Fractal Analytics is one of the largest AI providers in the world, with clients like Visa, Colgate Palmolive, Kimberly-Clark, and more. Some of their top AI experts have compiled a list of predictions for 2019 – see those below! They talk about everything from AI in healthcare to Facebook to voice to Nvidia to 5G. 

Andy Walters - Strategic Advisor & Board Member 

• The AI/Analytics arms race will continue as businesses need to get lean, agile and focused on growth. Leaders that have leveraged AI capabilities across targeted business processes will expand to enterprise-wide value driving opportunities. The "Intelligent Enterprise" will beat competition on the top and bottom line.
• Healthcare companies will be under more pressure when it comes to margin, new drug launches and regulations. They will leverage disruptive AI technology to drive break-throughs for researchers, doctors and patients. Pharmaceuticals will accelerate strategic investments in start-ups and analytics capabilities.
• Enterprises and start-ups alike will fully leverage cloud-based technology and look to AI Chip makers like Nvidia to partner with cloud and software providers to build scaled AI engines that spur innovative business models.  Cloud based AI/Analytics will provide the next "Moore's Law" breakthrough for the domain.

Doug Hillary – Strategic Advisor & Board Member 

• Enterprises will increase the use of Natural Language Processing (NLP) and voice integration with back-end data, analytics and legacy CRM/ERP systems to create more personalized and enhanced customer service for consumers and employees.
• The next wave of “immersive intelligence” will make lives easier as consumers. Augmented Reality will merge with machine learning, virtual assistants and wearable devices to make it easier to connect, shop, play and perform tasks around the home, as well as to manage wellness with health care providers.
• Investments in Robotic Process Automation (RPA) will rapidly accelerate to drive productivity and efficiency gains across a broader range of back office operations and more complex end-to-end automation challenges (e.g., PO to order processing).
• The launch of 5G in 2018, and the rollout of 5G-enabled "edge" devices such as smartphones, hotspots, gateways and IoT devices in 2019, will accelerate the ability to perform analytics in a hybrid or distributed architecture; enabling a new breed of commercial and consumer use cases for AI.
• Slowing macroeconomic growth and scarcity of talent, along with an increased focus to reap the benefits of digital transformation in hyper-competitive markets, will force CIO’s to do less in-house, and to rely instead on an ecosystem of partners.
• Executives and HR professionals will need to get serious about addressing the challenge of retraining, or up-skilling existing workforces, to accommodate, work and thrive with “digital labor.

Jeff Shacket – Principal Consultant

• Healthcare organizations will continue to pursue piecemeal efforts in AI – that is, experiment in pockets – rather than develop comprehensive strategies to advance their analytics capabilities. 

Vishal Agarwal – Principal Consultant

• AI driven assistants will become more cognitive in 2019, executing more complex tasks than before.

Bhaskar Roy – Client Partner and Head, Customer Analytics

• Shift from Cloud processing to Edge - As more real-time decisions are needed, decisioning time will need to decrease to enable smarter actions by connected machines, IoT, etc. This will lead to a shift of data processing, and computing power towards the edge, to enable more dynamic real-world applications.
• Data security and privacy – As more countries look at localizing data storage, and defining limits/ boundaries of data usage, the ability to drive personalized insights will be challenged. This should ideally lead to organizations looking at innovative ways to drive customer engagement.
• Voice commerce to gain importance – As more and more services/websites integrate with voice capabilities like Siri, Alexa, Google Assistant, etc., organizations will need to redefine their SEO strategies to drive similar/ higher conversions. This, combined with the commoditisation of voice-bot/chat-bot capabilities, should lead to newer customer experience initiatives.

David Yeo, Client Partner

• Leading companies will expand adoption of speechbots, which will perform a greater share of the lower-value added tasks.
• Image analytics will increase in sophistication, and support claims adjusters in more accurately estimating damages to property and auto.
• AI will begin to be incorporated into metrics dashboards at leading companies to offer Natural Language Processing (NLP) interfaces and predictive insights.

Sankar Narayanan – Chief Practice Officer, BFSI, TMT, Healthcare and Engineering

• Enhancing experiences and services for both advisors and clients (institutional investors, HNIs, etc.) will be key focus areas for Artificial Intelligence initiatives of asset management companies in the coming year. Simplified investment solutions and on-demand advice (often through online/mobile channels), will also advance in the coming year. 
• 2019 will likely see a serious move by one or more of the technology giants, such as GAFA, to disrupt the multi-trillion-dollar investment industry -- e.g. providing asset management services, intensifying competition, as well as creating pressure amongst incumbents to improve operational efficiency. Continuing differentiators will include enormous data, top data science talent, and ability to operate at high efficiency.
• The war for AI and engineering talent: shifting fee structures towards an 'alpha' generation. It is predicted that at least 4-5 leading institutions will link fees with performance. This will lead to fervent activity towards acquiring alternative data sources, AI and data engineering talent and further partnerships with academia and FinTechs.
• AI should progress towards large scale implementations -- incorporating engineering such as cloud migration, reduced data latency and design -- especially to simplify and personalize solutions.
• The influence of behavioral sciences will go beyond goal-based strategies. A widespread adoption of behavioral sciences will incorporate facets of cognitive bias, human emotions and game theory into investment and advisory processes.

Lana Klein – Managing Partner, Growth Analytics & AI Transformation (GAIT)

• Managing the AI Wild West – The growth of AI will spur a wave of legal, ethical and legislative action to manage how humanity deals with big data and technology.  AI and big data are creating many new moral and legal dilemmas for the human kind. Technology grew much faster than human ability to deal with legal, ethical and psychological implications, creating AI “wild west.”  In the coming years we will see action related to figuring out how to address these issues. How do we feel about someone possibly being privy to our every move, need and thought – even if they mean no harm? How much of our privacy are we willing to sacrifice for more convenience and safety? How do we feel about possibility of expanding one’s intellect by fusing AI chips in with our brains?  These and many other questions will need to be addressed. New laws will be passed, new ethics will be formed and new psychological boundaries will emerge
• Emergence of AI Personal Security Agents - AI engines that will play a role similar to antivirus software, helping us mange personal data exposure, protecting privacy, and shielding us from data misuses. Think about this: Google knows your most secret thoughts and fears, Faceboook knows your entire family and social circle, who your secret crush is, what political views you publicly express, vs. what you think, places you have travelled to and liked. Mint knows more about your finances and spending habits more than even you do, and 23&me knows your entire genome.

Alina Ignatiuk – Engagement Manager and Alexander Sychov – Principal Data Scientist, GAIT

• Democratization of AI: the power of non-experts to use and replicate AI technologies will increase. This will be enabled by the number of educational programs designed for non-tech specialists, applications with user-friendly interfaces for AI developers and higher salaries and interest in AI products on the market. Such democratization will help to increase the exposure of AI technologies and literally bring them to each household and enable “the masses/ wider community” to take advantage of that and improve quality of life. At the same time, tech-oriented companies will still face the shortage in skilled specialists that are able to work on and develop break-through innovations in this field.
• "That who owns data, owns the world": tech giants will be using monopolistic advantage towards the big data they own and make extra profits by discovering insights using AI. At the same time, more data will become publicly available, leading to more public initiatives and improvement of community services.
• Fragmentation and diversification of the AI and data science market: Although the major fundamental developments and break-through innovations will still be done by “AI blue chip” (think leading global technology and large analytics companies, plus governments of major countries), more companies all over the world will be joining this sector with increasing number of AI technology-based startups.
• The development and spread of AI applications and technologies will become a two-way street for human beings: keep rising issues with privacy of personal data, increasing invasion of personal privacy, and increasing psychological manipulation by using more implicit, psychologic and behavioral data. At the same time, customers will be provided better variety and quality of services and products, also AI will enable introduction to the market break-through products and services that will lead to the next level of living standards.
• The development of AI technologies enables their application in new industries, like legal services: for example, the US precedent based legacy system generates a huge amount of data like judicial ruling, precedents, court hearing summaries etc. This is a perfect background for AI application and provides great opportunities of costs reduction for law companies.
• Moving from human-based insight generation to AI-based insight generation: this will be enabled by the increasing number of publicly available socio-economic data and market trends.
• AI will be an important driver of productivity increase in traditional industries: brining new AI technologies and products to industries like agriculture, manufacturing, health care and public sector and will lead to the next technological revolution and productivity increase.
• AI will continue to bring IT and data management industries to the next level: increasing understanding and usage of companies’ data will lead to a higher demand and the next level for new AI-based applications, data integration and data management projects among all global companies.
• Business interest in the AI field will drive the scientific inventions in the AI field and other related fields, like mathematics, physics, medicine.
• AI will continue to change higher education market: AI-oriented programs will spread out not only within technical majors, but more dual-degree programs will appear for business, medicine, social sciences, etc. This trend will expand in developed countries and be echoed by developing markets.
• AI will continue to shape the global labor market:  this impact will be observed in two main directions – on one hand the number of low-qualified specialists in traditional labor intensive industries (like banking, insurance, government, education, healthcare, etc.) will be replaced by AI products; on the other hand the development of the data science labor market itself will lead to bigger diversity of specialists required. Increasing the number of educational programs and trainings will bring to the market more “citizen data scientists,” data visualization specialists, cross-fields specialists (AI and healthcare, AI & insurance, AI & environment, etc.).

The content & opinions in this article are the author’s and do not necessarily represent the views of RoboticsTomorrow

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