AI and Machine Learning: 9 Predictions for 2019

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In 2018, there was heightened awareness of artificial intelligence and machine learning and, in 2019, the emphasis is expected to increase on cognitive computing technologies that can analyze data in ways previously unimaginable and act independently or with limited human intervention. Here, executives of leading IT companies offer 9 predictions for what's ahead in 2019.

1-Machine learning projects will move from science projects and innovation labs to full production led by industry disruptors in 2019: Virtually every company has machine learning projects, but most of them are reliant on specialty platforms that cannot access all of the data relevant to business objectives. All of the data is stored in a variety of data warehouses and data lakes, none of which have the ability to run end-to-end machine learning, forcing data movement to the specialty platforms. Only a subset of data is then used to train and score ML models, resulting in limited accuracy.  In 2019, current industry disruptors and smart traditional companies will bring machine learning to all its data, instead of moving its data to the ML platforms. These companies will more accurately predict and influence outcomes, including predictive maintenance on medical devices, predictive revenue based on personalized customer behavior analytics, and proactive fraud detection and prevention. — Joy King, Micro Focus VP Vertica product marketing

2-Advanced analytics and artificial intelligence will be everywhere and in everything, including infrastructure operations: Advanced analytics and AI will continue becoming more highly focused and purpose-built for specific needs, and these capabilities will increasingly be embedded in management tools. This much-anticipated capability will simplify IT operations, improve infrastructure and application robustness, and lower overall costs. Along with this trend, AI and analytics will become embedded in high availability and disaster recovery solutions, as well as cloud service provider offerings to improve service levels. With the ability to quickly, automatically and accurately understand issues and diagnose problems across complex configurations, the reliability, and thus the availability, of critical services delivered from the cloud will vastly improve. —Jerry Melnick, president and CEO, SIOS Technology

3-AIOps and the “right type” of monitoring: AI is making huge strides in monitoring things like signals that are natural to humans (images/video/speech). However, in comparison, the killer applications within IT have not emerged yet because no company knows yet how to prepare the "right type" of signals and the related feedback to allow for machine learning, and produce a strong meaningful application supporting IT management. Within the next 3 years, companies will figure out the right mix of signals and feedback for machine learning and will create a breakthrough in monitoring strategies. The first to leverage this new strategy after gathering the right data will have the key advantage in the market. Ultimately, these tools will increase team efficiencies by enabling teams, which used to require experts, to operate through generalists providing considerable customer value. —Gadi Oren, VP of products, LogicMonitor

4-AI-augmented analytics will be mainstream: 2019 will be the year when AI-led analytics (known as automated discovery) will become mainstream. Human brains are not wired to evaluate millions of data combinations at sub second speeds, but machine learning is literally built for this problem and the perfect solution. Business leaders and data analysts are better understanding that AI is not going to replace jobs, but augment them, and I expect that in the next year, the majority of data analysts will have the power of data science at their fingertips without the need to write code.— Ketan Karkhanis, SVP and GM, analytics, salesforce

5-AI/data science meets the line of business: One of AI’s biggest obstacles has been the disconnect between data science teams and subject matter experts (SMEs) in the business. SMEs play a critical role but the complexity of the underlying tech typically requires a lot of data science expertise. Enterprises will put increasing pressure on their teams to close this gap so that they can get more value from their AI initiatives. In addition, as AI becomes embedded in more and more processes, there is an increasing need for transparency in how it works and makes decisions on our behalf. Users will demand real-world, plain English examples and explanations for full transparency and “explainability.” This will also make it easier for data science and SMEs to collaborate on improving AI’s contribution to the business. — Tom Wilde, CEO and founder, Indico

6–AI connects Employers with their Employees: While companies continue to spend money on AI technologies that improve the customer experience, in 2019, we’ll start to see companies delivering similarly compelling experiences for their employees. Conversational AI and development bot platforms are a few examples of technologies that businesses will use to help current and new employees succeed.  — Steve Hamrick, VP of product management, SAP Jam 

7-AI is revolutionizing B2B sales and marketing, but enterprises will struggle in their adoption. Using AI for revenue optimization (ABM, forecasting, and lead scoring, etc.) is slow going due to weaknesses in internal customer databases. Limited and inaccurate data will be one barrier, and immature AI technology the second barrier. Traction will come when companies take command of their first-party data, clean and augment it with third-party data. — Umberto Milletti, an entrepreneur and sales and marketing leader, InsideView

8-AI and Object Storage Play Tag: As businesses increase their use of AI to extract greater value from their digital assets, metadata tagging will become an even more critical element of enterprise storage. This will bring more attention to object storage, which is centered on metadata, and the key will be integrating well with AI tools. — Jon Toor, CMO, Cloudian

9-As AI and ML become mainstream, a new breed of security data scientists will emerge in 2019: AI and ML techniques are data dependent. Preparing, processing, and interpreting data require data scientists to be polymath. They need to know computer science, data science, and above all, need to have domain expertise to be able to tell bad data from good data and bad results from good results. What we have already begun seeing is the need for security experts who understand data science and computer science to be able to first make sense of the security data available to us today. Once this data is prepared, processed and interpreted, it can then be used by AI and ML techniques to automate security in real time. — Setu Kulkarni, vice president of corporate strategy, WhiteHat Security