What do you do if your product innovation is lacking artificial intelligence and machine learning?

🌐 #DataStrategies: The challenge of insufficient data in AI and ML initiatives is real, but it’s far from insurmountable. Embracing synthetic data is a game-changer. By using methods like GANs or data anonymization, you can create a robust set that mirrors real-world scenarios, allowing your models to learn & improve without compromising on quality or privacy. The utilization of open data is another savvy move. It’s an incredible source of information at your fingertips. Do you have data partnerships? They can be a goldmine, providing access to diverse datasets that you might not have been able to gather on your own. In my journey, leveraging these approaches not only filled data gaps but also sparked innovation in ways I hadn’t imagined.

First of all, you need to incorporate into your product development team members who have AI and ML as their full expertise. Next, you need to plan how this team will collect data in order to incorporate this knowledge (AI and ML) into your product design. Finally, this team has to understand how AI and ML can contribute to improving the functionality of the new products being developed and how they will be trained to perform as desired, so as not to fail and not provide opportunities for future users/customers of these products to be dissatisfied.

Really this applies today to only a limited amount of product dev. I can assure you that 99.9% of product dev in FMCG do not use AI, nor fundamentally needs it. even though there will be some efficiency in the future. So the title should be restricted to the top of product in question... Synthetic market research data will be of enormous help in the near future .

Hoje existe um buzz muito grande em torno de IA e Machine Learning mas a verdade é que tem muitos produtos digitais que não dependem dessa tecnologia para ter sucesso. O principal ponto é atender de uma forma única seu cliente no que quer que você se proponha a desenvolver. Mais importante que a tecnologia nos dias de hoje é como você cria uma conexão com seus consumidores. A cada dia mais esse se mostra um grande diferencial competitivo.

Investigate the feasibility of transfer learning, where pre-trained models or knowledge from related domains are adapted and fine-tuned for the specific problem/situation at hand. This method can reduce the need for large amounts of domain-specific data by leveraging knowledge learned from similar tasks or domains. Focus on improving the quality of the existing data through data cleaning, preprocessing, and feature engineering techniques. By making sure that the data is representative, accurate, and relevant to the problem domain, the effectiveness of AI and ML models can be enhanced even with limited data.

Not all products need AI/ML to succeed. Focus on value first, then approach. Explore AI/ML if it can significantly improve user experience, optimize operations, or unlock new revenue. For product innovation skills gap, consider Build-or-Partner, while balancing immediate opportunities with long-term value creation. Build: Invest in talent and infrastructure for long-term control and strategic value. Start with low-code/no-code for faster AI/ML adoption with reduced costs and errors. Partner: Accelerate time-to-market with existing expertise, but choose partners aligned with your goals.

🚀 #SkillBuilding: Tackling the skills gap in AI and ML doesn’t have to be daunting. Low-code or no-code platforms give you an ability to build and deploy AI models. They allow teams to experiment and innovate without deep expertise. There's also online learning – use platforms like Coursera or Udemy resources to upgrade your team’s skills. They range from beginner to advanced levels, making them ideal for continuous learning. And don’t underestimate the value of external consultants. They can provide not just expertise but also fresh perspectives that can accelerate your AI journey. I’ve seen these strategies transform teams, turning skill gaps into centers of learning and growth. What’s been your experience in upskilling for AI and ML?

There is so much hype about this Tech and yes the skill set is lacking - every industry will soon pickup and adopt. although very important considerations related to AI responsibility, ethics, bias are thought through and AI is regulated- that will thankfully slow down the pace. Let’s take that time to educate the workforce

Today, access to AI and ML training is more accessible, Google, Microsoft and IBM are constantly launching new courses on the topic, to increase the capacity and knowledge of people who will apply AI with their products. I myself have already completed the IBM AI Basics course through IDX at MIT, I have just started Google Cloud IA, all with free access. For application in my marketing world, it has helped a lot to think about solutions that are already bringing me results efficiently and especially with security in data management.

When we don't have enough skills, experts in this area to be consulted. If feasible and economically, should partner or engage with companies like IBM to leverage their industry leadership thought and expertise. Other options is to engage an SME/Consultant for a short period who can fill the skill gap. In the long run, we need to ensure that the skill gap is addressed by enabling and improving skills. Major companies like IBM, Microsoft, etc., offer numerous courses in this arena that can be leveraged to enhance skills.

🔍 #CustomerInsights: In the AI-powered product innovation process, customer feedback is your key. Without it, you're flying blind. Online surveys and polls are straightforward tools to gauge satisfaction and preferences, and are a structured way to collect valuable insights. User testing and interviews offer a more hands-on approach, allowing you to see your product in action and get direct feedback. Social media and online reviews are the modern-day focus groups. They offer unfiltered opinions and sentiments, providing a real-time pulse on customer trends. In my experience, these methods helped in fine-tuning products, and in building stronger customer relationships.

A usable product/feature is always much more appreciated than an extremely fancy product/feature. This statement is in the context if our customer of the product/feature will really benifit by AI/ML - fying the features or is it just another fancy/shiny update? Most of the time the users don't care how a certain feature is functioning, whether it is powered by AI or not. So the best way to.figure out the efficacy/performance uplifts of the proposed/developed AI/ML features is by doing A/B testing the feature against a subset of users, check on the metrics before productionising it

#customerfeedback is critical. While online surveys or polls help in gathering customer feedback, we need to ensure that we possess the expertise to effectively utilize them. If not, we should consider engaging or partnering with someone who has mastered the art of gathering feedback. Regardless of the strategy chosen, it is essential to ensure that it is economical and meets your objectives quickly. In gist, Speed.Quality.CustomerVoice are critical.

then go get it with actual and properly designed by formally traine research specialists from reputable institutes. Or fail on your product launch .

If your only tool is a hammer then every problem looks like a nail! Using either AI or ML or Generative AI is not the only way to solve problems. First it is important to analyze the solutions to your problems before jumping into it and looking frenetically at data (and data collection, labeling…), AI models… Thinking, analyzing, benchmarking, mathematical modeling, (standard) operational research or statistics are the methods which must be thoroughly considered to avoid wasting your time on the wrong. Anyway, after careful consideration, if there is no solution with these methods, start blending them together with AI/ML. Using AI in stand alone should be considered the third and last steps, when the 2 first steps have failed.

🤝 #BeyondTheBasics: Beyond data, skills, and feedback, there are other crucial factors in AI-driven innovation. Ethical AI is a growing concern – ensuring that your AI solutions are fair, transparent, and responsible is essential for long-term trust and credibility. Another aspect is the integration of AI and ML into your existing systems and workflows. Seamless integration is key to maximizing efficiency and effectiveness. Also, don’t forget about scalability. Your solutions should not only solve current problems but also be adaptable for future challenges. AI and ML are not silver bullets; they should complement human creativity and intuition, not replace them.

First question is if AI will add value to my product. Assuming it’s an innovative one, quite likely but not necessarily. Let’s not add features because they are buzz words but because they solve real problems. Once you answered this question, you are good to proceed.

In the era of AI (Artificial Intelligence) one should not underestimate the power of NI (Natural Intelligence). In innovation space, we are always going to face of situations with lack of data because we may be the first ones to do something. Learning from other industries and evolving workable solutions through rapid experimenting should also be kept on the radar.

Let's refrain from hastily assuming that AI or Gen AI will solve our problems or must be used. Instead, master the 'problem domain' space, break down the problem into minute levels, and carefully consider what solution is required—whether it's simplification, lean methodologies, automation, AI, Gen AI, etc. Choose the approach that is most suitable for your context; that is what matters the most.