe done leveraging the combined 'human' expertise aspect of all participants to identify the initial list of main topics, for future knowledge sharing dedicated sessions for students, interested alumni from IT-Software space. For topic of 'AI' itself, experts may share their experience in advance before meet on these threads (articles) by LinkedIn, which further forms base to identify related experts and do in-person brainstorming during course of alumni meet. For such big crowd activities, 'engagement' best happens if participants get opportunity to participate before/ during/ after the event!

Engaging clients in a dialogue promotes collaboration. By fostering open communication, encourage clients to share insights and concerns, ensuring a tailored AI solution that precisely meets their needs. This collaborative approach strengthens the client-provider relationship, making them integral contributors to the AI implementation process.

In my opinion, the best way to engage clients effectively is to prioritize active and empathetic listening. By actively seeking their input, asking open-ended questions, and genuinely understanding their perspective, expectations, and concerns, we create a dialogue that fosters trust and collaboration. Our goal must be to address their doubts and objections with clarity and empathy, ensuring that their needs and preferences are fully understood and respected. Also, encourage the clients to ask questions, share feedback, and express concerns openly, knowing that their insights are invaluable in shaping successful outcomes. As Tech Consultants, we use genuine dialogue and mutual respect to forge strong partnerships.

When presenting the AI value proposition, highlight efficiency gains, data-driven decision-making, and scalability. Use real examples to showcase practical applications. Address implementation concerns with user-friendly interfaces and integration capabilities. Stress cost savings through automation and resource optimization. Emphasize AI's role in enhancing customer experiences and its adaptability to industry trends. Offer pilot programs for immediate benefits, building client confidence. Ensure transparency on data security and compliance. Position AI as a strategic investment aligned with long-term goals, emphasizing ongoing support and innovation for sustainable growth

Personally, I feel this article doesn't relate much to AI, but it still provides value in terms of the fundamentals of providing a value proposition.

It all boils down to ROI (Return on Investment). Is there a clear business case? AI certainly provides many clear opportunities for investing here, but not all use cases have a clear return. Ensure the flashy hype is grounded in reality of business.