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'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
How could a ‘conventional’ company transform itself into an AI-powered organization?
Artificial Intelligence can help a business adopt a more data-driven approach to decision-making. By harnessing the power of big data and analytics, businesses can gain a better understanding of customer behaviours and preferences, as well as market trends. This, in turn, can help them make more informed strategic decisions about where to allocate their resources and how to best serve their customers. AI can also help a company automate many tasks and processes, thereby freeing up employees’ time for more value-added activities. This would not only improve efficiency and reduce costs, but it would also enable employees to focus on more strategic tasks that require human interaction and judgement.
So, how can a company go about making the transition towards an AI-powered organization? There is no one-size-fits-all answer to this question, as the nature of this transition varies depending on the specific needs and circumstances of each business. However, there are a few key steps that all companies can take to get started:
- First, it’s important to assess your current business environment and identify areas where AI could be most beneficial.
- Second, once you have a clear idea of the opportunities that AI brings, you can develop a plan for how to integrate it into your operations.
- Third, the business would need to create a data-driven infrastructure, possibly on top of a data lake, a data warehouse, and so on.
- Fourth, the business would need to invest in AI technologies, and find ways to use these tools to improve its operations.
- Fifth, the business should set up a dedicated team to manage and oversee AI initiatives, or develop partnerships with external service providers.
- Sixth, the business would need to adopt a data-driven culture. This means that all employees would need to be data-literate and understand how to use data to make better decisions.
- Finally, it’s important to remember that a successful AI transformation requires a commitment from all levels of the organization. Employees across the hierarchy need to be on board with the new strategy and be willing to work together to make it a success.
If a company can successfully make these changes, it will be able to harness the power of big data and become a data-driven, AI-powered organization. But how could a company find the right talent and form a powerful Data & AI team?
One option is to hire external service providers who have the necessary skills and experience. This can be a cost-effective way to get up and running quickly, as many providers offer a range of services that cover everything from data management to AI development. Another option is to develop an in-house team of AI experts. This can be a more expensive option, but it gives businesses more control over the development and implementation of AI initiatives. A company could also choose a hybrid approach, which involves hiring external service providers for specific tasks while developing an in-house team for other tasks.
No matter which approaches a company chooses, it’s important to make sure that the team has the necessary skills and experience to successfully implement AI initiatives. This includes skills spanning Data Science, Machine Learning, and Artificial Intelligence. When forming a team it is important to ensure that there is a diversity of backgrounds and skillsets, so that a variety of viewpoints can be considered when making decisions about AI.
The time needed to set up a Data and AI function varies depending on the size and complexity of the company. However, a good rule of thumb is that it will take a minimum of 12 months to get a functioning team in place. This includes hiring and training the right people and getting them up to speed on the company’s data infrastructure and AI technologies. It’s important to note that a Data and AI function will require ongoing development and support, so it’s important to set aside adequate resources for this.
AI has the potential to become a source of differentiation for modern products and services. This is because big tech companies are not well-positioned to provide custom AI services to businesses - they are more focused on developing general-purpose AI technologies that can be used by a wide range of businesses. However, businesses need specialised AI services that are suitable for their specific domain and this is where a company’s in-house team of AI experts can come in handy, as they can develop custom AI solutions, tailored to the needs of the business.
The benefits of a data-driven, AI-powered organization are clear: increased efficiency, better decision making, and a competitive edge in the marketplace. However, making these changes is not easy, and it requires a commitment from all levels of the organization. A successful AI transformation is a continuous journey. This means that a company needs to invest in AI and get prepared over the long term, and not just in the early stages of the transformation.