Rapid technological advancements are transforming businesses every day, with Artificial Intelligence and machine learning becoming a growing trend in the business world.
And while most companies believe that AI will benefit their organisations and help maintain a competitive advantage, some are still uncertain whether the return on investment is all worth it.
The AI market worth around USD 1.5 billion last year, and predicted to skyrocket to nearly USD 60 billion in the coming decade, is currently dominated by Big Data and image recognition. Machine learning, on the other hand, worth over USD 300 million, brings in the largest revenue.
Investment in Artificial Intelligence is expected to reach a 300% growth by the end of the year, 75% of developer teams to use AI technology in one or more business applications or services, 20% of all business content to be authored by machines, and one third of the fastest-growing companies expected to employ more smart machines than people by the end of 2018.
Going further, by 2020, 30% of all B2B companies are predicted to employ AI in their sales processes and over 90% of all customer interactions to be driven by AI by the year 2025.
The early adopters of AI and machine learning – high tech and telecommunications, automotive industry, financial services and media corporations – are followed closely by healthcare, retail and manufacturing sectors.
Telecommunication networks and traffic routing automation, assisted driving and cognitive systems imitating human behaviour, financial and investment machine analysis, machine media planning and consumer targeting, automated diagnosis and robot assisted surgeries, virtual chatbots, biometric and personalised shopping services, smart collaborative industrial robots on assembly lines, quality control through computer vision, AI finds implementation and experiences a rapid growth in nearly all end-use industries.
Machines are becoming smarter, but we still live in a world of applied AI where smart machines can carry out a specific specialized task and only upon deep learning and training on large amounts of data, use algorithms to simulate what would happen given every combination of input values and calculate the most effective output based on the available data. With other words, AI’s level of intelligence depends on the amount of data it processes.
The role of data in applied AI is far more significant than it is in Big Data and analytics applications. To generate business value, AI applications have to be trained on large amounts of company-specific data, and as access to data is the major factor for the effective adoption of AI technology, the data management and technology high investment costs along with data privacy are the biggest challenges identified by early adopters. Followed by the burden of significant organisational changes to make smart machines and people work effectively together, on top of unsettled legislation when it comes to decisions taken by machines.
Business adoption of AI is at a very early stage, and with a large gap between early adopters and laggards. Based on the potential business and financial added-value, each organisation has to identify its individual needs to add AI capabilities to its existing processes and services and clearly spell out which parts to be better run entirely by humans and which semi- or fully-automated and left on machines’ judgement.
We, at EVS Translations, for example, started by automating all our corporate processes and workflows into one state-of-the-art data management system, to continue ahead with investments in neural machine translation solutions.
