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Nail it and then scale it – Leaving corporate America was one of the first lean startup books I read for entrepreneurial purposes. In 2010 I joined a small digital company called Ziff Davis, now one of the largest digital media companies in the world. But in 2010 I was looking for ways to expand. The book helped. In 7 years, we increased revenue by $ 1.2 billion and more and increased market value by $ 4 billion when I left. Nail it then Scale – Helps entrepreneurs in the process of starting a high-growth business. However, I now see that its principles were applicable, even when I was in a much larger company and now I am back in a global multinational company.
Medium and large organizations (revenues of more than $ 1 billion to $ 25 billion) are the only entities that can innovate. Growth for such organizations is an even more esoteric concept. Most C-suite managers believe that they should use predictive data and analysis to achieve their growth goals. Let’s call these projects “AI” for ease. Few organizations know how to achieve this. More than 40% of these companies spend between $ 4 million and $ 16 million a year on artificial intelligence projects, which cost between $ 16 million and $ 170 million a year. The top 1% is at the top of this range.
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However, a common issue for large companies is the inability to expand their AI initiatives across the organization. In fact, most executives would say that their organizations are struggling to increase their innovations and believe that if they don’t grow they are likely to shrink or even leave the business. I have spent my entire career building a business and a scale for growth, primarily using data and analysis as a basic foundation. If you want to learn an insight into building data science in your organization, this article can help.
If you manage to expand your artificial intelligence initiatives, you can expect to get three times your ROI plus 30% + more on your major financial measures. If it’s an incentive enough for you, keep reading. Most large companies can be classified into three vessels: The POC Guys, The Scale Guys, and The Growth Guys. let’s discuss each of them.
For most Fortune 500 companies, investing in analytics and AI projects has not been an issue. Moreover, I have not seen many examples of companies that have stopped budgeting for such initiatives, as most managers understand the value hidden in the “data”. So why do most companies fail in their analysis and AI implementations? From what I’ve seen, mostly because data and analytics aren’t done intentionally or as a priority planning in these companies, usually a lot of jargon or noise data is spread across departmental databases with groups of people creating dashboards using tools. Qlikview, Tableau, and PowerBI, which offer a marginal contribution, and, above all, analysis and AI are not done in teamwork. It is usually a remote data science team, created within the organization or often hidden under the company’s IT flood, that ultimately does these projects. Most projects are never embedded in the organization and therefore never give their true value.
The boys of scale and growth do not suffer from the previous problem.They have a clearly defined strategy where they are able to successfully deploy each POC from pilot to MVP for one to two years with a defined structure, artificial intelligence champions and governance. Their processes and ownership are identified with clear responsibility Without leadership and leadership, projects fail due to territorial disputes over data and analytical ownership.Therefore, alignment is essential.In addition, successful companies know how to reuse artificial intelligence models and code for customization and acceleration. spread and thus bring products to market quickly much more than POC Guys.
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Another thing the guys at Scale & Growth do is treat data as a team effort with multidisciplinary teams across the organization and backed by C-suites, led by the Director General of Data. We do this at LafargeHolcim, where businesses, IT and digital teams work together to increase the number of pilots in the POD for MVP and for more production. We have PODs made up of data engineers and artificial intelligence, business experts, data managers, computer analysts, display and user interface experts, AI / ML engineers, data scientists and product experts, and a well-defined program management team. There is not a single crossover of artificial intelligence in our organization, and we have teams working together to drive our global artificial intelligence efforts.
In fact, as we embarked on our artificial intelligence journey, we became aware of the importance of officially training our staff to teach how AI is applied to their roles, facilitating and improving their work, and enabling a more rounded combination of skills to increase productivity and job satisfaction. We emphasize that our analytical efforts are implemented throughout our organization and are not fenced in specific projects. In this way, our global work team has the opportunity to work with its colleagues in artificial intelligence and can internalize benefits for their teams and the entire organization. We also focus on the ROI part of the investment so that we can prove the ROI in advance to justify the investment without necessarily correcting it. This is because IA has stated that it is strategically essential for the growth of the company. We are developing a platform for AI acceleration and scaling to dismantle silane and democratize analysis and perspectives across our global workforce. For us at LafargeHolcim, artificial intelligence is not just about speed, it’s about going to the future, it’s not just about making money, it’s about deliberately investing in projects that create a change of scale, and that’s not just about collecting data. but making sense. Data to create unprecedented perspectives and build a future of building materials companies. Ultimately, we are committed to bringing 2 billion people to Earth in the future through the development of sustainable infrastructure and creative building solutions using carbon-friendly concrete, the most widely used material on Earth after water. Allianz Global Corporate & Specialty’s Safety and Shipping Review () is an annual review of shipping losses, risk challenges and security trends and evolution.
The international shipbuilding industry is responsible for transporting about 90% of the world’s trade, so ship safety is key. In the early 1990s, the world fleet lost more than 200 vessels a year. It has dropped to around 50-75 per year in the last four years –
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