State of AI Adoption in 2020: How Will the Landscape Change?

AI has been one of the biggest buzzwords in the technology industry over the past few years, given its immense potential to transform our world. With more tasks being performed with AI, the enterprise adoption of this nascent technology is rapidly evolving. From business planning and forecasting to predictive maintenance and customer service, AI is now an intrinsic part of an enterprise ecosystem. The potential of AI is limitless, but certain barriers are holding traditional large enterprises back from embracing AI in a big way. These include factors such as the absence of a clear strategy, lack of data, skills shortage, and functional silos within the organization. While a few companies have mapped out potential AI opportunities across their infrastructures, only a handful have a clear strategy in place for sourcing the data that enables AI work. According to IBM, in the business world, the rate of adoption of artificial intelligence has lagged behind the level of interest through 2019. Even though people hear that most business leaders believe AI provides a competitive advantage, up until recently, some industry watchers have pegged enterprise adoption at less than 20%. According to a recent global survey, ‘From Roadblock to Scale: The Global Sprint Towards AI,’ commissioned by IBM that polled more than 4,500 technology decision-makers across the US, Europe, and China, here are the significant insights for AI adoption in 2020. Results from the Roadblocks to Scale survey indicate that while there is still work to be done, advances in data discovery and management, skills training and AI explainability are driving the rate of AI adoption faster than many predicted. For example, 45% of the respondents from large companies (1,000+ employees) said they have adopted AI, while 29% of small and medium-sized businesses (under 1,000 employees) responding said they have adopted the technology. These numbers are significantly higher than some industry watchers have estimated to date. Skills Major roadblocks are still holding companies back from the benefits of AI. Amongst respondents, 37% cite limited AI expertise or knowledge as a hindrance from successful AI adoption at their business, with increasing data complexities and siloed data (31%) and lack of tools for developing AI models (26%) following close behind. Trust Trust is part of the bedrock of AI’s deployment. Globally, 78% of respondents across all countries surveyed say it is very or critically important that they can trust that their AI’s output is fair, safe, and reliable. Moreover, being able to explain how AI arrived at a decision is universally important (83% of global respondents). Data Companies currently deploying AI technologies are more likely to use a hybrid cloud (38% adopted) or hybrid multi-cloud (17% adopted), as AI success is fed by data. And, data is everywhere, on all clouds. Rob Thomas, General Manager, IBM Data and AI says, “based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proofs-of-concept for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents.” 2019 was a productive year for AI, but 2020 is shaping up to bring an exciting new level of commitment and with it, exciting new outcomes for all involved.  

Predictions for AI Adoption in 2020

2020 promises to be even more exciting, with AI systems continuing to mature and companies extending usage and applications to address highly specialized needs. In the year ahead, organizations will be empowered to allocate resources even more wisely while achieving greater efficiency. Here are the top predictions for AI Adoption in 2020. There has been some cross-industry concern that as AI continues to improve, its resulting applications will take over human jobs and displace workers. Certainly, AI is being leveraged in new and interesting ways, but rather than replace the human workforce with machines, AI-based technologies instead will become humans’ assistant. Moreover, transfer learning, in which machine learning algorithms improve based on exposure to other algorithms, will become a more widely used technique in 2020. To date, it has been leveraged primarily with image processing, but we will see transfer learning applied to areas like text mining continue to improve. For a long time, AI has suffered from a lack of transparency. With machines developing more self-learning capabilities, developers might not know exactly why a machine learning system arrived at certain conclusions. When processes are hidden, behaviors can give pause to users who wonder if they should trust data generated by such a system. To combat this problem, more interpretable models are coming to the forefront. In 2020, the differences between data explainability, traceability and determinism will become realized in AI. What is needed at which circumstances will also be clarified? As computing elements make complex predictions more understandable, solutions can be created that help explain those predictions. By removing the mystery of the black box, organizations can refine or expand queries to deliver more valuable information. Traditionally, machine learning models have not been straightforward to deploy for data scientists and engineers. Furthermore, this will change in the coming year as AI is delivered more like a service. AI models will be executed in cheaper, easier ways in the cloud. This is a significant development on multiple fronts. By shifting to server-less deployment in the cloud, a machine learning model does not consume the same amount of computing resources as on a server. This results in a much different level of efficiency. This in and of itself will make AI as a service more popular. Moving AI to the cloud also positively influences the delivery model. Instead of coming in the form of a very heavy solution, an API can be created and shared.