Most white papers on Data Science topics – whether that’s Machine Learning (ML) or Artificial Intelligence (AI), or indeed others (RPA, NLP) – either focus on the very high-level, conceptual challenges facing organisations with this new technology, or the detailed specifics of the technology or techniques involved, often including why this technology or platform is better than that one.
There is a place for both of these, of course, but there is currently little for those caught in the middle – those who are actually trying to implement any practical changes “on the ground”, having to interpret top-down business processes that may or may not have any relation to how the tools or teams work, whilst also delivering on day-to-day business objectives.
And yet, this group arguably has a better understanding of what future business will look like, because right now they are battling some of the critical challenges that will face organisations using Artificial Intelligence in 2-3 years.
At the same time, much of the language associated with AI/ML/RPA/NL etc. appears to be targeted at very large organisations, or at least those with existing well-established data operations. This group is clearly important and are likely to be the main leaders in the Data Science world over the next few years. However, one important thing (of the many) that Covid has changed in the last two years is how businesses use technology to improve business resilience; many businesses for whom Machine Learning might have been an irrelevance in early 2020 are now reconsidering their roadmap and working out how AI can help them. But they are just starting out; high-falutin management consulting speak doesn’t help this emerging group of companies, who don’t have advanced Data practices and skills already.
So, this white paper focuses on some of the practical ways in which AI will change businesses, and what sort of different operational challenges companies, of all types, will have to address.
Let’s take a look at Chapter 1:
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