A large amount of AI projects fail. Rackspace Technology's survey estimates the number at a whopping 34% [2]. Much of this failure is due to management not understanding the risks and intricacies of AI technologies and, vice versa, developers not knowing how to scale the technologies or the business needs. Many managers seem to think that AI projects will be just like typical software project, however, there are specific challenges involved. These challenges may relate to the skills required in the team, legal issues requiring model transparency, big data governance, cultural challenges for adoption, and many more.

In her global role based in London, UK, Hande leads the marketing of data and artificial intelligence services for HPE GreenLake Edge to Cloud Platform. Hande has 20 years of technology and marketing experience as a consultant, portfolio and marketing manager. Hande has passion for emerging technologies and their impact on organizations, societies and ethics.

It's clear that, with AI becoming embedded in all aspects of our life, companies need to do more to ensure their systems are free of bias and even find ways to use the technology to help mitigate harmful biases in order to make fairer business decisions. So how do we do that? It starts by building a diverse team, something the industry is still failing to do; according to research published by the AI Now Institute, 80% of AI professors are men, and only 15% of AI researchers at Facebook and 10% of AI researchers at Google are women. Jen Rodvold, head of digital ethics and tech for good at Sopra Steria, comments: "Diversity is key not only to driving a successful AI strategy, but essential to a business' bottom line. A diverse workforce will offer a range of different perspectives, flag any bias involved in the development process and help to interrogate wider organisational processes that could be perpetuating bias and impacting the way your technology is developed in unforeseen ways."

As artificial intelligence (AI) starts to develop--as an innovation as well as a scalable business practice, understanding why a few organizations are more effective than others at it frequently comes to realizing how to execute on goal-oriented plans. Take data strategy, for instance--a key building block of AI adoption. By far most (85%) of the organizations creating AI today state they have an AI data strategy set up, yet the greater part likewise concede they don't understand enough about data infrastructure to deliver on AI initiatives. Another study recommends why it's important to close the gap. Nearly 66% of companies viewed as top performers with AI state they have an unmistakable and steady data strategy.

My holiday reading this year was Gregory Zuckerman's The Man Who Solved the Market, which I finished in one long sitting on Christmas Eve. It tells a fascinating story of the legendary Jim Simons and his secretive hedge-fund firm, Renaissance Technologies. Without a doubt, Simons has an extremely successful career. Simons started a side project on the mathematical analysis of stock trading strategies when he worked at the Institute for Defense Analyses (IDA) as a Cold War codebreaker. After the IDA fired him for publicly speaking out against the Vietnam War, Simons joined the faculty at Stony Brook University, where he recruited top talents from across the country and built a world-class math department.

As the hype around AI continues, building and executing on an AI strategy that supports market competitiveness will be top of mind for executives. The AI pilots are complete, yet executives are still grappling with what AI means for their organisations. As the use cases develop and capabilities emerge, businesses will look to defining an AI strategy for the enterprise to maximise the benefit and impact. Core to this strategy will be the understanding of how data is accessed and integrated, as well as the plan for talent and skills development, infrastructure evolution, auditability requirements and governance requirements. These strategies will ensure organisations are building the capabilities needed to succeed with AI in the long term and transform operational business models.