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g-f(2)817 The New World (1/14/2022), HBR Analytical Services, Keys to Successful Innovation through Artificial Intelligence

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageThe New World, Successful Innovation through Artificial Intelligence (12/14/2021)  g-f(2)426 

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Keys to Successful Innovation through Artificial Intelligence 

      • While the technology is still in its infancy in some industries, other sectors are testing the waters, and still others are already reaping the benefits from their AI/ML transformation and charting a path for their industries.
      • More and more companies are using artificial intelligence (AI) and machine learning (ML) for everything from boosting productivity to enhancing customer experiences and satisfaction to making better decisions faster. These technologies are also used to generate new revenue opportunities and improve operational efficiencies.
      • Implementing AI/ML successfully requires more than just great technology and mountains of data. It also requires rethinking silos and fragmented legacy systems, adding capabilities, and retooling the company culture.
      • While the details of implementing a successful strategy for AI/ML vary depending on each business and the specific project, the steps to get there are universal.

                Genioux knowledge fact condensed as an image

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                Golden Knowledge (GK) Juice

                • Artificial intelligence (AI) and machine learning (ML) are playing an increasingly outsized role in strengthening and transforming industries around the world. The global AI market value is expected to reach $267 billion by 2027, and the technology is expected to contribute $15.7 trillion to the global economy by 2030.
                • As the world economy moves from a time when every industry operates from a different set of core competencies to one shaped by data and analytics, everyone from C-suite executives to IT teams must understand how to use AI/ ML strategically and efficiently to achieve their organizations’ transformational and strategic goals.
                • But the road to a successful AI/ML implementation is not always a straight or smooth one. The journey may come with detours and require constant reiteration and reevaluation to stay on track and produce the intended outcome. Defining business goals is the first step to figuring out what sort of strategy is needed. “In every large corporation, you have somebody screaming, ‘You need to do more AI!’ The question from smart people is, ‘For what reason?,’ says Mark Maenner, head of data transformation for the BMW Group.
                • Implementing AI/ML successfully requires more than just great technology and mountains of data. It also requires rethinking silos and fragmented legacy systems, adding capabilities, and retooling the company culture, Marco Iansiti and Karim R. Lakhani, Harvard Business School professors and coauthors of Competing in the Age of AI, wrote in a 2020 Harvard Business Review article of the same name.
                • An AI/ML transformation starts with a well-conceived strategy to address specific business problems, then moves to getting buy-in from key stakeholders and democratizing the data so that everybody has access and can benefit from its use. From there, companies can implement AI to address the business problem initially identified.

                Condensed knowledge

                Lessons learned, HBR Analytical Services

                Golden Knowledge (GK) Juice

                • Conclusion
                  • Maenner believes the transformational aspect of these technologies comes down to finding the right balance between the revolution and the evolution. “You really need to push people on one side and ask them to take ownership of their responsibilities on the other side,” he says. “They have to understand that if they are investing [in] AI/ML, it will also harvest success in terms of budgets or improved processes.” 
                  • While there are many challenges in adopting AI/ML or finding the right strategy to employ these technologies, enterprise success stories like those of ADP and the BMW Group show that any hurdles are clearly outweighed by the benefits. Further, adopting AI is no longer optional for businesses that want to remain competitive, and it is even more critical for those that want to stand out as industry leaders. 
                  • In essence, it’s a tug-of-war to see which prevails—AI or the status quo. Close observers such as DataPrime’s Borne are betting on AI. “Every company needs to have a competitive advantage and will keep pushing against the headwinds to succeed,” he says.
                • Successfully Innovating with AI/ML 
                  • Long before the pandemic, companies around the globe were realizing the transformative effect of these essential, strategic technologies. A Harris poll found that 55% of companies reported that they accelerated their AI strategy in 2020 due to Covid-19, and 67% expect to further accelerate their AI strategy in 2021.
                  • The BMW Group is one such company that leverages technology effectively to increase innovation. It employs AI/ML for everything from product development to forecasting demands for its goods and services, explain the BMW Group’s Viehhauser and Maenner. The company developed a proprietary translation solution to help its multilingual workforce better communicate. And it processes energy-relevant data at all its locations to establish energy consumption patterns and heat and cool its buildings more efficiently. “BMW Intelligent Personal Assistant,” a voiceactivated virtual assistant, provides information about the vehicle, plays entertainment, and helps customers park and drive more safely. In addition, the company has a new operating system that offers an all-encompassing, intelligent, multi-sensory experience tailored to the user at hand. The company is able to fulfill its overall mission to make better products and enhance the customer experience through the effective use of technology.
                • Keys to a Successful AI Implementation
                  • Define your why. Adopting AI/ML is never the goal; AI exists to support the overall business objective developed by considering the largerpicture goal. As AI evangelist Andrew Ng asserts in his AI Transformation Playbook, strategy is key to harnessing the power of AI.
                  • Don’t try to boil the ocean. There is always the temptation to tackle everything on a company’s AI/ML wish list at once, but business leaders should start small by breaking down the ultimate goal into its component parts. Identify specific use cases in which AI/ ML could solve business problems or add value to existing products and services.
                  • Move from control to consensus. Admiral Grace Hopper, the founder of modern computing, was quoted in a 1976 Computerworld article as saying the most dangerous phrase in the language is, “We’ve always done it this way.” A successful implementation needs executive buy-in to get everyone ready for change and on board with a new plan.
                  • Break down the silos. Large companies have traditionally separated divisions such as marketing, operations, and HR into different entities that operate like church and state. But today’s most efficient organizations eliminate silos and integrate data analytics across divisions to power as many processes as possible. This development represents a big but necessary shift.
                  • Recognize a failing project. There’s a common denominator among companies that fail in their efforts to adopt AI: They lack the vision, discipline, talent, alignment, right information, and right use case.
                  • Find the right partner. 
                    • The chances of a successful strategy fueled by AI/ML greatly improve, says Borne, if business leaders remember that data is a science and its implementation requires an interactive process of testing, validating, and refining the hypothesis, then testing and refining the next iteration.
                    • An experienced AI vendor can help organizations succeed during this phase, says Maenner. 
                  • Use data responsibly and ethically. As AI becomes more prevalent in daily life, there are increased concerns about whether that data is used responsibly, which means everything from enhancing data privacy to reducing bias in the modeling.

                Some relevant characteristics of this "genioux fact"

                • Category 2: The Big Picture of the Digital Age
                • [genioux fact deduced or extracted from HBR Analytic Services]
                • This is a “genioux fact fast solution.”
                • Tag Opportunities those travelling at high speed on GKPath
                • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
                • Type of validity of the "genioux fact". 

                  • Inherited from sources + Supported by the knowledge of one or more experts.


                “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)817, Fernando Machuca, January 14, 2022, Corporation.

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