Over the years, I have written blogs on user experience, customer experience, innovation, design thinking, agile development, data science, predictive analytics, AI, personalization, brand, business strategy, product management, emotional connection, design, art, culture, leadership life… and other topics. – Enjoy.
Generative artificial intelligence (Gen AI) “tools” – like OpenAI ChatGPT, Google Gemini, or Backplain – have made it easy to write and polish emails or presentations, summarize reports, write code, create social media campaigns, and accelerate customer service interactions. To use these to their fullest potential comes down to the prompt — the questions and statements —
The Lean institute say 95% of process activities associated with manufacturing ultimately add no value to the customer and HCL Technologies say 70% of the engineering changes which delay production are due to brands trying to retrospectively correct issues with design to deal with assembly, cost & quality problems. Design for excellence (DfX) is a
Design for excellence (DfX) is a systematic and proactive approach to design that uses the knowledge and experience of experts in other parts of the product development process (test, manufacturing, operations, support, etc.) to ensure the final product can be realized at reasonably low life cycle cost while still meeting reliability, quality, durability, and customer expectation targets. DfX
According to a report by Fortune Business Insights, the global loyalty management market may expand significantly, increasing from $6.47 billion in 2023 to $28.65 billion by 2030. This represents a compound annual growth rate (CAGR) of 23.7% over the forecast period, which signifies that companies must ensure a better customer experience for their clients. “Customer
According to Ivanka Visnjic and Ronnie Leten, authors of How to collaborate well and scale up fast (Havard Business Review. September–October 2024), “Large firms aren’t designed for moon shots. Their owners don’t like the risks and won’t kill the goose that lays the golden egg. As a result, all too often they end up defaulting to incremental
Artificial Intelligence (AI) can enhance a wide range of activities and processes by improving efficiency, accuracy, and decision-making. Here’s a breakdown of how AI can contribute across various domains: Sustainability – Energy: AI can optimize energy consumption in homes, businesses, and cities by analyzing usage patterns and automating energy-efficient adjustments. – Agriculture: AI can monitor crop health, forecast weather
Artificial Intelligence (AI) plays a significant role in driving and optimizing the circular economy by enhancing sustainability and resource efficiency. The circular economy is a model focused on minimizing waste and making the most of resources by promoting reuse, recycling, and regeneration of products and materials.Here are several key areas where AI contributes to the circular economy:
An AI innovation strategy is a comprehensive approach to adopt, develop, and implement artificial intelligence (AI) technologies that drives growth, enhances competitiveness, and creates societal value. A good innovation strategy defines goals, key focus areas, risks, and how to foster the right ecosystem for success: Vision and Objectives Vision: Define the overarching purpose for adopting AI.
Hyperautomation is the expansion of automation by integrating advanced technologies like artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other advanced tools to automate as many business processes as possible. Unlike traditional automation, which focuses on automating repetitive tasks, hyperautomation involves creating systems that can adapt, learn, and improve as they operate. Key
Robotic Process Automation (RPA) is a technology that uses software robots or “bots” to automate repetitive, rule-based tasks in financial institutions. When combined with AI, it becomes more intelligent, allowing organizations to handle complex processes that require decision-making, learning, and adapting. RPA significantly improves operational efficiency, reduces manual errors, and allows employees to focus on
Artificial Intelligence (AI) can play a significant role in carbon utilization by optimizing processes, improving efficiency, and enabling new approaches to capture, convert, and reuse carbon dioxide (CO₂). Here are a few ways AI can be leveraged for carbon utilization: Carbon Capture Optimization AI can optimize the process of capturing CO₂ from industrial emissions or
A typical startup team needs to be quick and adaptable to gain an audience and a market share BUT they can’t make this approach work without a clear strategic vision and a well-defined strategic plan. Otherwise, efforts can quickly turn into chaos, which can be extremely costly. Here are six steps to take to build a strong strategy: