Me: \u201cAlexa, tell me what will happen in 2020.\u201d<\/p>\n
Amazon AI: \u201dHere\u2019s what I found on Wikipedia: The 2020 UEFA European Football Championship\u2026[continues to read from Wikipedia]\u201d<\/p>\n
Me: \u201cAlexa, give me a prediction for 2020.\u201d<\/p>\n
Amazon AI: \u201cThe universe has not revealed the answer to me.\u201d<\/p>\n
Well, some slight improvement over last year\u2019s responses<\/a>, when Alexa\u2019s answer to the first question was \u201cDo you want to open \u2018this day in history’?” As for the universe, it is an open book for the 120 senior executives featured here, all involved with AI, delivering 2020 predictions for a wide range of topics: Autonomous vehicles, deepfakes, small data, voice and natural language processing, human and augmented intelligence, bias and explainability, edge and IoT processing, and many promising applications of artificial intelligence and machine learning technologies and tools. And there will be even more 2020 AI predictions, in a second installment to be posted here later this month. <\/p>\n The state of AI in 2020<\/p>\n <\/fbs-accordion>Getty<\/small><\/figcaption><\/figure>\n \u201cVehicle AI is going to be designed to break the law: Autonomous Vehicles should not and cannot be designed to drive the speed limit (roads were designed with speeding factored in)\u2014it will have to learn to go over the limit and match speeds of other drivers in order to be implemented wide-scale and keep other drivers safe. The same idea applies to car crashes\u2014cars will have to learn to break the law\/driving rules if it means saving more lives. In 2020, we\u2019re going to see a greater call for and debate around designing algorithms to act illegally\u201d\u2014Dr. Stefan Heck, CEO, Nauto<\/a><\/p>\n \u201cWe\u2019ll see a rise of data synthesis methodologies to combat data challenges in AI. AI algorithms built on deep learning can only work accurately when they\u2019re trained and then validated on massive amounts of data. But companies developing AI often are challenged getting access to the right kinds of data, and the necessary volumes of data. To combat this, companies can take data that has already been collected and synthesize it to create new data. Data synthesis doesn\u2019t eliminate the need for collecting real-world data\u2014this will always be critical to the development of accurate AI algorithms. However, it can augment those data sets\u201d\u2014Rana el Kaliouby, PhD, Co-Founder and CEO, Affectiva<\/a>, author of Girl Decoded<\/em><\/a><\/p>\n \u201cBoard rooms and C-suites of the world’s largest B2C brands have turned their focus onto personalization and AI-driven customer experiences, yet they are struggling to bring their vision into reality. This is due to departmental structures (and politics), aging technology infrastructures, and a lack of senior and mid-level managers who can articulate business needs and translate them into technological solutions. Because such a transformation is critical to their survival, we\u2019ll see increased emphasis and risk-taking on creating the customer experience of the future\u201d\u2014Liad Agmon, CEO, Dynamic Yield<\/a><\/p>\n \u201cThroughout 2020, expect to see the energy around artificial intelligence and machine learning (AI\/ML) shift from research into engineering, bringing an increased focus on managing the AI\/ML lifecycle in production. Expect to also see increased investment in data preparation\u2014an integral component in any data project that is still often regarded as the biggest bottleneck for many\u2014driving improvement in data quality and relieve IT from the pressures of preparing data. Lastly, there will be an increased focus on monitoring AI\/ML pipelines, which helps track the quality of prediction serving in production, as well as compares production workload traces to repeat the cycle of data prep and quality in the face of new evidence\u201d\u2014Joe Hellerstein, Co-Founder and CSO, Trifacta<\/a><\/p>\n \u201cAs voice becomes more useful for consumers to quickly and easily do things with just a simple voice command, we\u2019ll see the volume of voice queries explode. Each of these queries represents a consumer intent, generally highly correlated with an action consumers want to take\u2014add to my shopping cart, navigate to the nearest Starbucks, etc.\u2014high value, transaction-oriented consumer intents.\u00a0Any consumer-focused AI platform will need to be able to collect and analyze this massive volume of high value consumer touch points represented by voice commands\u201d\u2014John Foster, CEO, Aiqudo<\/a><\/p>\n \u201cIn recent years, we have seen more and more countries developing national artificial intelligence strategies. The enormous economic value that lies in AI will likely enable an increase in efficiency and well-being in all aspects of life. However, from the perspective of a government agency, the move towards implementing this technology must be accompanied with appropriate infrastructure, availability of databases and regulation, especially in matters of autonomous decision-making and privacy. There must also be thorough preparation of human capital to cope with the labor market challenges brought about by an era of intelligent machines\u201d\u2014Aharon Aharon, CEO, Israel Innovation Authority<\/a><\/p>\n \u201cThe truth is that voice is far from where it needs to be before it\u2019s a reliable AI-based engagement tool, and 2020 will not be the year we reach that destination. Even some of the most advanced AI-based voice solutions are only right for certain applications, struggle outside of controlled environments and must overcome a higher level, or perhaps deeper depth, uncanny valley factor that comes with speech-based human-machine interaction. Voice will get better, we will find the right applications to deploy it and we will get used to using it, but for 2020, it\u2019s not ready and narrow so don\u2019t put all your eggs in that basket just yet\u201d\u2014Ryan Lester, Senior Director, Customer Engagement Technologies, LogMeIn<\/a> <\/p>\n “Retailers will increasingly turn to AI as their main source of marketing insights as opposed to surveys and studies. Many retailers, however, will still struggle to turn those insights derived from AI into actionable business rules for their pricing, supply chain and merchandising. AI is becoming more and more mainstream, but not all companies yet realize that using AI\u00a0technology for data analysis is only the first step into a larger process of true transformation”\u2014Georges Bory, Managing Director,\u00a0ActiveViam<\/a><\/p>\n “In 2020, from a few corners of the world, we will start to see AI built to tackle hard, meaningful problems; AI built to help us humans do what we cannot, as opposed to simply attempting to mimic our abilities. ‘Amplified Intelligence’ is what we call this next-era AI\u201d\u2014Dr. Radhika Dirks, CEO, XLabs<\/a><\/p>\n \u201cWith AI actually baked into the chips themselves, a whole new era of computing at the source is being empowered\u2014and we are only at the beginning.\u00a0AI chips are already improving vehicles\u2019 abilities to process visual data more efficiently, paving the way for autonomous vehicles of the future. For smart cities, AI chips will assist with crucial tasks such as real-time traffic monitoring, locating missing persons, and finding stolen vehicles. For smart homes, chips will ensure more privacy and reliability by processing data at the source. Demand for these new technologies will set the stage for a variety of new applications and use cases, fueling the activity of next generation products and refinement of product needs. A new age of AI chips means a new age of technology\u201d\u2014Orr Danon, Co-Founder and CEO, Hailo<\/a><\/p>\n \u201c<\/strong>We\u2019re going to start seeing some dramatic breakthroughs and some real transformative changes in 2020. To an extent not seen to date, AI and ML will, so to speak, emerge from the lab and infiltrate your life. A recession, if there is one in 2020, will accelerate this coming AI\/ML impact. That\u2019s because a lot of the efforts that AI and ML are focused on automation and building efficiencies in the way humans work\u201d\u2014Peter Guagenti, CMO, MemSQL<\/a><\/p>\n \u201cYesterday\u2019s Hadoop platform teams are today\u2019s AI\/analytics teams. What used to be statistical models now has converged with computer science and has become AI and machine learning. So data, analytics, and AI teams can\u2019t be siloed from one another any longer. In fact, they need to collaborate and work together to derive value from the same data that they all use. In 2020, we\u2019ll see more organizations building dedicated teams around the data stack\u201d\u2014Haoyuan Li, Founder and CTO, Alluxio<\/a><\/p>\n \u201cMachine learning and artificial intelligence will deliver cost savings through greater cloud efficiencies. Achieving this will require the environment or application to understand when it needs more resources and then automatically scaling up those resources to meet the increased demand. Conversely, the technology will need to understand when specific resources are no longer needed and safely turn them off to minimize costs\u201d\u2014Frank Jablonski, VP, Global Marketing, SIOS Technology<\/a><\/p>\n \u201cNew advances in enterprise graphs and machine learning will enable context identification and content recommendation, helping information workers cope with information sprawl. AI will help transform the digital workplace by augmenting and focusing human efforts and reducing cognitive burden by delivering information based on users\u2019 current context\u201d\u2014Yaacov Cohen, Co-Founder and CEO, harmon.ie<\/a><\/p>\n \u201cIn 2020, AI will dramatically improve the employee experience (EX). The ability to automatically and instantly collect data from across multiple channels, analyze it and provide actionable insight will enable support agents to more quickly, easily and accurately address customer inquiries and come to highly satisfactory issue resolution\u201d\u2014Anand Janefalkar, Founder and CEO, UJET<\/a><\/p>\n \u201cBecause the supply chain industry has historically been slow to adopt when it comes to digitization, there simply hasn\u2019t been enough collected data for AI\/ML algorithms to make reliable suggestions. As we start to see a more modern supply chain emerge in 2020, AI and ML algorithms will enable a 30,000-foot view of the supply chain and provide valuable insights to ease previously tedious processes like product redirects, new partner and supplier onboarding, order cancellations, oversupply and more\u201d\u2014Jorge Rodriguez, SVP of Product Development, Cleo<\/a><\/p>\n \u201cEnterprises will finally transition into deploying\u00a0complex\u00a0AI models in\u00a0production\u00a0at\u00a0scale. So far, most AI applications were either experiments (i.e. not in production), or simple recommendation\/prediction\/regression models, or they were applied on smaller problems. In 2020, we will see more enterprises getting bolder with their AI ambitions and requiring their vendors to support large deployments\u201d\u2014Sudhir Jha, Senior Vice President and Head of Brighterion<\/a>, Mastercard<\/a><\/p>\n \u201cAs data volumes continue to explode, one of the key challenges is how to get the full strategic value of this data. Object storage will be instrumental in helping to process AI and ML workloads in 2020 as this newer storage architecture leverages metadata in ways traditional file storage doesn\u2019t\u201d\u2014Jon Toor, CMO,\u00a0Cloudian<\/a><\/p>\n \u201cIn 2020, AI will help to close gaps in merchant capabilities that many small businesses with limited resources face. With AI, small businesses can manage their stores seamlessly and efficiently, both by automating work processes (such as employee management, IT service administration, or compliance regulation management) and enabling better inventory and delivery management. AI-based chatbots and virtual assistants further streamline business transactions and increase operational efficiencies, while also providing an optimal customer experience\u201d\u2014Phil Grier, Commerce Engineer, Yahoo Small Business<\/a><\/p>\n “In 2020, AI will become much more accessible to non-technical SEOs. From AI automating meta descriptions, to automating titles or redirects, the technology will permeate day-to-day tasks that are normally time-consuming but necessary. But specialists shouldn’t fear what’s new\u2014instead they should keep a pulse on the industry innovation and align more closely with the data science field whenever possible”\u2014Britney Muller, Senior SEO Scientist<\/a>,\u00a0Moz\u00a0<\/a><\/p>\n \u201cWe will start seeing domain specific digital companions that will help us accomplish tasks and make better use of technology\u2014for example, a proactive co-driver that will help us work the technology in the car, become safer drivers and anticipate our needs during the drive\u201d\u2014Dafna Presler, VP Marketing, Intuition Robotics\u00a0<\/a><\/p>\n \u201c<\/em>5G, IoT integration and the associated ability to connect distributed and diverse data sources into a \u2018systems of intelligence\u2019 will drive huge AI opportunities in consumer and content management, network optimization and integration of an exploding number of sensor-based data sources. Consumer data regulations, legacy technology and the degree of interaction with a technology fluent consumer will gauge adoption. Telco and Entertainment and Energy have fairly low hurdles in these areas and have additional trends that are helping AI adoption\u201d\u2014Simon Moss, Global Partner, Automation and AI, Infosys Consulting<\/a><\/p>\n \u201cBy 2025, \u2018real time\u2019 won\u2019t be good enough. The industry will need AI in order to move beyond real time to become predictive. We will need to go one step further to actually predict what\u2019s coming before it happens\u2014like a meteorologist predicting the weather. Large sets of accurate data can provide context and highlight emerging patterns, revealing degrees of probability. With a little help from AI, prediction is within reach\u201d\u2014Tim Armandpour, SVP of Engineering, PagerDuty<\/a><\/p>\n \u201cIn 2020, we will witness even more of a shift from people-centric to data-centric, automated decision making that leverages predictive and prescriptive analytics, including AI. This will require industries to make better use of technology and analytics in their supply chains in order to respond to customer demands for tailored products and services. With this will come a greater emphasis on the need to reskill the workforce and revamp data infrastructures. We\u2019re already seeing an increased demand for professional development courses and certifications outpacing nonprofessional 4-year degree programs within supply chain as a way to stay relevant\u201d\u2014Abe Eshkenazi, CEO, Association for Supply Chain Management (ASCM)<\/a><\/p>\n “Edge processing with AI creates a better IoT experience. <\/strong>IoT device makers know the benefits of edge-based processing, but until now, many of the challenges in terms of cost, performance and security have made it impractical for implementing in consumer products and systems. The shift toward more use of edge processing in conjunction with cloud connectivity has begun in earnest and will continue to evolve in 2020. From a consumer perspective, this trend will result in an IoT experience that\u2019s faster, more reliable and more private”\u2014Patrick Worfolk, CTO, Synaptics<\/a><\/p>\n “In 2020, enterprises looking to gain full visibility into their hybrid IT environments will require AIOps-based solutions that integrate infrastructure monitoring, workload automation and capacity planning into one platform. As such, vendors who fail to adopt an AIOps model of service and enterprises who fail to invest in end-to-end infrastructure visibility will be unable to deliver on customer requirements and performance SLAs\u201d\u2014Philippe Vincent, CEO, Virtana<\/a><\/p>\n \u201cIn 2020, we will start to see traditional Enterprise Content Management (ECM) platforms begin to focus on collaboration services to enable content accessibility to both internal and external business partners. While traditionally ECM providers have focused on providing a repository of data and automation for a designated audience (business executives, customers, etc.), in the coming year we will see a major shift towards investment in AI and machine learning to improve workflow visualization\u201d\u2014Thomas\u00a0Phelps, VP, Corporate Strategy and CIO,\u00a0Laserfiche<\/a><\/p>\n \u201cAmid the past year\u2019s severe weather\u00a0conditions and with climate change looming, farmers and agronomists are confronted with a number of formidable challenges. As the agriculture\u00a0industry embraces digital management solutions, AI will streamline the entire process, from gathering imagery to delivering actionable insights, improving accuracy\u00a0and reducing human\u00a0error. Integrating AI into farming will strengthen farmers\u2019 expertise and knowledge by providing them with valuable insights and deeper understanding of their fields. Agriculture\u2019s digital revolution will make it easier for farmers and their agronomic partners to develop further solutions to combat the effects of climate change\u201d\u2014Ofir Schlam, Co-Founder and CEO, Taranis<\/a><\/p>\n \u201cAI\u2019s role in the payments space is more than just a means for stronger data science and payment fraud prevention technologies. As financial regulations evolve to meet the new demands of the global payments landscape, we will see AI play an integral role in ensuring merchant compliance and seamless customer experience. In the years to come, AI will enable businesses to streamline operations, better serve customers and in turn, create exponential developments economically and culturally\u201d\u2014Igal Rotem, CEO, Credorax<\/a><\/p>\n \u201cIn the coming years, companies should focus less on how tech can replace traditional jobs and more on how AI and human expertise can complement one another. We strongly believe in the combination of AI and human expertise\u2014we call it the science and the art. Tech will always be able to do things faster than humans. But there are also areas where tech will never replace humans. Negotiations, for example, require intuition, confidence, and deal making experience that computers cannot replicate.\u00a0Our belief is that we will need to continue to work towards finding that perfect balance between science and art in 2020\u201d\u2014Guy Zipori, Co-Founder and CEO, Skyline AI<\/a><\/p>\n \u201cIt\u2019s easier now than ever to do in-database indexing and analytics, and we have tools to make sure data can be moved to the right place. The mysticism of data is gone: consolidation and the rapid demise of Hadoop distributors in 2019 is a signal of this shift. The next focus area will be very distributed, or \u2018wide data.\u2019 Data formats are becoming more varied and fragmented, and as a result different types of databases suitable for different flavors of data have more than doubled\u201d\u2014Dan Sommer, Senior Director, Global Market Intelligence Lead, Qlik<\/a><\/p>\n \u201cMany AI solutions today attempt to convert user data into advertising money. Amidst AI\u2019s meteoric rise, I expect in 2020 to see more users raise the following question: how can these same AI solutions work\u00a0for me<\/em>\u00a0on a daily basis, as opposed to work\u00a0on me<\/em>. This question alone will push\u00a0AI into places we perceive today as sacrosanct, whether that be parenting, relationships, education, etc. On a positive note, this means that AI tools will allow for greater individualization and a personal touch. Companies creating these AI applications, however, must prioritize protecting the personal data they collect\u201d\u2014Tal Guttman, Co-Founder and CEO, Jiminy<\/a><\/p>\n \u201cWe\u2019ll see the rise of Digital Ethics Officers, who will be responsible for implementing ethical frameworks to make decisions. This includes security, bias, intended use, and built-in governance\u201d\u2014Sanjay Srivastava, Chief Digital Officer, Genpact<\/a><\/p>\n \u201cAs opposed to being a cause of job insecurity in 2020, artificial intelligence will prove to be a crucial tool for improving careers. Through AI, employers will be able to provide enhanced opportunities for their employees and facilitate the diversity of experience they crave. Thanks to AI, employees will be able to expand and enhance their skill sets and ensure they stay relevant in a rapidly evolving market.\u00a0Accountability, particularly with respect to explaining results and bias prevention, will continue to go hand-in-hand with AI’s development\u201d\u2014Amichai Schreiber, Co-Founder and CTO, InnerMobility<\/a> by Gloat<\/p>\n \u201cThere is a fundamental shift occurring in the insurance industry: carriers know they need to significantly improve customer experience and ensure that their products are relevant and personalized. AI will be used by many in 2020 to achieve these goals and AI will be crucial for modernizing the underwriting experience. AI enables insurers to better utilize the troves of data at their disposal to benefit from vital client insights that maximize their services and products. This results in satisfied customers and a more efficient business\u201d\u2014Yaffa Cohen-Ifrah, CMO and Head of Corporate Communications, Sapiens<\/a><\/p>\n \u201cMachine learning and neural networks are the muscle behind AI innovations that are taking the world by storm. Over the last 3 years, we have seen a definitive shift on the Udemy platform to AI and data science courses, as businesses try to address their knowledge gaps. With the increasing demand for data scientists and AI experts, we anticipate the continued growth of what we call Capability Academies. Capability Academies are in-depth training initiatives to develop and sustain skill capabilities that support specific business strategies and function areas”\u2014Shelley Osborne, VP of Learning, Udemy<\/a><\/p>\n \u201cIn 2020, real world implementations of AI and machine learning will grow, especially in the banking and finance industry. Organizations that implement AI solutions will be able to accelerate and improve finance and treasury processes. Specifically, we will see the deployment of intelligent chatbots that will answer customer and vendor inquiries as well as intelligent software agents for invoice capture, cash application, exception or dispute handling, calculating customer credit risk and detecting fraud\u201d\u2014Vishal Awathi, SVP of Technology and Products,\u00a0Serrala<\/a><\/p>\n \u201cEmpathy in the machine dialogue will become very important. Voice assistant adoption will be massively improved if you get a level of empathy in that machine dialogue. Alexa and other players will likely become more emotional and detect things like frustration in user responses. It\u2019s going to be a significant improvement in 2020 and beyond\u201d\u2014Holger Reisinger, SVP Large Enterprise, Jabra<\/a><\/p>\n \u201c2020 will see more focus on explainable AI, to reduce any bias in the predictions. Data scientists will become an integral part of the product teams and work closely with them to create a data-first approach to app development, instead of focusing on making sense of data generated by apps\u201d\u2014Sanjay Jupudi, President, Qentelli<\/a><\/p>\n \u201cNatural Language Processing (NLP) combined with AI will increasingly make decisions that may be inscrutable to human observers, whether by analyzing stock data to make investment decisions, or parsing mountains of unstructured social media for broad sentiment analysis around a brand, or specific intelligence on who to target for which product pitch. But, training is everything. A lot of these algorithms are being trained on existing human practices that are inherently biased and problematic. It\u2019d be naive to assume we can eliminate that from NLP algorithms at the outset\u201d\u2014Eric Sammer, Distinguished Engineer, Splunk<\/a> <\/p>\n \u201cIn 2020, healthcare organizations will leverage AI to assist medical staff in providing patients with possible prognoses for their symptoms. Developing AI tools that can be translated from one health system to another will have some complexity, but once the healthcare industry puts an effort behind allowing AI to become more scalable, solutions will be better positioned to move beyond a departmental or hospital specialty\u201d\u2014Jim VanderMey, CIO, OST<\/a><\/p>\n ” As more companies set out to solve problems that are about the relationships between things, locations, and people, graph technology will become more popular in enterprises. The problem for graph in 2020 is the lack of understanding around the technology, and as a result the shortage of talent with specific graph skills. Thinking through problems with a \u2018relationship-first\u2019 mindset will help graph adoption to be successful\u201d\u2014Patrick McFadin, VP of developer relations, DataStax<\/a><\/p>\n “Voice usage will continue to explode, but not in the way you think. Assistants like Alexa and Siri have somewhat topped out as a standalone experience around things like music, podcasts and weather.\u00a0The breakthrough will be that the primary way we use voice will be in our apps, telling our apps what to do e.g. order dinner, buy movie tickets or research a product. Every app will have to be re-engineered to be voice-first, just like we all became mobile-first a decade ago\u201d\u2014Tobias Dengel, CEO, WillowTree<\/a><\/p>\n \u201cUsing the power of AI at the edge and self-learning models, in 2020, machine learning models can move beyond traditional analytics capabilities and significantly improve predictive functionality and overall ROI. With edge AI, software can proactively interface with live data streams and cater to intelligence at or near the source, leading to increased overall productivity, efficiency, and cost-savings\u201d\u2014Senthil Kumar, VP of Software Engineering, FogHorn<\/a><\/p>\n \u201cThe internet of things (IoT), through services such as Echo and Siri, has been democratized and popularized for consumers in the home. In 2020, IoT will be democratized in the workplace with the advent of affordable, plug-and-play technology that improves the user experience and productivity in meetings. AI will be crucial to this process\u201d\u2014Oded Gal, CPO, Zoom Video Communications<\/a><\/p>\n \u201cGDPR and CCPA are good starts in regulating data, but soon the best AI and data solutions companies will go beyond those guidelines and promise that any users\u2019 data they gather will be employed to directly benefit those users. The organizations able to make that promise will provide AI and data solutions that gather data about users\u2019 behavior and context, correlate users\u2019 action with best outcomes, then teach users best-practices\u2014and they\u2019ll refine those practices with each and every user\u201d\u2014Jake Saper, Partner, Emergence Capital<\/a><\/p>\n \u201cIn 2020, AI will play a much bigger role in driving two-way dialogue with data. AI does the heavy lifting, diving deep into data and uncovering insights (ones that teams didn\u2019t even know to look for in the first place). Over time, AI can learn what benefits the end user and cater the search accordingly. It continues a harmony that can exist between man and machine, bringing together the strengths of human creativity and context, with the power and scale of machines. AI can fill a resource gap, as an \u2018always on\u2019 assistant, freeing up teams for more creative and high-value tasks\u201d\u2014John Bates, Director of Product Management,\u00a0Adobe\u00a0Analytics<\/a><\/p>\n \u201c<\/strong>With the rise of automation to carry out day to day business functions, leveraging AI to augment human capabilities will continue to be a delicate balance. Whether being used to automate repetitive tasks (data prep, etc.) or connecting pipelines through contextual information from you and your peers, AI will begin to infiltrate all areas of business functions\u201d\u2014Laurent Bride, CTO and COO, Talend<\/a><\/p>\n \u201cIn 2020, the\u00a0Turing\u00a0Test is passed emphatically, and the potent combination of video deepfakes and conversational chatbots give human-like AIs massive swagger. These AIs benevolently make their way into customer service, shopping, and healthcare. They take a darker turn toward social media, blackhat security, and political campaigns. In the end, we don’t know the difference between humans acting fake and fake humans\u201d\u2014Nick Caldwell, Chief Product Officer, Looker<\/a><\/p>\n “In 2020, companies will realize that they are limiting their understanding of customer behaviors by only considering rational decision making enabled by AI-driven fact-based technology. The missing piece is that as humans, the core of our decision making is emotional. Looking forward, companies must learn to balance the use of Artificial Intelligence with Artificial Humanity, which accounts for the emotional\u2014and sometimes irrational\u2014drivers behind human decision making. Companies will look to uncover the valid and valuable use of AI in fact-based rationale, and the need to balance this with creating emotional bonds through human interactions”\u2014Matt Matsui,\u00a0Chief Product Officer, Calabrio<\/a><\/p>\n \u201cAI is fueling a paradigm shift in software and, more broadly, in how businesses across industries deliver digital experiences.\u00a0Heading into 2020 and beyond, companies will become more forward-thinking by designing customer experiences with an \u2018AI-first\u2019 mindset\u2014moving beyond automating tools for specific tasks, or adding \u2018smart\u2019 features to traditional software, but rather leveraging AI as a problem-solving partner\u201d\u2014Scott Prevost, Vice President of Engineering,\u00a0Adobe\u00a0Sensei<\/a><\/p>\n \u201cWe expect to see the use of AI accelerate in 2020, as companies increasingly use the data they collect to build and deploy AI models powering new services and generating new business insights. If they hope to keep these companies happy, data centers need to respond, not just with faster networks and servers in their core data centers, but also edge data centers that enable the deployment of AI models closer to end-users. In 2020, we expect data center owners and operators to increasingly focus on how they can deliver the performance their customers need for AI-enabled cloud services\u201d\u2014John Schmidt, VP, Cloud Hyperscale Solutions, CommScope<\/a><\/p>\n \u201cIn 2020, the focus on analytics will be driven by increased regulatory and compliance pressures, risks from data breaches and ransomware, and the need to properly classify data for Artificial Intelligence and Machine Learning projects.\u00a0Without \u2018clean\u2019 data of value, those AI and ML projects will stumble. I expect more businesses to be hitting this point in their \u2018data maturity\u2019 where analytics projects take priority\u201d\u2014<\/strong>Matt Tyrer, Technology Evangelist, Commvault<\/a><\/p>\n \u201cThe greatest potential for AI is not \u2018artificial\u2019 but \u2018augmented\u2019 intelligence. How we can design and make accessible machine intelligence so that it can help us get to optimal decisions and actions? When we give artificial intelligence a seat at the table with the other diverse humans, we increase the performance of the team. We\u2019re only beginning to explore the potential of augmented intelligence\u201d\u2014<\/strong>Loni Stark, Senior Director, Content & Commerce,\u00a0Adobe<\/a><\/p>\n \u201c2020 will be about finally creating an in-meeting experience that\u2019s as adaptive as the pre-call experience. For users, that means making sure the right people get the right focus, users always have the right information in front of them at the right time, and everyone is in a position to make more dynamic decisions faster. From an infrastructure perspective, that means gathering information across a multitude of systems and applying it to future needs by leveraging AI to adjust based on predictive behavior\u201d\u2014Jordan Owens, VP of architecture,\u00a0Pexip<\/a><\/p>\n \u201cWhat the world is calling AI today will split into several areas in 2020, which someone in marketing will inevitably create pithier names for. These include: Robotic Process Automation (RPA); automated feature engineering and selection; perception AI, which is the automation and refinement of physical perception; and resource allocation AI, the marriage of optimization technologies to sense and respond to demands in real-time\u201d\u2014Cheryl Wiebe, Practice Lead, Industrial Intelligence Consulting, Teradata<\/a><\/p>\n “With respect to Artificial Intelligence, just because data exists within an organization doesn\u2019t mean that data is in a usable, transferable format. 2020 is the year that businesses will begin to understand that their data is not AI-ready, rendering their business processes inefficient, ineffective or inaccurate”\u2014Carl Vause, CEO, Soft Robotics<\/a><\/p>\n \u201cA massive expansion for the Internet of Things and the growing trend of software instrumentation will put an increased focus on time series data in 2020. Smart cars and internet-connected machines are starting to produce huge volumes of time-stamped data that companies need to collect and analyze, while new software monitoring and measuring strategies have created enormous logs of events that need similar treatment. These trends account for the largest portion of data growth today\u2014and the data from these sources always has a core element of time that is crucial to any meaningful analysis. Many enterprises will realize they need a specific strategy for time series data to glean the full value of its business potential\u201d\u2014Evan Kaplan, CEO, InfluxData<\/a> <\/p>\n \u201cAI is both under- and over-hyped in that it can be transformative for a business, but leaders aren\u2019t sure how to get the best use out of it. CIOs should evaluate what processes can be automated to free up time for workers to deliver deeper insights to the business. Technologies like AI will also expand software features that have yet to be discovered. Those that enable SaaS companies to provide lower costs of delivery will control the market as the space matures and competition grows\u201d\u2014Karl Mosgofian, CIO,\u00a0Gainsight<\/a><\/p>\n \u201cAs AI evolves, it will soon become the expected entry point for contact center technology standards. Additionally, AI will be accessible and leveraged by both smaller contact centers and large contact centers. AI will also begin to integrate with omnichannel solutions to instantly solve consumer questions\/demands. However, the biggest shift in the use of AI is expected to come from the collections industry as it looks to both catch up and automatically comply with ever-changing FCC regulations\u201d\u2014Jesse Bird, Co-Founder and CTO, TCN<\/a><\/p>\n \u201cWith AI and data becoming centralized, manufacturers are forced to pay massive fees to top cloud providers to access data that is keeping systems up and running. As a result, new routes to training AI that can be deployed and refined at the edge will become more prevalent. As we move into the new year, more and more manufacturers will begin to turn to the edge to generate data, minimize latency problems and reduce massive cloud fees. By running AI where it is needed (at the edge), manufacturers can maintain ownership of their data\u201d\u2014Max Versace, PhD, Co-Founder CEO, Neurala<\/a><\/p>\n \u201c<\/strong>In 2020, we’ll start to hit the limits of computing power for Deep Learning. As Moore’s Law slows, companies will run out of computing resources for complex AI tasks. Instead of just throwing more GPUs at a problem, we’ll have to think about optimization, and using the resources we have in the most efficient way\u201d\u2014Omri Geller, Co-Founder and CEO, Run.AI<\/a><\/p>\n \u201cIn 2020, we will see the integration of conversational AI emerge as a top priority for those in the c-suite. This tracks to the demand Accenture has seen from clients in the C-suite who are interested in incorporating conversational AI technologies into their business models. The same research also cites C-suite-led commitment as essential to scaling AI\u201d\u2014Laetitia Cailleteau, Conversational AI Global Lead at Accenture and a Founder of Accenture\u2019s London\u00a0Liquid Studios<\/a><\/p>\n \u201cAs 5G leads to greater IoT adoption and creates new opportunities for disruption, in-memory-accelerated real-time machine learning will be needed to address these challenges. AI\/ML will get closer to edge and IoT devices and will emerge as the best approach to creating a great application experience\u201d\u2014John DesJardins, VP of Solution Architecture and CTO, Hazelcast<\/a><\/p>\n \u201cAI techniques with which we are familiar today\u2014such as neural networks, event clustering, and regression\u2014will be joined by less familiar techniques such as topological data analysis (TDA) and generative neural nets. TDA holds promise in commercial applications because data has shape and shape matters. TDA maps the geometric structure of datasets that are large, highly dimensional or noisy to detect patterns and uncover insights\u201d\u2014Phil Tee, CEO,\u00a0Moogsoft<\/a><\/p>\n \u201c2020 will see a consumer outage of a banking site or retail site driven by an AI algorithm making the wrong decision. The AI algorithm will observe unusual behavior and wrongly determine, for example, that a breach is happening. It will then take the system offline, resulting in loss of revenue and service to customers. This incident will result in a shift back to \u2018decision support\u2019 as people become more skeptical and risk-averse\u201d\u2014Antony Edwards, CTO, Eggplant Software<\/a><\/p>\n “While 2020 won’t be the year that we hand over self-driving keys to our cars, we should see some incremental yet meaningful improvements in AI, along with an ever-increasing number of applications. It will be exciting to see AI continue to work its way into all kinds of technology products in seemingly simple ways that make processes more efficient”\u2014Lane Lillquist, Co-Founder and CTO, InCloudCounsel<\/a> <\/p>\n \u201cIn 2020, companies will focus less on shipping traditional applications and focus more on\u00a0selling AI use cases. They\u2019ll offer customers AI models for a specific use case (i.e. diagnosing repair needs in 5G infrastructure) and separate models for a different use case (i.e. determining when oil and gas infrastructure needs to be retired). Organizations will rely less on one-size-fits-all apps and instead leverage highly specialized models for custom use cases, which will ultimately deliver better results\u201d\u2014Arka Dhar, CEO, Zinier<\/a><\/p>\n \u201cThe use of conversational AI will provide a new channel for engaging both the workforce and customers. Moving beyond simple chatbots, the powerful combination of context awareness, natural language processing, ability to have smart interactions, and more robust intent libraries will enable virtual agents to take action. Virtual agents will not only be able to converse in dialogue but also will be able to seamlessly transition into the delivery of a multi-media experience to guide users to information, answers, or troubleshoot a problem\u201d\u2014John Prestridge, SVP of North America,\u00a0EasyVista<\/a><\/p>\n \u201cWe’re still very early in the adoption of AI in retail. To date, most of the adoption has been in the supply chain, such as automating inventory data management, and customer service (chatbots to answer questions). I believe we’ll see more retailers deploying AI-enabled technologies, such as cameras that measure store traffic and make adjustments to digital screen content, as well as deployment of voice-enabled assistants that customers can use while shopping in-store\u201d\u2014Trey Courtney, Global Chief Product & Partnerships Officer, Mood Media<\/a><\/p>\n \u201cAI will play an integral role in the continual evolution of building the ideal ad stack. AI will allow publishers to dynamically adjust demand sources\u2014an act that has historically been performed by humans\u2014and ultimately create a way to maximize revenue in real time\u201d\u2014Kurt Donnell, President, Freestar<\/a><\/p>\n \u201cWhile more companies will take an \u2018AI First\u2019 approach to their digital transformation projects, many will still struggle to operationalize AI, with the biggest challenges stemming from lack of trust and transparency in automated decisioning systems. Unless ethical issues are addressed, AI adoption will slow down, threatening its value in many enterprises. Balancing the risk of AI versus the value of AI will become a top-of-mind discussion for most Fortune 500 CEOs and boards\u201d\u2014Matt Sanchez, CTO, CognitiveScale<\/a><\/p>\n \u201cThanks to AI, the speed at which marketers will be able to gather, analyze and execute targeted campaigns will continue to get faster, resulting in more effective marketing strategies. Marketers will be able to create the right piece of content for the right person and place the piece in the right channel in real time, as opposed to six months from now\u201d\u2014Michelle Yancey, Group Account Director, Centerline Digital <\/a><\/p>\n \u201cAI engines will continue to be an essential component of advertising technology. In 2020, companies that over-rely on manual optimizations will be left further behind as no human can compete with the power of an AI engine. Additionally, there is a growing trend of expanding AI tools from the core business into the operational aspects across teams. As the tools leveraged continue to be commoditized, it will enable more and more teams to utilize them, whether they are AI experts or not\u201d\u2014Tal Mor, CTO, Tremor Video<\/a><\/p>\n \u201cAI’s expanded application, from machine-learning and reasoning (e.g., semantic targeting, ‘you may also like’) to consumer-facing, real-world uses (e.g., voice recognition, robotics) will increase tensions between tech innovation and consumer distrust with data collection and use. Ultimately, that dynamic will put significant pressure to moving forward a national, comprehensive regulatory policy for consumer privacy\u201d\u2014Jenna Umbrianna, General Manager, Anagram<\/a><\/p>\n \u201cAdvertising is the science and art of persuasion. And persuasion is all about understanding, predicting and improving the science of human behavior. Artificial intelligence will bring us closer to an era when advertising is less annoying and more pleasing, useful, relevant and entertaining\u201d\u2014Tod Loofbourrow, CEO and Chairman, ViralGains<\/a><\/p>\n \u201cAn often overlooked aspect of AI is the human intelligence it is fused with. As people, we possess ingenuity, creativity, and innovative problem-solving abilities; this will likely remain irreplaceable by machines for the foreseeable future. Therefore, the real value of AI is realized when highly skilled individuals are directing and applying the technology to achieve solid business outcomes for brands. In 2020, advertisers will place a premium on having strong teams who can extract the maximum value from AI\u201d\u2014Matt Fanelli, SVP, Digital, MNI Targeted Media<\/a><\/p>\n \u201cWe’ve already seen a shift in intelligent assistants to push not pull, but we expect it to continue in 2020. The makers of intelligent assistants are moving away from a pull mentality and into a push mentality (this is referred to as a \u2018proactive\u2019 assistant). An example of this, if your phone learns your routine and realizes that you’ve deviated from it, say, due to traffic, then it can suggest bumping your upcoming appointments or sending a \u2018running late\u2019 message. Companies that understand this trend also understand that these use cases are only unlocked through AI\u201d\u2014Ben Johnson, VP, Mobile & Emerging Technology, Rightpoint<\/a><\/p>\n \u201cAs AI continues to move into the mainstream its hunger for data will continue to grow. The development of tools to quickly transform and integrate data to make it more accessible for constructing models will greatly reduce the time investment of data preparation. The result will free data scientists to apply their craft as well as allow smaller organizations to develop models without the need for large multifunctional teams\u201d\u2014Dr. Brandon Haynie, Chief Data Scientist, Babel Street<\/a><\/p>\n \u201cAdvancements in explainable AI will continue in 2020 and beyond as new standards are developed around the technical definition of explainability, slowly followed by new technologies to address the explainability problem for business leaders, non-technical audiences. In real estate, for example, offering a compelling explanation for why a mortgage application was rejected by an AI-driven platform will eventually be a necessity as AI adoption continues. Although we\u2019ll see evolving technical tools and standards, progress for layperson tools will be slower with some narrow and domain-specific solutions emerging first. Like the general public\u2019s understanding of \u2018the web\u2019 in the 90s, awareness, understanding and trust in AI will gradually increase as the capabilities and use of the technology spreads\u201d\u2014Sheldon Fernandez, CEO, DarwinAI<\/a> <\/p>\n \u201cMost people spend a significant amount of their time setting up meetings, preparing for meetings and getting IT equipment working. I envision that time shrinking in 2020 and in the coming years it will go to nothing (hopefully). Voice assistants for laptops and smartphones will continue to become more intelligent and support your everyday work needs on the go. For example, you will be able to say, \u2018Open up the doc I was working on yesterday\u2019 or \u2018Find me David\u2019s email from today.\u2019 This trend will open up people\u2019s time to be more productive and focused on higher-level work, not glued to their emails or texts\u201d\u2014Patrick Worfolk, CTO, Synaptics<\/a><\/p>\n \u201cAI continues to receive lots of attention and massive investment dollars. As the hype cycle continues, it\u2019s going to become more important for companies to focus on what really makes or breaks AI: the data. AI algorithms are only as good as the data that feeds them. In 2020, we\u2019ll see AI companies that don\u2019t have good, clean data, hit stumbling blocks as other companies with solid data flourish\u201d\u2014Chris Harrington, CEO, XANT<\/a><\/p>\n “We are now at a tipping point for business adoption of automation and AI. Businesses that incorporate automation in the right way, considering the customer experience and entire customer journey, will experience unprecedented growth. In 2020 and beyond, businesses that embrace conversational interactions with customers will see increased team efficiency, stronger customer relationships, and faster growth\u201d\u2014Fergal Reid, Principal Machine Learning Engineer, Intercom\u00a0<\/a><\/p>\n \u201cIn 2020, I expect to see profound advancements in AI across the healthcare sector. The complex, multivariate problems that abound in medicine\u2014from biology to back office to the bedside\u2014are ideal platforms for machine learning algorithms. There are many wrinkles still to work out, such as patient privacy, bias in training data, and balancing the collaboration just right between humans and algorithms, but none of these are insurmountable and can all be overcome with a thoughtful, disciplined approach. The potential gains are so large that I\u2019m confident AI will make significant inroads in this field\u201d\u2014Dave Costenaro, Chief Data Officer, Capacity<\/a>\u00a0<\/p>\n \u201cIn 2020, AI and virtual human technology will be leveraged to improve leadership training by providing better insights into how we are applying our skills in realistic and emotionally engaging experiences. We are looking at people truly engaging with virtual personalities for the first time, which is made possible by AI removing the traditional communication barriers between humans and computers (in the form of voice input, speech recognition, and emotional analysis). This is happening in a way that will benefit true development of critical soft skills, creating a more social and communicative work environment\u201d\u2014Remmelt Blessinga, Product Manager, Talespin<\/a><\/p>\n \u201cThe most exciting thing that will continue to surge in AI is how it is helping us to better understand the world around us, from medicine to media. AI is empowering us to learn about music and content at a much deeper level, like how music fans actually listen so we can automatically deliver more engaging and entertaining listening experiences. Through AI we can now produce in a millisecond what in the past would have taken hours in a radio studio, and with advances in machine learning on the near horizon, it will only get better and faster in 2020\u201d\u2014Zack Zalon, Co-Founder and CEO, Super Hi-Fi<\/a><\/p>\n \u201cSynthetic media will continue to improve allowing us to see ourselves in any GIF or transform our voice for use anywhere but that will lead to increasingly authentic deepfakes that allow people to easily assume and manipulate personas, creating added stress to cybersecurity\u201d\u2014Fred Schonenberg, Founder, VentureFuel<\/a><\/p>\n \u201cAI will continue to provide a critical role in technology and marketing in 2020 and beyond.\u00a0Leveraging AI and ML helps companies create more automation within their backend, ultimately helping to communicate more effectively with consumers.\u00a0Being able to predict what a consumer wants and being able to serve them a custom experience (based on their interests\/purchase patterns and history) will prove to be extremely valuable moving forward\u201d\u2014Chris Roebuck, Co-Founder and CEO, Clicktivated<\/a><\/p>\n \u201cIn 2020 and beyond, instead of users seeking and trying to determine what to watch next themselves, built-in predictive technology will suggest what content to watch and when, according to your unique taste profile and viewing habits. Dynamic recommendations will take into account not just users\u2019 likes and preferences, but also life events, seasonal viewing habits, current affairs, and companion preferences\u201d\u2014Evelyn Watters, Founder and CEO, VUniverse<\/a><\/p>\n \u201cThe key difference for social media marketing in 2020 and beyond, lies in automation. Where previously, brands relied on social media managers or teams of people to create content and respond to customers\u2019 queries online, this role will be increasingly replaced by chatbots and AI software, which will allow brands to have a 24\/7 social presence. Machine learning algorithms will help companies optimize the social content they put out, based on their customers\u2019 behavior on social media, and become better at offering individualized product recommendations at a segment-of-one level\u201d\u2014Redickaa Subrammanian, CEO, Resulticks<\/a><\/p>\n “2020 will be the year that AI goes mainstream in healthcare. After 2019, which was an early-adoption year, more medical centers will begin to realize the benefits of AI as word of mouth spreads among the community. Counter-intuitively, this will actually lead to\u00a0fewer\u00a0<\/em>AI healthcare companies overall as there will be winners and losers”\u2014Elad Walach, Co-founder and CEO, Aidoc<\/a><\/p>\n “AI will grow…through AI. The progress of AI will become exponential as AI-based technologies that actually build AI become more widely adopted. This is already beginning through methods such as neural architecture search and automated feature generation”\u2014Shai Yanovski, VP of Data Science, Explorium<\/a><\/p>\n \u201cAs the need for more\u2014and better\u2014AI outstrips the availability of highly-trained data scientists and engineers, companies will turn to technology, training, and education, leveling-up their existing teams and involving more previously unheard voices in the process. This will help lead to less biased and more responsible AI\u201d\u2014Kurt Muehmel, Chief Customer Officer, Dataiku<\/a>\u00a0<\/p>\n \u201cAI will drive sustainability in 2020 and beyond.\u00a0By leveraging AI algorithms, companies can measure environmental and social impacts, automatically make responsible corrections, and optimize operations for sustainability. Though the sustainability challenge grows more complex every day, these technologies can help businesses to operate responsibly\u2014and profitably\u2014via reduced waste, more efficient production, smarter transportation strategies, and reduced resource consumption\u201d\u2014Dr. Michael Feindt, Strategic Advisor and Founder of Blue Yonder, a\u00a0JDA<\/a>\u00a0company<\/p>\n \u201cIn 2020, more and more small business players will start proactively trying to adopt AI-powered technology but will be held back due to the prohibitive cost of available tools and a lack of understanding about \u2018the machine.\u2019 Software vendors that build AI in a way that\u2019s easy to understand and affordable to the average business will see a huge advantage in gaining market share\u201d\u2014Tamara Grominsky, Director of Product Marketing, Unbounce<\/a><\/p>\n \u201cIn 2020, the companies that are going to be successful in operationalizing machine learning as part of their mission-critical processes will break organizational silos to form multi-disciplinary teams. These teams will include data engineers, application developers, and data scientists who will focus on applications rather than on data lakes. They will integrate machine learning native to the applications to avoid the data swamp\u201d\u2014Monte Zweben, CEO, Splice Machine<\/a><\/p>\n \u201cGiven the current state of AI technology, explainable AI is not a reasonable goal or expectation; however, we need to ensure that the new crop of machine learning-enabled data platforms have the necessary infrastructure to implement governance, transparency, and repeatability. If we can assess the data that goes into training a model beforehand, and continuously evaluate that model\u2019s performance, then we can find the flaws in the system that produce unintentional bias and fix them\u2014before we have to hear about them on Twitter\u201d\u2014Irina Farooq, Chief Product Officer,\u00a0Kinetica<\/a><\/p>\n \u201cBots are no longer limited to simplistic customer interactions. They utilize natural language processing to better comprehend the user\u2019s intent and deliver useful, appropriate responses. With more conversations being successfully navigated by bots, brands will increase their usage in order to improve response times and drive greater contact center efficiencies\u201d\u2014Ido Bornstein-HaCohen, CEO, Conversocial<\/strong><\/a><\/p>\n \u201cIn 2020, we will see AR regarded as a business solution that solves problems across industries and changes the way businesses operate. AI and AR will continue to expand into new industries such as commercial real estate and other verticals within physical real estate such as malls and other retail facilities. Key product features will include AR for Indoor Navigation, AR search, AR ticketing systems. Leading apps\/use cases will include apps for building maintenance\u00a0and Operations, apps as central IoT controllers and digital concierge for access control and navigation of buildings\u201d\u2014Emil Alon, CEO,\u00a0Resonai<\/a> <\/strong><\/p>\n \u201cWhile investment in AI across the enterprise has grown tremendously, a major challenge in particular for IT professionals is measuring its ROI. AI-powered enterprise products have taken longer to produce than anticipated due to the challenges of building training datasets, the non-deterministic nature of machine learning, and the lack of maturity of machine learning platform tools. Regardless of these challenges, AI will have an influence across applications and industries that cannot be ignored\u201d\u2014Chris Ackerson, Director of Product Search and Artificial Intelligence, AlphaSense<\/a><\/p>\n \u201cArtificial intelligence will be on full display at the Olympic Games in Tokyo next year. We can expect robotic assistants, powered by AI, to be part of publicity stunts leading up to the Games and actively helping officials during live events, like flagging \u2018off-sides\u2019 in football matches, for example. AI will also likely be involved in predicting polling and projections in the US presidential elections, further bringing this technology into the mainstream view\u201d\u2014Abhijit Sahay, Chief Transformation Officer-Data, Altimetrik<\/a><\/p>\n \u201cSpeech analytics tools were an important bridge to support Robotic Process Automation (RPA), and in 2020, we’ll see these two technologies continue to work hand-in-hand. We\u2019re going to see the most progress in anticipating intent by layering emotion and sincerity with historical data in real-time. Over the next 1-2 years, we’ll be able to determine things like likelihood of person paying their past-due bill. This type of customer intent will end up being crucial for business analytics and planning\u201d\u2014Umesh Sachdev, CEO, Uniphore<\/a><\/p>\n \u201cThanks to significant advances in machine learning, 2020 will be the year that AI tackles the full scope of financial services. 2019 saw the rise of the robo advisor for assets, like investment management, but cracking the liabilities side of personal finance has proven much more difficult. Now AI has advanced to the point that it can take into account all the factors that go into debt management to make personalized and actionable recommendations for consumers\u201d\u2014Adrian Nazari, Co-Founder and CEO, Credit Sesame<\/a><\/p>\n \u201cAs a result of extensive data collection capabilities coupled with high powered accessible compute power, we\u2019ll see AI and machine learning bolster manufacturing across numerous facets of the industry. The main focus will be on the customer, working to enhance their experience by delivering design speed, feedback and optimization. Secondarily, machine learning will vastly impact manufacturing efficiency to improve areas such as process control, error detection and demand anticipation, enabling more flexible supply chains\u201d\u2014Vicki Holt, CEO, Protolabs<\/a><\/p>\n \u201cThere will be a groundswell from grassroots thought leaders and startups to make AI fair, accountable and transparent. With this in mind, more Fortune 100 companies will look for ways to govern AI to minimize algorithmic risks and more states in the US will introduce regulations around AI. Explainable AI will soon become mainstream to help address these issues\u201d\u2014Krishna Gade, Co-founder and CEO, Fiddler<\/a><\/p>\n \u201dIn 2020, we will see applied\u00a0AI and machine learning in auto insurance at scale. Mundane paperwork will be addressed with massive amounts of AI-based processes, while humans will be freed up to deliver a high-touch, personalized experience, offering policyholders emotional support after a traumatic event\u201d\u2014Gitesh Ramamurthy, CEO,\u00a0CCC Information Services<\/a><\/p>\n \u201cAI will be integrated seamlessly and gradually into our day-to-day work to make us more efficient and effective in our jobs. Unlike the ‘killer robots’ people see in the movies, this type of automation will be nearly invisible. As more tasks become automated, leadership needs to prioritize continuous training for employees whose job responsibilities may shift. They\u2019ll also need to emphasize soft skills when hiring\u2014communication, teamwork, empathy\u2014which we know can\u2019t be replaced by an algorithm\u201d\u2014Christine Trodella, Head of Americas, Workplace<\/a>, Facebook<\/p>\n \u201cAs AI continues to advance, 2020 will see devices and apps offer even more personalized services; this will be made possible through an increased understanding of user behavior and search patterns, allowing organizations to have a more in-depth view of user preferences and therefore deliver more human-centric experiences in real-time\u201d\u2014Phani Nagarjuna, Chief Analytics Officer, Sutherland<\/a><\/p>\n \u201cIn the future, everyone is going to adopt some type of AI technology as it becomes more and more critical. But just because teams know how to build AI doesn\u2019t mean they know how to properly use it. You can build an algorithm for almost anything but that doesn\u2019t mean it\u2019s going to have utility in a business or have the ability to adapt new data. As complexity in software grows, understanding the difference between building AI and using it will be crucial as we head into 2020\u201d\u2014Jon Seaton, Director of Data Science,\u00a0Functionize<\/a><\/p>\n \u201cAI is revolutionizing the\u00a0buying and selling\u00a0of\u00a0complex B2B\u00a0services, traditionally left to analog processes,\u00a0by\u00a0understanding and responding to\u00a0the complexities of\u00a0human\u00a0intent. Machine and deep learning are making it possible for\u00a0users of complex B2B services\u00a0to\u00a0define\u00a0and\u00a0match complex requirements to ideal trading partners (suppliers)\u00a0through an intuitive, needs-identification process\u00a0and a vast understanding of potential trading partner strengths and capabilities.\u00a0User experience continues to improve as AI becomes better informed about individual preferences and company requirements with every interaction, especially intangible areas like organizational culture and values\u201d\u2014Keith Hausmann, Chief Revenue Officer, Globality<\/a><\/p>\n \u201c5G\u2019s exceptional bandwidth and the applications that it enables, such as connected vehicles, will push a lot of the machine learning computation and model serving to the Telco Edge. This will require application developers to consume CDN-like services but for data processing and model serving. We will also start to see a lot more interesting applications of AI techniques in other fields\u201d\u2014Alex Bordei, VP of Product and Engineering, Lentiq<\/a><\/p>\n \u00a0\u201cAI will be used to find new ways to learn more about consumers of products and services in order to expose them to the content most likely to be of interest to them. While Facebook is an early adopter of AI from a marketing perspective, other platforms are following. Applications such as these with the ability to react to data through AI, machine learning, and predictive analytics are equipped to provide data-driven solutions to customers\u2019 problems through more personalized support, process automation, and other features\u201d\u2014Dan Drechsel, Venture Capitalist, BIP Capital<\/a>\u00a0<\/p>\n \u201cWhile AI and machine learning will increase the amount of automation in the workplace, only a small percentage of jobs will be completely replaced. What we\u2019ll see instead is that humans will increasingly work side by side with machines. It\u2019s happened in the past\u2014from tractors on farms to spreadsheets in an office environment. The automation of mundane tasks or complicated analyses will help to enrich many job functions and allow individuals to focus more time and attention on the strategic needs of the business\u201d\u2014Julia Kanouse, CEO, Illinois Technology Association<\/a><\/p>\n \u201cAI has been hyped-up for the past few years, but now we’re starting to see the true capabilities\u2014and limitations\u2014of the technology. While AI can make predictions, it\u2019s missing the human element of adaptability to real-world scenarios. In general, the biggest missing link in AI today is lack of context and fluid domain expertise. To fill that void, in 2020 we will start to see increased utilization of contextual intelligence and the data needed to draw these insights such as user location, weather and more\u201d\u2014Dr. Hossein Rahnama, Founder and CEO, Flybits<\/a> <\/p>\n \u201cIn 2020, we\u2019ll see HR leaders leverage AI to better identify which employees may be on the verge of quitting. As HR data sets expand with new elements such as survey responses, employee peer to peer recognition and employee engagement levels in HR communication platforms, so will the quality of the algorithms in their ability to assess employee sentiment. For HR leaders looking to better determine the attitudes that drive employee turnover and increase retention, using AI to provide insights into employee engagement will be crucial\u201d\u2014Steve Beauchamp, CEO, Paylocity<\/a><\/p>\n \u201cIn 2020, AI will be used to help companies drive automation in their processes by doing work accurately enough to replace tasks previously completed only by humans. When AI is leveraged correctly, it can help recruiters identify areas where they can eliminate bias, such as within job descriptions. AI is not likely ready to make final hiring selections, but it will significantly speed up many steps that are completed by recruiters today\u201d\u2014Adam Godson, Chief Technology and Product Innovation Officer, Cielo<\/a><\/p>\n \u201cIn 2020, we expect AI to be used more frequently in the employment-based immigration space to provide attorneys, human resource leaders and foreign talent with a more seamless and less stressful immigration process. By integrating AI and technology into the immigration process, HR leaders will be armed with the resources, tools and data they need to sponsor foreign nationals and manage immigration programs with thoughtfulness, efficiency and continued compliance\u201d\u2014Richard Burke, CEO, Envoy Global\u00a0<\/a><\/p>\n![\"The](\"https:\/\/specials-images.forbesimg.com\/imageserve\/1185259096\/960x0.jpg?fit=scale\")
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