The last couple of years has seen Artificial Intelligence (AI) become a house hold topic with many being curious about how far the technology can go. Consumer products like Amazon Alexa and Google Home have long used machine learning as a selling point; However, AI applications in the enterprise remain limited to narrow machine learning […]
The last couple of years has seen Artificial Intelligence (AI) become a house hold topic with many being curious about how far the technology can go. Consumer products like Amazon Alexa and Google Home have long used machine learning as a selling point; However, AI applications in the enterprise remain limited to narrow machine learning tasks. With progressive improvements in the convergence of of hardware and algorithms happening on a daily basis, AI is likely to have a larger impact on business and industry in the coming years.
A multi-sector research study conducted by Cowen and Company revealed that 81 percent of IT decision makers are already investing in, or planning to invest in AI. Furthermore, CIOs are currently integrating AI into their tech stacks with 43 percent reporting that they are in the evaluation phase, while an additional 38 percent have already implemented AI and plan to invest more. Research firm McKinsey estimates that large tech companies spent close to $30 billion on AI in 2016 alone. IDC predicts that AI will grow to become a $47 billion behemoth by 2020, with a compound annual growth rate of 55 percent. With market forecasts predicting explosive growth for the artificial intelligence market, it is quite clear that the future of the enterprise will be defined by artificial intelligence. Below are the top 10 predictions for AI in the enterprise.
According to a Forrester report, 54 percent of global information workers are interrupted a couple of times a month to spend time looking for insights, information and answers. There are now more file formats and types than ever before, and the bulk of it is unstructured data, making it hard for traditional CRM platforms to recognize. AI powered cognitive search returns more relevant results to users by analyzing search behavior, the content they read, the pages they visited, or files they downloaded – to establish the searchers intent or the context of the search query. The machine learning algorithm’s ability to self-learn improves search relevance over time and subsequently the user experience.
ML is especially suited for cyber security since hard-coding rules to detect whenever a hacker is trying to get into your system is quite challenging. However, AI is a double edged sword when in comes to data security. The more AI advances, the more its potential for attacks grow. Neural networks and deep learning techniques enable computers to identify and interpret patterns, and they can also find and exploit vulnerabilities. 2018 will likely see the rise of intelligent Ransomware or malware that learns as it spreads. As a result, data security concerns will speed up the acceptance of AI forcing companies to adopt it as a cyber security measure. A recent survey conducted by PWC indicates that 27 percent of executives say that their organization plans to invest in cyber security safe guards that use machine learning.
The general feeling over the last few years has been that data is the lifeblood of any organization. A recently concluded study by Oxford Economics and SAP revealed that 94 percent of business tech decision makers are investing in Big Data and analytics, driving more access to real-time data. Through out 2018 and beyond, data will remain a priority as companies aim to digitally transform their processes and turn insights into actions for real-time results. Additionally, machine learning will also play a huge role as companies aim to meet regulatory requirements such as the GDPR. Individuals will be empowered to demand that their personal data be legally recognized as their IP. If or when this happens, both parties will turn to AI to provide answers as to how the data should be used.
Whenever artificial intelligence and jobs are mentioned in the same breath; one side views it as the destroyer of jobs while the other sees it as the liberator of menial tasks in the workplace. A 2013 working paper from the University of Oxford suggests that half of all jobs in the US economy can be rendered obsolete by ‘computerization’. Others argue that intelligent machines will give rise to new jobs. According to a Gartner report, by 2019 more than 10% of hires in customer service will mostly be writing scripts for chatbot interactions. The same report also predicts that by 2020, 20 percent of all organizations will dedicate employees to guide and monitor neural networks. What’s certain is that AI will elevate the enterprise by completely transforming the way we work, collaborate and secure data.
A major challenge in the AI space has been finding talent. You require people on your team with the necessary ability to train AI based systems. Organizations with access to substantial R&D dollars are still trying to fill their ranks with qualified candidates who can take on ‘industry-disrupting’ projects. In 2018, as some businesses look to re-skill their existing workforce to achieve broader machine learning literacy; larger organizations may look to add data science and AI related officers in or close to the C-suite. These senior-level decision makers will be responsible for guiding how machine learning and AI can be integrated into the company’s existing strategy and products. Others will consider hiring practitioners in algorithms, math and AI techniques to offer input.
As AI based tools become more user friendly, users will no longer have to understand how to write code in order to work with them. Gartner defines a citizen data scientist as an individual who generates or creates models that utilize predictive capabilities or advanced diagnostic capabilities, but whose primary role falls outside the fields of analytics and statistics. As stated by the IDC 2018 IT industry predictions, over 75 percent of commercial enterprise applications will utilize AI in some form, by 2019. As business use cases for AI become more mainstream, the need for functional expertise across the organization will be important; to the point that the skill sets AI specialists typically lack will be required. As AI is integrated into every facet of the enterprise, citizen data scientists may end up being more important than computer scientists.
AI and blockchain are ground-breaking technological trends in their own rights; when combined, they have the potential to become even more revolutionary. Both serve to improve the capabilities of the other, while also providing opportunities for enhanced accountability and oversight. In the coming year, we can expect to see blockchain combined with AI to create a new level of deep learning that learns faster than previously imagined. The immutable nature of data stored on a blockchain could enhance the accuracy of AI predictions. Sectors such as the telecom, financial services and retail are among the key industries that are best suited for the adoption of these technologies.
AI already plays a key role in shaping consumer experience. Chatbots have become one of the most recognizable forms of AI, with 80 percent of marketing leaders citing the use of chatbots to enhance customer experience. Despite the fact that the market for enterprise AI has recorded substantial growth, they still require more complex solutions. Some consumer products have already made their way into the enterprise, a good example being voice-activated digital assistants. With Amazon’s recent announcement of Alexa for Business, we can expect employees to start relying on smart assistants to manage their calendars, make calls, schedule reminders, and run to-do lists without lifting a finger.
Now that machine learning has proven its value, as the technology matures, more businesses will turn to the cloud for Machine Learning as a Service. The adoption of MLaaS will increase starting in private clouds within large organizations and in multi-tenant public cloud environments for medium sized enterprises. This will enable a wider range of enterprises to take advantage of machine learning without heavily investing in additional hardware or training their own algorithms.
Every forward-thinking innovative organization currently has an initiative or project around digital transformation, with AI usually being the focus. AI represents a significant change in the way enterprises do business. Predictive algorithms, translators and chatbots have already become mainstream and multiple businesses across the globe are utilizing them to boost profitability by reducing costs and understanding their customers better. Expect an even higher level of personalization to become ubiquitous and enhance customer experience everywhere.
