AI/ML 2019 Predictions (Part 4)
AI/ML 2019 Predictions (Part 4)
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Given the speed with which technology is evolving, we thought it would be interesting to ask IT executives to share their predictions for 2019. Here are more predictions for Artificial Intelligence (AI), Machine Learning (ML), and other aspects of data science:
Fears will be eased as we come to realize that we’re nowhere near replacing humans in abstract disciplines such as driving with autonomous vehicles, threat hunting in security, and other analytical roles that still require higher-level thinking. Companies predicating their success on this will start to get pushback, either financially as they find it more challenging to raise funds and maintain investor enthusiasm, or by way of unhappy customers that have been over promised and underwhelmed.
ML algorithms will play a big part in the latter half of 2019, especially when it comes to remediation and predictions of multi-cloud failures.
AI and ML continue to make great strides in integration with business workflows and ease of use. In 2019 I expect this trend continue and I would not be surprised to see AI being actively added in industries such as healthcare, recruiting, and finance. I do not think education is quite there yet for large-scale integrations.
ML is one of the most hyped terms of the year, but its disruptive effects are being felt across retail, finance, automotive and throughout industries. A confluence of affordable storage, processing power, GPU-optimizations and widely available open-sourced algorithms, as well as deep datasets for training, has improved the available technology dramatically. In 2019, the variety of data science languages will continue to grow. But there is also a clear trend that Python becomes the technology leader, including the Python-based Deep Learning library TensorFlow.
In 2018, we’ve seen increased investments in AI, which isn’t necessarily surprising. In fact, 80 percent of businesses think that AI can make an immediate impact once implemented into the workplace, and half of organizations have a clear definition of how AI and its employees could best work together.
However, what is surprising is that there is still room to improve when it comes to leadership involvement in AI investment decisions. Less than half (48 percent) of CEOs are involved in conversations about AI, and only 24 percent of CTOs are included in these discussions. But having them involved is key since these executives oversee major parts of the business and have the best idea of where AI can make the biggest impact for both employees and customers.
We can expect that AI will become more sophisticated in 2019. Beyond improving customer experience, I believe we’ll see AI implemented internally to enable profound organizational transformations, like what we are doing at Globant — building stellar teams based on employee data. Just like how AI can be used to organize complex customer data, it can be used to analyze employees’ soft and hard skills, and then help managers create unique opportunities for personal and professional growth, build teams based on complementary employee strengths and encourage cross-team collaboration. This allows managers to focus more on ensuring that employees are strong cultural fits that will work well together.
What will be the AI story for 2019? The long-promised enterprise AI transformation is poised to begin in earnest in 2019. Most enterprises have reached a point of digital maturity, ensuring access to quality data at scale. With mature data sets, AI providers can offer lower cost, easier to use AI tools for specific business use cases. The effect of enterprise AI at scale will be significant. Gartner expects the business value of AI to hit nearly $3.9 trillion in by 2022. Consumers also stand to benefit in almost every sector. They’ll see more innovative products and services, smarter homes, factories and cities, improved health, and a higher quality of life.
What do these technology changes mean for the future of work and the intelligent enterprise? AI will augment — not replace — the workforce. AI applications will be transformational, improving efficiency and performance, generating huge cost savings, and giving rise to more innovative products and services. However, the future of work will involve humans and AI. The most innovative companies have already started planning how to best make this symbiotic future a reality. In the shorter term — this means the gap in skilled workers in data science will persist and demand will remain very high. Educational opportunities will expand to help address the need. In the long term — as we develop the skills and technology to unlock AI — society stands to benefit enormously from AI-augmented workers, homes, vehicles, power grids, factories, cities, more.
What’s the single most important trend shaping the way enterprises create, consume and compute information? Today, organizations are digitizing business processes, including core capabilities, in their entirety. Managing the resulting information, and being able to maximize its value, will be the key differentiator for the Intelligent and Connected Enterprise. Technologies including the cloud, AI and ML, edge computing and more are all part of the ongoing digital transformation of the enterprise.
What are the positives and negatives of this trend? When processes and data are fully integrated, analytics and AI can be applied to analyze information, deliver insights, and apply smart automation. Machine-enhanced decision-making, or augmented intelligence, can be rooted in a better understanding of transactions and interactions, that then helps improve decisions and outcomes. Of course, with digitization comes concerns, especially related to security — which must be job number one for vendors and the enterprise.
Are organizations ready to embrace it or does more work need to be done? Technology adoption is like evolution, it never stops. But at the enterprise level, organizations are making big steps into the cloud, experimenting with AI and ML use-cases, and investing in infrastructure to enable information management at the unprecedented scale we are going to see.
Will it change the way information is managed? Yes and no. Even though digital information is evolving at a rapid pace, the world is still document-centric. However, how they are created, their levels of complexity and their collaborative nature is changing rapidly. What will certainly continue to evolve is how the Intelligent and Connected Enterprise engages with information in meaningful ways to uncover insights.
Breaking down barriers; the balance between AI and human fear — Whether we realize it or not, our reliance on AI is more alive than ever before, and in 2019 companies will make a concerted effort to further understand the limitations of AI and analyze where human intervention is required, while simultaneously discovering ways for AI to respond to more nuanced human behavior
Growing acceptance of AI as the first line of customer experience — Consumers will become more accepting of AI-powered chatbots as the first line of a customer experience, and more companies will adopt them in an effort to create an ultra-personalized and convenient experience
AI takes customer-centric marketing to new heights — With companies of every size shifting toward AI-powered technologies, enhanced trend analysis through AI will reach unprecedented levels of value to help companies evaluate how to optimize their marketing efforts as part of the data-driven rise of the CMO
Intent-based AI overhauls the service desk — 2018 was the year of bots, and over the next year we’ll see pervasive analytics and intent-based AI take this a leap further, highlighting the importance of specialized service desks that streamline IT support management and allow for instant knowledge delivery
Machine learning goes for maximum value — Data is growing exponentially but the ability to access that data is not practical for a good ML algorithm — over the coming year, a major challenge will be evolving algorithms to produce the maximum value of data that applies to your specific need
While AI and ML have long been in the marketing hype phase, these developing technologies will start to show actual production use case applications in the new year. Any AI or ML application that includes large amounts of data for crunching and analysis will undoubtedly enable businesses to slash human work hours and provide a quicker, more accurate read of the data and situation. AI and ML will help IT organizations become more proactive and more secure. It will also provide invaluable predictive insights to find out what might go wrong with the business and uncover hidden security vulnerabilities.
As more companies attempt to take AI out of the lab and into production, we’ll see growing adoption of tools to manage development lifecycle. AI has a unique dual-phase development model and current CI/CD toolchain doesn’t address the unique challenges in terms of training, repeatability and data management.
Many companies realize that they can get many AI/ML benefits with simpler tools — rules engines and simple recommender systems for instance. I expect to see growing adoption of those, either as a stepping stone into the fully autonomous world or as a good-enough solution for many industries.
We’ll see many data engineering tools get rebranded as AI/ML data pipeline tools. They are mostly the same as the usual data engineering tools, but with larger budgets. I expect a real AI-focused data pipeline to handle the flow of both data and models between training and production, especially with handling for feedback loops and model improvements. But we’ll surely see many more vendors just change terminology, as it is faster and cheaper than actually adding much-needed capabilities.
Automation of processes will have more impact than AI. AI and ML are overhyped for many real-life applications, including the contact center industry. For example, instead of trying to identify specific patterns in images or data (an AI/ML sweet spot), it will be much more useful to increase the volume of satisfying self-service support sessions through intelligently applied automation to resolve common questions and provide guided user flows through defined business processes. By leveraging human intelligence primarily for those support scenarios that can’t be effectively automated, call center operations will be further optimized.
The commercial application of AI and ML will increase in multimedia — e.g. television, movies, music, games. For example, the face modeling experiment made by Unreal Engine team with Andy Serkis was amazingly realistic. There have been many experiments around applying ML techniques to teach virtual humanoid models to walk gracefully on complex surfaces like stairways, stones etc and make other complex actions. The adoption of AI/ML will slowly end the era of scripted animations.
An AI and ML arms race will take place between the fake news creators and those fighting to stifle it. Startups will use AI/ML to identify and stop the spread of fake news. Other startups and politically motivated entities will use AI/ML to create and spread Fake News that is increasingly difficult to detect by humans and AI.
AI/ML will be used to identify flaws in engineering and development processes. Its application will empirically discover gaps in a team’s agile methodologies, and suggest the best way a team can achieve the highest performance using predictive analytics.
AI as a workload is going to become the primary driver for IT strategy. AI represents a transformational development for the IT industry: customers across all verticals are increasingly focusing on intelligent applications to enable their business with AI. This applies to any workflow implemented in software - not only across the traditional business side of enterprises, but also in research, production processes, and increasingly the products themselves. The improved scale of automation achievable with AI will quickly become critical for a company’s competitiveness building and will make AI a strategy-defining technology.
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