Seu cliente está convencido de que a IA revolucionará seus negócios. Como você gerencia suas expectativas infladas?

Managing a client with inflated expectations about AI requires a balanced approach that aligns their enthusiasm with practical realities 1. Reality Check Explain that while AI has transformative potential, it’s not a magic bullet that will solve all problems instantly. Share examples of AI projects that faced challenges, emphasizing that successful AI projects require time, resources, & refinement. 2. The Data Stress the importance of data. AI’s effectiveness largely depends on the quality of data it’s fed. Explain that AI models require clean, relevant, and well-structured data to deliver valuable insights. 3. Start with small wins Begin with small, manageable projects that can deliver quick wins & build confidence in AI within the org

One of the important considerations while working on any AI applied business solutions is that “not everything is learnable”. So not every dataset or every business logic can be taught so that machine would learn. Secondly, usually to often it is quite challenging to commercialize AI solutions in these days. It is crucial to understand exact problem and building proper evaluation metrics. Because sometimes it might not be very possible to see results (or ROI) in short term. Therefore, these details should be clearly discussed between stakeholders.

To manage your client’s inflated expectations, start by setting realistic goals based on data and proven AI capabilities. Educate them on the limitations and challenges of AI, emphasizing the need for a clear problem definition and quality data. Highlight successful case studies relevant to their industry, but stress the importance of incremental progress and continuous iteration. Establish milestones and metrics to track tangible benefits over time, ensuring that expectations are aligned with the pace of AI development and integration into their business. This approach builds trust and long-term success.

While we appreciate our clients foresight of adopting AI in their business, we should encourage them to start with appraisal of their readiness to adopt AI. A strategic plan to evaluate existing data, people, process and tools, along with due security & ethical considerations; would help them in seeing a clear path to meet their objective. Starting small by trying to leverage AI for simple cognitive tasks as use case for quick wins, would also help not only in boosting confidence but in also giving opportunity to learn from the unforeseeable unknowns.

Managing expectations is about aligning vision with reality. AI, like any revolutionary tool, has the power to transform, but it's not a magic wand. It's about helping the client see that true impact comes from thoughtful integration and iteration, not overnight success. We start by setting clear, measurable goals and showing the incremental wins that build toward that larger vision. When expectations are grounded in what's possible today, they stay inspired by the journey rather than disillusioned by the hype.

To manage this situation with clients, consider these steps: 1. Reality Check: Clarify that AI is a tool assisting businesses, not an immediate solution to all challenges. It needs high-quality data, proper maintenance, and time to adapt and improve. 2. Set Achievable Goals: Guide your client to establish realistic targets. Highlight AI's potential impact gradually over time rather than immediate transformation. 3. Gradual Integration: Help the client understand that the integration of AI technologies into business operations has to be gradual. This can prevent overwhelming their systems and staff and will lead to better long-term adoption.

The one example from the company Kaiser Permanente in the WSJ article today was how physicians first felt threatened by AI. They initially thought it was a threat to their autonomy and decision making. However, a shift has happened where they moved from reluctance and skepticism over to delight and now demand. There is a balance needed to solve the right problems using the tool of AI. And a reality check is often needed to temper expectations of what AI can actually accomplish. Hope this helps you.

I love clients' enthusiasm for AI's potential! However, I ensure they understand that revolutionizing their business with AI takes time. While AI can be a game-changer, it's not an overnight sensation. It requires quality data, ongoing maintenance, and continuous learning. I temper expectations by highlighting that AI's impact grows over time, delivering increasingly impressive results. With a focus on achievable goals and gradual integration, clients can harness AI's transformative power. As AI systems learn and improve, businesses can expect significant enhancements, leading to remarkable long-term benefits. Patience and persistence are key to unlocking AI's full potential.

In my personal experience I have met several customers that were reluctant to AI implementation in their projects. When talking about AI they felt a bit far from their expectations and, overall, not in li e with their needs. I quickly managed these issues by adding some KPIs based on the customer feedbacks. The result? Up to now we are addressing hundreds of projects!

To ensure successful AI implementation, stress that high-quality data is crucial. Advise your client to invest in cleaning, organizing, and securing their data, as AI performance relies on it. Without strong data, AI systems may underperform, leading to potential issues.

A robust data foundation is essential for the successful implementation of AI, as the quality of data directly impacts the performance of AI algorithms. It's crucial for clients to recognize that AI systems are only as effective as the data they are trained on. Encourage them to invest in thorough data cleaning, organization, and security, as these steps are vital for ensuring the accuracy and effectiveness of AI solutions.

Absolutely, the quality of data is fundamental to the success of any AI implementation. High-quality data ensures that AI models are accurate, reliable & capable of delivering meaningful insights. - Understand the Importance of Data Quality - High-quality data improves the accuracy of AI models, ensuring that predictions & classifications are reliable. Quality data helps reduce bias, leading to more fair & equitable outcomes. Reliable data enables better decision-making based on accurate & relevant insights. High-quality data builds trust in AI systems among stakeholders. Ensure data is collected from reliable & relevant sources. Verify credibility & accuracy of these sources. Collect data that is directly related to problem you’re solving

I would take to the time to understand their beliefs and in a polite and educational manner work through any misunderstandings the client has using case studies and facts. If the client is still not interested in the truth, I would suggest considering offering the client to find another provider. Brand reputation can be destroyed by one un happy client - despite your best efforts. You may also need to re-evaluate your own marketing materials to ensure they are factual and not misleading.

The potential of AI and GenAI applications is huge. Across all industries and areas of activity, there are a multitude of opportunities to speed up processes or save resources. But AI is not magic. It is the result of well-structured and implemented projects. The database that is available for training the applications is of particular importance. Companies must take initiatives to collect, cleanse, aggregate and process data. At the same time, those responsible must create the organisational conditions to ensure an optimal flow of data through the company.

To manage a client's inflated expectations about AI, emphasize the critical importance of a strong data foundation. Explain that high-quality, well-organized data is essential for any successful AI implementation. Illustrate how data quality directly impacts AI performance and provide examples of projects where poor data led to suboptimal results. This helps set realistic expectations and underscores the need for proper data management before diving into AI solutions.

AI is only as effective as the data it's trained on. For example, imagine trying to build a recommendation engine for an e-commerce platform. If the underlying customer data is incomplete or inaccurate—say, it’s missing key purchase histories or demographic details—the AI’s recommendations will be off-target, leading to poor customer experiences and missed sales opportunities. To avoid this, it’s crucial to invest in cleaning, organizing, and securing your data before implementing AI. By ensuring that your data is comprehensive and accurate, you set the stage for AI systems that deliver precise, actionable insights, driving better outcomes for your business. This preparation might seem time-consuming, but it’s mandatory.

For successful AI implementation, emphasize these key points to your client: Data Quality is Paramount: AI algorithms rely heavily on the data they're trained on. High-quality data is essential for accurate and effective AI outcomes. Invest in Data Preparation: Encourage your client to invest time and resources in cleaning, organizing, and securing their data. This preparation is crucial for AI success. Solid Data Foundation: Explain that without a strong data foundation, AI systems are likely to underperform, leading to frustration and setbacks. Long-Term Payoff: Assure them that upfront effort in data preparation will pay off, resulting in more reliable and impactful AI solutions.

When a client is eager about AI’s potential, it’s vital to manage expectations strategically. Start with a reality check—like how Netflix gradually introduced AI for recommendations before expanding its use. Develop a strategic AI roadmap, positioning AI as a long-term investment with phased implementations, much like how Amazon continuously refines its AI-driven logistics. Ensure robust data management, similar to how Google handles vast data for its AI projects. Foster a learning culture, inspired by Microsoft’s AI upskilling initiatives. By taking this approach, businesses can achieve sustainable and impactful AI integration.

If the customer's knowledge is at the conceptual level, I recommend a different approach. Avoid creating a strategy built solely on beliefs or someone else's knowledge. This creates weaknesses in your strategy-to-leadership foundation. Instead: 1. Think in stages, focusing on where there's a good match between today's problems or opportunities. This forms your foundation stage. 2. Build understanding through experience. Collect "battle scars" and form your own ideas about AI, backed by real-life feedbacks. This is your confirmation stage. 3. Then, look towards the horizon and develop a strategy where AI makes sense in your context. Build a dedicated AI strategy when information processing and insights are at the core of your business.

⧷ A revolução da IA não se trata de trocar um manual por um código, mas de trocar o código do DNA da sua empresa. ⧷ Imagine um software de IA como um bioengenheiro que esculpe a própria essência da sua organização. Não pense na IA como um software qualquer, mas como uma nova biologia de negócios. ⧷ Ao invés de apenas aplicar IA a um processo, pense na IA como uma ferramenta para remodelar esse processo, redefinir seu papel no mercado, e dar vida a uma nova versão do seu negócio, mais eficiente, adaptável e estratégica. ⧷ Essa é a verdadeira revolução. #ai #ia

Adopting AI strategically, rather than following trends, is crucial for organizations seeking to unlock its full potential. Start by identifying specific use cases—look for repetitive, data-heavy tasks or areas requiring complex decision-making where AI can add real value. Align AI initiatives with broader business goals to justify investment and ensure successful implementation. Integrating AI with existing technologies and human expertise fosters collaboration, while change management and upskilling teams prepare the organization for a smooth transition.

A client was swept up in AI hype, but the reality was complex. The team was eager to dive into the latest AI trends during the first meeting without considering business alignment. We had to ground the client with a strategic, business-focused approach. We examined actual problems and opportunities - where could AI genuinely add value versus what was flashy? The client resisted practical realities. However, examining the client's messy, siloed data led to a breakthrough. We had to address data issues to use advanced AI. They eventually saw the value in a pragmatic approach. AI pilots paved the path for ambitious initiatives aligned strategically with their digital strategy. They set realistic expectations and delivered sustainable value.

When managing client expectations of AI, we must not only give them a reality check but also ensure that their use of AI truly aligns with their business goals. Discuss their long-term objectives and challenges to determine how AI can contribute meaningfully. Whether it’s improving customer engagement, optimizing supply chains, or streamlining internal operations, AI should be seen as a tool that directly supports the company’s strategic vision. Help your client pinpoint high-impact use cases where AI can provide significant value. Focus on areas where AI can solve pressing problems or create new opportunities. By identifying targeted applications, you ensure that AI investments are both purposeful and effective.

When advising your client on AI adoption, emphasize the following: Strategic Purpose: AI should be adopted with clear, strategic goals in mind, not just because it’s trendy. It needs to address specific business needs. Identify Opportunities: Guide them in pinpointing business processes where AI can add value, such as improving decision-making, enhancing customer service, or boosting operational efficiency. Thoughtful Application: Help them understand that AI is most effective when applied thoughtfully, targeting areas where it can make a meaningful impact. Integration with Strategy: Encourage them to view AI as part of a broader digital transformation strategy, one that complements other technologies and leverages human expertise.

Businesses often have inflated expectations of AI, seeing it as a magical solution that can solve everything perfectly and instantly. However, the reality is different. AI is limited and typically trained for specific tasks only. Its effectiveness depends on the quality of the data it uses. To make the most of AI, businesses need to understand its true capabilities and set realistic expectations. They should plan phased integration and transformation to build trust in AI. Starting with simple AI solutions for repetitive tasks is a good approach for low budgets and quick results. For long-term and complex tasks, businesses should invest in quality data through good data governance, management, and ethical practices.

When allocating resources, we must first identify key roles and skills. Pinpoint those required to implement and manage AI within your client’s organization successfully. This may include data scientists, AI engineers, machine learning specialists, and IT professionals who can oversee the integration of AI technologies. Highlight the importance of having team members who not only understand AI but also can interpret data outputs and make strategic decisions based on AI insights. You should also assess your client’s current capabilities and identify gaps in skills and resources. Determine whether their existing team can support the AI implementation or if new hires or external consultants will be needed.

⭌ Defina um plano de "development of AI ecosystems". Inclui uma etapa inicial de pesquisa, análise de dados e diagnóstico das necessidades da empresa. ⭌ Em seguida, traga consultores não apenas de IA, mas de especialistas em comportamento, ética, análise de mercado, design e UX para montar uma equipe que cria as soluções de IA adequadas e garante a saúde do sistema. ⭌ O foco no conhecimento abrangente e no equilíbrio entre tecnologia e conhecimento humano mitigará a mentalidade "somente código", levando à otimização de investimentos e sucesso. #ai #ia

When discussing AI implementation with your client, highlight these key considerations: Investment in Talent: Emphasize the need for skilled professionals who can manage, interpret, and optimize AI systems effectively. Financial Commitment: Make them aware of the financial investment required for software, infrastructure, and training necessary for successful AI deployment. Resource Planning: Stress the importance of being upfront about these resource needs to help them plan effectively and avoid unexpected costs later. This transparency ensures that your client is fully prepared for the commitments involved in AI implementation, leading to a smoother and more successful adoption process.

Explain that while AI has the potential to revolutionize operations, its success heavily depends on how resources are allocated. Highlight that the right investment in talent, technology, and time is crucial. For AI to deliver real value, the organization needs skilled professionals to manage and interpret AI systems, robust data infrastructure to support AI models, and sufficient time for implementation and iteration.

It is important that the customer & stakeholders understand the strategic ROI for leveraging AI. The technology and talent investment, needs to align to the strategic outcomes of the customers with clear understanding of the ROI.

L'IA peut faire des merveilles, mais elle n'est pas magique. Il est essentiel de rappeler au client que la technologie seule ne suffit pas. Il faut des pros qualifiés, des investissements en logiciels et infrastructure, et une vraie stratégie derrière. En gérant ces attentes dès le départ, on évite les mauvaises surprises et on s'assure d'un projet réaliste et solide

Implementing AI requires a significant investment in both technology and talent. It's essential to recognize the need for skilled professionals who can manage and interpret AI systems, as their expertise is crucial for success. Additionally, financial investments in software, infrastructure, and potential training should be clearly discussed. By being upfront about the resources needed, clients can plan effectively and avoid unexpected costs down the line. This proactive approach not only ensures a smoother implementation but also sets the stage for maximizing the return on investment in AI initiatives.

En lugar de ver la asignación de recursos como un costo, ayude al cliente a entenderlo como una inversión estratégica en el futuro de la empresa. Proponga una planificación cuidadosa donde el talento especializado no solo gestione la IA, sino que también la impulse hacia nuevas fronteras. Además, sugiera una reserva de recursos para la innovación continua, permitiendo que la empresa se mantenga ágil y capaz de evolucionar con las tecnologías emergentes, garantizando así un retorno constante de la inversión en IA.

Los recursos a destinar son una consecuencia directa del alcance del proyecto a gestionar. Por lo tanto será muy importante no "inflar" las tareas para no sobrecargar con colaboradores que luego no tendran una tarea bien definida. También es clave que sean recursos debidamente entrenados y capacitados para sacar el mayor provecho y reducir los tiempos de ejecución.

Conduct a Thorough Assessment: Before implementing AI, assess your client's existing processes, data quality, and team capabilities. This will help identify areas where AI can truly add value and avoid overselling its potential. Prioritize Use Cases: Rather than trying to implement AI across the entire business at once, focus on high-impact use cases. This will allow you to demonstrate the benefits of AI quickly and build trust. Manage Expectations Throughout the Process: Set realistic timelines and communicate progress regularly. This will help prevent disappointment and ensure that your client remains aligned with your AI strategy.

With "deterministic" AI solutions, it has traditionally been easier to provide transparency of the training data. Mostly, this was due to businesses being responsible to curate, validate and clean the data. With LLMs, this is much harder to assess as many providers have been reluctant to share information about the training data. For instance, a LLM could generate content that results in copyright infringement even though it may not have been intended by the model developer.

Ethics in AI is crucial! As AI becomes more widespread, it's essential to address the ethical implications of its use. Clients should be aware of potential issues like: -Bias in AI algorithms -Lack of transparency and accountability -Impact on fairness and trust among customers and stakeholders By prioritizing ethics in AI, businesses can: -Promote fairness and transparency -Build trust with customers and stakeholders -Ensure responsible AI adoption Encouraging clients to adopt guidelines and best practices for AI ethics is key to fostering a culture of responsibility and trust.

Encourage the client to stay informed about advancements in AI technology and methodologies to leverage new opportunities. Provide ongoing training for the client’s team to ensure they are equipped to handle and maximize the benefits of evolving AI solutions.

Studies show that 60% of consumers are concerned about bias in AI algorithms, making ethical considerations essential for businesses. As AI becomes more prevalent, it's crucial to address these issues. Clients should be aware of the importance of transparency and accountability in AI systems. Encouraging them to consider the ethical implications of AI in their business practices is vital. Adopting guidelines that promote fairness and trust among customers and stakeholders not only enhances brand reputation but also fosters long-term relationships. By prioritizing ethics in AI, businesses can navigate challenges and build a more responsible future.

Responsible AI is essential for ensuring that the deployment of AI technologies is ethical, fair, and aligned with corporate values. By prioritizing responsible AI, organizations not only protect themselves from legal and reputational risks but also build trust with their customers and society, fostering sustainable innovation that benefits everyone.

As AI becomes more integrated into business processes, ethical considerations must not be overlooked. Bias in AI algorithms can undermine fairness and trust. Encourage clients to adopt transparent and accountable practices, ensuring their AI systems promote equity. Highlight the importance of guidelines that address these issues, building a foundation of trust among customers and stakeholders. Prioritizing ethics in AI not only enhances brand reputation but also fosters long-term, sustainable success in the digital age.

L'IA peut faire des merveilles, mais il est essentiel de rester réaliste. En tant que conseiller, je rappelle souvent à mes clients que l'IA n'est pas une solution magique. Il faut d'abord aborder les questions d'éthique, comme les biais algorithmiques, la transparence et la responsabilité. Adopter l'IA, oui, mais de manière éclairée et éthique pour maintenir la confiance avec leurs clients.

Más allá de evitar riesgos, la ética en IA puede convertirse en un diferenciador competitivo. Propón al cliente que adopte un enfoque de "ética como innovación", donde la transparencia, la equidad y la responsabilidad no solo sean cumplidas, sino que sean pilares centrales de la estrategia de IA. Esto no solo protegerá a la empresa de posibles problemas regulatorios, sino que también fortalecerá su reputación, atrayendo a clientes que valoran prácticas empresariales responsables y sostenibles.

Se deben realizar auditorías de sesgos periódicas para identificar y mitigar posibles prejuicios en los algoritmos. Es fundamental implementar mecanismos de retroalimentación continua para que los usuarios puedan reportar problemas y mejorar los sistemas de IA de manera proactiva. También sería valioso desarrollar políticas claras sobre la privacidad de los datos y asegurar que la IA se alinee con las regulaciones legales y normativas vigentes, protegiendo los derechos de los usuarios. Finalmente, es recomendable que el cliente invierta en educación y capacitación tanto para su equipo interno como para sus usuarios finales, asegurando que todos comprendan y puedan manejar adecuadamente las tecnologías de IA en uso.

We are all biased. We have different experiences, knowledge, and beliefs that shape our biases. The key is to be aware and minimize them. AI is not much different: 1. It is trained on specific data sets 2. It is trained in a specific way 3. It is limited by what it knows Unlike humans, AI's biases differ in these ways: 1. It does not have "experiences" that shape its thinking 2. It does not have emotions that impact the bias 3. It can be very aware of its biases That last point is where you can leverage the power of AI: Ask it to identify its biases! You wouldn't typically ask a human, but you can ask AI by adding this snippet to your prompt: Define 3 possible biases in your results and describe how they may impact my decisions.

Implementing AI is a process of ongoing learning. Predictive AI may uncover insights not previously expected. Generative AI can have benefits not initially imagined. Be flexible, celebrate and enhance the wins, and cull your failures quickly. Effectively using AI is a journey of ongoing learning

Find resources you trust and listen to them. The AI landscape is rapidly evolving and will do so for the next few years. If you can, find someone that can span these areas: 1. Technical: strong technical background 2. Training/Development: is able to turn complex concepts into easy-to-understand lessons 3. Business: will help you define how to leverage AI for your industry or profession While this may seem like a unicorn, there are people out there with the skills and background to do this.

It's crucial to emphasize the importance of continuous learning and flexibility in the AI journey. AI technology is advancing quickly, with fresh innovations and strategies emerging all the time. It's important to find a balance too, as we can become overwhelmed by all the information available. Encourage your client to remain proactive in staying updated and to be open to refining their AI strategies as new opportunities arise. By embracing this mindset, they’ll be better positioned to harness AI effectively and sustain a competitive advantage in their industry.

Educate on AI capabilities - Explain AI's strengths and limits. Set realistic goals - Align expectations with possibilities. Emphasize iterative development - Highlight ongoing refinement needs. Encourage client involvement - Promote active learning and adjustments. Communicate transparently - Keep discussions grounded in reality. Provide continuous updates - Regularly share progress and insights. Balance optimism with caution - Foster a practical approach to AI.

Create personalized learning paths: Tailor training programs to individual employee needs based on their roles and performance. Foster a culture of continuous learning: Encourage employees to explore new AI technologies and share their insights with the team. Partner with AI experts: Collaborate with external experts to stay up-to-date on the latest AI developments. Measure and track progress: Regularly assess the impact of your learning initiatives and make adjustments as needed.

AI is one of the fastest-evolving fields in technology, with breakthroughs happening at an unprecedented pace. From improvements in machine learning algorithms to advancements in natural language processing and the development of new AI frameworks, staying updated on the latest developments is essential. Encourage your clients to invest in ongoing education—whether through attending industry conferences, participating in AI-focused webinars, or subscribing to leading AI publications.

How do you know your client is wrong? Your statement “inflated expectation” is also subjective and not sure what led you to believe it’s inflated.

Create a culture of experimentation. Set aside resources for small-scale pilots of new techniques or applications. I use a "10% time" rule - 10% of resources dedicated to exploring new ideas. It keeps the project fresh and can lead to unexpected breakthroughs. Establish feedback loops. Not just for the AI system, but for the entire process. Regular reviews, lessons learned sessions, user feedback. I've had projects completely pivot based on insights from these sessions, and they were better for it. And most importantly, make sure the client understands that flexibility is key. The AI strategy that makes sense today might be outdated in a year. Build in regular strategic reviews and be prepared to change course if needed.

El verdadero valor de la IA no reside en su implementación inicial, sino en la capacidad de aprender y adaptarse. Ayuda al cliente a construir un ciclo de retroalimentación constante, donde cada implementación de IA sea una oportunidad para aprender y mejorar. Plantea la idea de que la innovación no es un destino, sino un viaje continuo, y que la disposición a evolucionar con la tecnología es lo que separa a los líderes de los seguidores en un mercado competitivo.

Continuous learning is crucial in AI to stay ahead in a rapidly evolving field. Encourage your client to invest in ongoing education and adaptability. By staying current with new developments and being ready to pivot when necessary, they can ensure that their AI strategy remains effective and aligned with industry trends. This proactive approach not only maximizes the benefits of AI but also strengthens their competitive position.