Predictions 2019: How AI, Machine Learning Continue to Impact Us

PREDICTIONS 2019: With the new year only weeks away, we present some ideas from various industry executives about what new impacts they believe AI and machine learning will be making on the IT business and our lives in general.

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We keep pounding away at this point here in eWEEK, and we'll do it again today: We are at an important convergence of technology here in this, the first quadrant of the 21st century.

It's all available now: high-connectivity bandwidths, super high-quality code and code libraries, unprecedentedly powerful processors that use less power than previous models, unlimited storage capacities, ingeniously designed mobile and stationary connected devices, a zillion types of cloud services—we could go on. What is next?

We're already seeing it: the introduction of much greater functionality through artificial intelligence, which is backed by machine learning. We're seeing more AI in more applications than we've ever seen before: retail, cars, office productivity, military, health care, home entertainment—the list is lengthy.

As the new year confronts us, we present some ideas from various industry executives about what new and continued impacts they believe AI and machine learning will be making on the IT business and our lives in general.

(Editor’s note: We have a lot of material in this category, so you can expect to see a follow-up on this topic soon.)

Here are a slew of cogent predictions regarding AI and machine learning in 2019:

Tom Wilde, CEO and Founder, Indico:
AI is no longer “what”—it’s “how.” “Companies are looking for business solutions—aimed at improving the customer experience, accelerating cycle time, increasing business efficiency, and expanding capacity and productivity. Expect to see fewer AI-only solutions coming to market, and fewer pure-AI startups being funded.”  

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.”

The rise of explainable AI. “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. This will also make it easier for data science and SMEs to collaborate on improving AI’s contribution to the business.”

More focus on mid/back-office applications/use cases. “A lot of the attention in AI to date has been on the front-office applications—those involving customer service interactions via bots. As companies look for ways to drive more profitable growth, they are looking at more opportunities to use AI and machine learning in their back-office operations—especially those manual, document-based workflows that drive many of their core business processes.” 

Vitaly Gordon, VP Data Science, Einstein, Salesforce:
AI is entering the Age of Commodity. “You do not need to know how the technology of a microwave works in order to use it; it is simply a tool. With the huge influx of no-code, point-and-click tools, we are entering into the same phase with AI where it will become a widely used utility by everyone, regardless of technical background. As a result, most of the AI applications in the coming years will be built by people with little or no AI training.”

Christian Beedgen, CTO, Sumo Logic:
Ethical intelligence >> artificial intelligence. “Our fascination with the use of computing power to augment human decision-making has likely outgrown even the tremendous advances made in algorithmic approaches. In reality, the successful use of AI and related techniques is still limited to areas around image recognition and natural language understanding, where input/output scenarios can be reasonably constructed, and that will not change drastically in 2019. The idea that any business can ‘turn on AI’ to become successful or more successful is preposterous, no matter how much data is being collected. But the collection of data to support humans and algorithms continues and raises important ethical questions and is something we need to pay close attention to over the next few years.”

Nikki Baird, Vice President of Retail Innovation, Aptos:
Retail breaks out of the AI black box: “First-generation AI solutions were simple—data in, answer out. Solutions were designed to protect the average end user from confusion and distraction. While black-box solutions serve their purpose, they also limit the value organizations can extrapolate by hiding AI logic, which in theory could be used to teach humans what was learned that led to various recommendations.  

“In 2019, we’ll see more organizations move to glass-box AI, which exposes the connections that the technology makes between various data points. For instance, glass-box AI not only tells you there is a new retail opportunity, it also uncovers how that opportunity was identified in the data. It also provides retailers with an opportunity to check their data—and any public or aggregate data they pull in—to ensure AI isn’t making bad assumptions under the adage ‘garbage in, garbage out.’”

Michael Wu, Ph.D., Chief AI Strategist, PROS:
AI will make the IoT smarter. “The IoT is not very smart today—we still need to program most of it with massive lists of ‘IF-THEN’ statements (e.g.: If I say ‘good night,’ THEN lock all doors, arm security, set thermostat to 65F, AND set alarm for 7 am tomorrow). In other words, our commands to IoT devices will need to be learned implicitly through human behavior, rather than explicitly programmed. AI will help unlock the transformative value of intelligent automation by collectively learning from all data recorded across multiple devices. Affordable sensors in IoT are creating vast quantities of data, which can be consumed by AI to learn how individuals typically program these devices, and eventually learn to personalize them to one’s particular lifestyle. This will make IoT devices that impact our everyday lives, such as our thermostats, locks or refrigerators, even smarter.”

Ravi Mayuram, CTO, Couchbase:
The groundwork has been laid for AI/ML technologies, and now the real questions will surface. Over the past year, companies have been figuring out where and how to implement AI/ML technologies, and many are still refining the “how.” While that’s true, the groundwork has been laid and the mentalities have been shifted, and 2019 will be a big year for questions in AI/ML—literally, in the sense of how organizations determine what questions to use to train their AI/ML algorithms. There are also broader conversations that have been sparked around ethics and biases, and 2019 will see the conversation continue, with academia and business working together to develop a trusted approach to developing AI/ML for the future.

Data gets a makeover to support AI/ML algorithms. “Today, data remains a difficult part of AI and is a barrier to effective training methods and truly trusted outcomes. Data quality and availability can vary wildly within an organization, and it can take time to determine what data is clean, up-to-date and trustworthy. 2019 will see data systems come under greater scrutiny within the enterprise as data grows in value, and we’ll see efforts to address data quality across the board to better leverage AI/ML technologies.”

Richard Rovner, Vice President, MathWorks:
Industrial applications are becoming a major consumer of AI but also bring new demands for specialization. “Smart cities, predictive maintenance, Industry 4.0, and other IoT and AI led applications demand a set of criteria be met as these move from visionary concepts to reality. Examples include safety critical applications that need increased reliability and verifiability, low-power, mass-produced and moving systems that require form factors, and advanced mechatronics design approaches that integrate mechanical, electrical and other components. A further challenge is these specialized applications are often developed and managed by decentralized development and service teams (not centralized under IT). Example applications range from agricultural equipment using AI for smart spraying and weed detection to overheating detection on aircraft engines.”

Carl Schmidt, CTO and co-Founder, Unbounce:
On analytics, attribution and AI-powered technology: "In 2019, I expect we'll see AI-powered attribution technology starting to hit its stride. In today’s digital environment, attribution continues to be a challenge—businesses are still piecing together data points from different platforms and many are still struggling to understand the full path to purchase—which marketing channels are driving revenue? What kinds of content help retain customers and at which stage of the customer journey? Where are customers falling out of the funnel? AI can sequence that customer journey together and identify when a customer comes to a company's site and leaves without converting. It's the businesses that adopt AI-powered attribution technology in 2019 that will have a leg up on the competition."

On conversational AI. "This year, conversational AI will challenge us to rethink what impact marketing can have on a business. Through conversational AI, we now have an unprecedented ability to learn about our customers and prospects, experiment with targeted messaging faster than ever before. More traditionally minded marketers will use it to automate knowledgebase lookups and provide a simplified interface to canned responses. Cutting-edge digital marketers will embrace the learning potential and use it to more deeply understand the needs of their audience. In addition to the obvious partnership with sales, truly savvy marketers will partner with their customer success, product management and user experience peers to maximize the impact of conversational AI will have on the business."

eWEEK is continuing this series of prediction articles until well into January.

Chris Preimesberger

Chris J. Preimesberger

Chris J. Preimesberger is Editor-in-Chief of eWEEK and responsible for all the publication's coverage. In his 15 years and more than 4,000 articles at eWEEK, he has distinguished himself in reporting...