Here's how you can enhance outcomes through collaboration between AI experts and domain specialists.

Shared goals is important, but often times its not sufficient. Its the The AI experts and Domain specialists interaction that determines success of program and it is here that their inherent passions that takes over. For eg. in pharma - Everyone wants to get the best drugs to patients faster. However, inspite of this common ground -- AI specialists are going to be passionate about predictive analytics using multi-omics data and GenAI and automation, whereas business is going to be passionate about understanding how much value is getting unlocked from the technology. Each side getting to care a little bit about each others passion and value drivers is important for people to converge towards the common goal and make progress

No doubt about it. Working mutually towards shared goals helps build collaborations which could also lead to mutual gains. The more people you have on board, the bigger the impact anyone could make. We just need to ensure that we're following the right path.

It’s important that both AI experts and domain specialists work together to define clear, measurable objectives. This could involve setting specific targets for the AI system’s performance. Regular communication and knowledge sharing sessions can then help both parties stay aligned on the goals and progress. This can include joint workshops, regular meetings, and shared documentation. Finally, there should be agreed-upon metrics to measure the outcomes of the AI solution.

Sometimes simply having the right framework in place helps. At Salesforce, the V2MOM model (Vision, Values, Methods, Obstacles, and Measures) and at Aquiva Labs, where I currently work, the Entrepreneurial Operating System’s V/TO (Vision/Traction Organizer) serve as key tools for company-wide alignment (not just AI experts and domain specialists). These frameworks clarify goals, define methods, and map out pathways to success. This way everyone understands their role in achieving shared objectives. It enhances engagement, streamlines decision-making and transforms strategic planning into actionable outcomes, driving collaboration and results.

AI experts often have a deep understanding of machine learning algorithms and data processing, but may lack insight into the practical applications within a specific domain. Conversely, domain specialists may have a wealth of knowledge in their field but limited understanding of AI capabilities. Bridging this gap through knowledge exchange is essential. Organizing workshops and seminars where AI experts can explain AI concepts in layman’s terms, and domain specialists can share their expertise, can be very effective. This fosters a common language and understanding.

A inteligência artificial (IA) tem um impacto significativo na melhoria das entregas em várias áreas de negócios. A colaboração entre especialistas em IA e áreas de negócios é fundamental para aproveitar ao máximo o potencial da #IA. Os especialistas podem fornecer soluções personalizadas para problemas específicos, enquanto as áreas de negócios fornecem informações valiosas sobre as necessidades e desafios do setor. Juntos, eles podem trabalhar para desenvolver soluções que aumentem a eficiência, reduzam custos e melhorem a qualidade das entregas. Além disso, a IA pode ajudar a automatizar processos repetitivos, liberando tempo e recursos para tarefas mais importantes.

Focus on data strategy and governance first. Then focus on data quality. This is where most businesses fail. Look at what Jensen Huang said - "we make hardware for mathematicians, for scientists, but businesses don't hire such".

This is where domain specialists play a key role: -They can assist in curating data sets that are most relevant for the AI’s purpose. This might involve selecting the right variables, identifying outliers, or providing context for data anomalies. -They provide context that helps AI experts understand the ‘why’ behind the data. This is crucial for building models that are not just data-driven but also context-aware. -They can guide AI experts in understanding the ethical implications of data usage, ensuring that AI solutions are developed responsibly.

AI thrives on data, and industry experts hold the key to unlocking specialized information. This brings insight, but understanding their true meaning requires more than just raw data. By collaborating with domain specialists, AI developers gain a deep understanding of the nuances within the data. This improves the chances that information is used accurately and effectively to train AI models.

Dados de qualidade aumentam o sucesso de algoritmos de ML e IA. Se possível colete seus próprios dados para garantir o controle de qualidade (porém é um processo demorado e caro). Garanta que os dados coletados sejam representativos da população para evitar vieses. Limpe e remova dados duplicados, trate os valores ausentes, padronize e documente tudo. Lembre-se: algoritmos ajustados por um engenheiro médio podem superar aqueles construídos pelos maiores especialistas do mundo se o engenheiro médio tiver acesso a melhores dados.

Leveraging data insights is key to enhancing collaboration. In a project focused on retail, we combined the expertise of AI specialists and retail managers. By analyzing data patterns and trends, we discovered crucial insights that significantly improved inventory management and customer satisfaction. These data-driven insights bridged the gap between technical capabilities and domain-specific needs, ensuring that our solutions were both practical and impactful. This collaboration not only optimized our processes but also demonstrated the power of integrating diverse perspectives to drive innovation and success.

In my experience, domain specialists provide rich, industry-specific data and context essential for effective AI training. Their collaboration ensures AI models learn from accurate, relevant information, enhancing performance and reliability.

I've found that inviting a domain expert as a partner in developing the AI system creates a win-win situation. The team gains valuable domain-specific knowledge and a continuous feedback loop, while the business gains a deeper understanding of the system's capabilities and limitations.

Iterative development ensures ongoing refinement of AI systems through real-world feedback, enhancing their sophistication and value over time. Collaboration extends beyond deployment, enabling continuous fine-tuning to meet evolving domain needs.

Iterative development is important in enhancing outcomes through collaboration between AI experts and domain specialists. It allows for continuous refinement of AI systems based on real-world feedback. By incorporating domain specialists' insights and experiences, AI models can be fine-tuned to better address specific challenges and deliver more accurate results. This iterative approach fosters innovation, enabling the development of AI systems that continuously improve and adapt to evolving needs. It ensures that AI solutions remain relevant, effective, and impactful in the long run, ultimately driving positive change and transforming industries.

Iteration is key to successful collaboration. Rather than building a monolithic AI solution, teams should develop prototypes and models iteratively. Domain specialists play a crucial role in evaluating these prototypes. Their feedback helps refine the AI system, making it more relevant, practical, and aligned with real-world needs. By involving domain experts early and often, AI teams can avoid costly missteps and ensure that the final product meets user expectations. For example, when developing a chatbot for customer service, collaboration between UX designers, customer support representatives, and NLP experts ensures a seamless user experience.

Encourage AI experts and domain specialists to collaborate in short development cycles, allowing for frequent feedback and adjustments. Establish regular check-ins and review sessions to assess progress, share insights, and realign objectives as needed. Use agile project management tools to document and track iterations, making improvements visible and accessible to all team members. Facilitate workshops and brainstorming sessions where both groups can exchange ideas and solve problems together. Promote a culture of continuous improvement, where lessons learned from each iteration are applied to subsequent cycles. This iterative process ensures that solutions are continuously and iteratively optimized!

Collaboration between AI experts and domain specialists is crucial to address ethical considerations in AI systems, such as biases in data. By working together, they can proactively create fair and responsible solutions.

Collaboration between AI experts and domain specialists is essential for developing a robust ethical control framework. Domain specialists identify specific ethical concerns and potential gaps, guiding AI experts in refining controls. This partnership ensures comprehensive coverage of issues, leading to a more effective and fair AI system. Continuous collaboration allows for the control framework to evolve with emerging standards and new ethical challenges. This approach builds a dynamic, adaptable control framework that enhances trust, accountability, and the overall integrity of AI technologies.

Creating ethical AI practices is key to building trust and ensuring responsible AI use. Collaboration between AI experts and domain specialists certainly helps here. If you’re not thinking about ethical and legal aspects, you definitely should be. One framework I recommend is Salesforce’s AI Ethics Maturity Model, which focuses on transparency, fairness, and accountability. Frameworks like this help align AI projects with broader societal values, boosting both their impact and credibility.

A ética em projetos de IA envolvem garantir que os sistemas de inteligência artificial sejam desenvolvidos e utilizados de maneira justa, transparente e responsável. Isso inclui abordar questões de viés, privacidade, segurança, e impactos sociais, além de garantir conformidade com regulamentações e normas éticas. É uma área crucial para promover a confiança e a equidade na implementação de tecnologias de IA.

Ethical discussions should be an integral part of collaboration. Domain specialists contribute by raising ethical concerns related to bias, fairness, privacy, and transparency. They understand the societal impact of AI solutions within their specific context. AI experts, in turn, design systems that adhere to ethical guidelines and address potential pitfalls. For instance, when deploying AI in criminal justice, collaboration between legal experts, data scientists, and policymakers ensures that predictive models do not perpetuate existing biases.

Domain specialists know what the user's want and need. Since they understand more the users, they can provide feedback on what the system should look like and operate. In addition, they are very useful in providing feedback when training as they know the language of the user more. Also resistance to new technology is common. Domain specialists can anticipate potential objections and work with AI experts to address these concerns proactively, easing the transition for users.

Ultimately, AI solutions are meant to benefit end-users. Collaboration with domain specialists helps create user-centric designs. By involving end-users early in the development process, teams gain insights into user needs, pain points, and preferences. Domain specialists can provide valuable feedback on usability, accessibility, and user acceptance. For instance, when building a personalized recommendation engine for a streaming platform, collaboration between content curators, UX designers, and AI engineers ensures that recommendations align with user preferences and enhance overall satisfaction.

Regularly involve end-users in the development process to gather feedback on AI tools and solutions. Organize user testing sessions where domain specialists can observe how users interact with AI systems and provide insights to AI experts for improvements. Establish a feedback loop where user experiences are continuously shared, facilitating iterative refinements and ensuring that user needs are addressed. Additionally, conduct workshops and training sessions to help users adopt new AI tools, ensuring they understand and effectively utilize the technology. Maintain open communication channels where users can easily report issues and suggest enhancements. By prioritizing user adoption, AI solutions become practical and user-friendly!

User adoption is crucial for successful AI implementation. Collaboration between domain specialists and AI experts eases user reservations, shapes intuitive training programs, and ensures user-friendly interfaces. By anticipating objections and addressing concerns proactively, this collaborative effort enhances user acceptance and facilitates smooth utilization of AI systems.

Look up every now and then, and you'll see that sometimes the best collaboration is between "AI" and "HI" (yeah, Human Intelligence). Not everything must be automated or processed via AI. Depending on the corporation using AI (Maturity, size, revenue, industry), there are elements of the process that are far more practical and economic being handled by the ultimate machine - a capable, competent human. Resist the urge to jam everything down the path of AI, and collaborate with the actual human element (have people do some of the most complex work) with a solid hand-off from the AI application or solution. Sometimes the collaboration expert is the solution, not their recommendation.

Reminding and Introducing them to a certain theory on focusing on core competencies in a value chain and then scaling it and for the rest collaborate, to not only make the whole complete but bigger than the sum of its parts The principle has held good for ages and it would remain sane and same for AI as well

- Rather than using off-the-shelf tools, AI experts and domain specialists should collaborate from the outset, ensuring solutions are tailored and integrated into existing workflows. - AI can reveal patterns and trends in data that humans miss. Combining AI's analytical power with domain expertise leads to deeper insights and novel discoveries. - Ongoing cross-disciplinary training keeps both AI experts and domain specialists updated on the latest advancements, maximizing their collaborative potential. - Blending AI-driven insights with human judgment leads to balanced, well-informed decisions that leverage both data and domain expertise.

It's essential to consider the ethical implications of AI collaboration, ensuring transparency and accountability. Incorporating diverse perspectives and continuously evaluating biases can lead to more inclusive and effective outcomes. Remember, successful collaboration goes beyond technical expertise; it requires a balanced integration of human judgment and AI capabilities.