The AI industry could top $4 trillion by 2022. As well as being a feat of engineering and computing there are a significant amount of social and moral implications that need to be considered.
10 AI trends to watch in 2020
What’s happening in artificial intelligence in the year ahead? Look for modeling at the edge, new attention to data governance, and continued talent wars, among key AI trends
What’s happening in artificial intelligence in the year ahead? Look for modeling at the edge, new attention to data governance, and continued talent wars, among key AI trends
1. IT leaders will get real about measuring AI impact
Here’s a sobering stat: Fewer than two out of five companies reported business gains from AI in the past three years, according to the MIT AI survey. That will need to change in the new year, given the significant investment organizations are continuing to make in AI capabilities.
One way to achieve this is to change the way we measure results. Think reporting against things like ease of use, improved processes, and customer satisfaction.
Here’s a sobering stat: Fewer than two out of five companies reported business gains from AI in the past three years, according to the MIT AI survey. That will need to change in the new year, given the significant investment organizations are continuing to make in AI capabilities.
One way to achieve this is to change the way we measure results. Think reporting against things like ease of use, improved processes, and customer satisfaction.
2. Operationalization will be the name of the game
AI has the potential to become the new operating system for the enterprise. “Over the last decade, organizations have been picking up AI know-how and started working with the technology, but successfully putting models into production has remained a challenge,” Gagné says. “This year will be a tipping point for the infrastructure needed to support effective deployments, providing integrated learning environments and data ecosystems that support adaptive decision making by AI.”
AI has the potential to become the new operating system for the enterprise. “Over the last decade, organizations have been picking up AI know-how and started working with the technology, but successfully putting models into production has remained a challenge,” Gagné says. “This year will be a tipping point for the infrastructure needed to support effective deployments, providing integrated learning environments and data ecosystems that support adaptive decision making by AI.”
3. Data governance will get sexy
2020 will be about bringing AI into production, agrees Pat Ryan, executive vice president of enterprise architecture at SPR. But that will require IT to put in work with the chief data officer’s organization. The problem, says Forrester in its 2020 AI predictions report, is “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play along.”
Next year, the luster of AI and ML will wear off as companies realize it’s not magic, but math, says Ryan. “Organizations now also know the need for high-quality data as the foundation for AI/ML, so come 2020, we’ll see a heightened sense of appreciation and need for data governance, data analysts, data engineers, and machine learning engineers.”
The goal: creating a data pipeline capable of continuous curation to drive more successful AI projects. That’s why “firms with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and/or deep learning for their insights initiatives than those without CDOs,” Forrester says.
2020 will be about bringing AI into production, agrees Pat Ryan, executive vice president of enterprise architecture at SPR. But that will require IT to put in work with the chief data officer’s organization. The problem, says Forrester in its 2020 AI predictions report, is “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play along.”
Next year, the luster of AI and ML will wear off as companies realize it’s not magic, but math, says Ryan. “Organizations now also know the need for high-quality data as the foundation for AI/ML, so come 2020, we’ll see a heightened sense of appreciation and need for data governance, data analysts, data engineers, and machine learning engineers.”
The goal: creating a data pipeline capable of continuous curation to drive more successful AI projects. That’s why “firms with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and/or deep learning for their insights initiatives than those without CDOs,” Forrester says.
4. AI pros will shine
At the very top of LinkedIn’s top 15 emerging jobs in the U.S. for 2020 is the AI specialist. Hiring for artificial intelligence pros of various titles (including AI and ML engineers) has grown 74 percent annually over the last four years, according to LinkedIn. “Artificial intelligence and machine learning have both become synonymous with innovation, and our data shows that’s more than just buzz,” LinkedIn said, with especially hot markets in the San Francisco Bay Area, New York, Boston, Seattle, and Los Angeles.
At the very top of LinkedIn’s top 15 emerging jobs in the U.S. for 2020 is the AI specialist. Hiring for artificial intelligence pros of various titles (including AI and ML engineers) has grown 74 percent annually over the last four years, according to LinkedIn. “Artificial intelligence and machine learning have both become synonymous with innovation, and our data shows that’s more than just buzz,” LinkedIn said, with especially hot markets in the San Francisco Bay Area, New York, Boston, Seattle, and Los Angeles.
5. Data modeling will move to the edge
Expect a shift from cloud-only to cloud-edge hybrid strategies to enable machine learning (ML) in the next year. “Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations,” says Senthil Kumar, FogHorn’s vice president of software engineering. To date, many organizations have settled for smaller sample sizes or time-deferred data for their efforts, which can provide an incomplete or inaccurate picture.
Forrester predicts that edge cloud service market (infrastructure-as-a-service and advanced cloud-native programming services on distributed edge computing infrastructure) will grow by at least 50 percent in 2020. “By implementing edge-first solutions, organizations can synthesize data locally, identify machine learning inferences on core raw data sets, and deliver enhanced predictive capabilities,” Kumar says. “By running ‘edgified’ versions of ML models in real-time, organizations enable faster responses to real-time events and the ability to act, react, pro-act to events of interest at the source.”
Expect a shift from cloud-only to cloud-edge hybrid strategies to enable machine learning (ML) in the next year. “Being able to analyze high-fidelity, high-resolution, raw machine data in the cloud is often expensive and does not happen in real-time due to transport and ecosystem considerations,” says Senthil Kumar, FogHorn’s vice president of software engineering. To date, many organizations have settled for smaller sample sizes or time-deferred data for their efforts, which can provide an incomplete or inaccurate picture.
Forrester predicts that edge cloud service market (infrastructure-as-a-service and advanced cloud-native programming services on distributed edge computing infrastructure) will grow by at least 50 percent in 2020. “By implementing edge-first solutions, organizations can synthesize data locally, identify machine learning inferences on core raw data sets, and deliver enhanced predictive capabilities,” Kumar says. “By running ‘edgified’ versions of ML models in real-time, organizations enable faster responses to real-time events and the ability to act, react, pro-act to events of interest at the source.”
6. AI will come for B2B
B2B sales and services’ complexity stands to benefit more from AI than the consumer corollaries. “Machine and deep learning are making it possible for users of complex B2B services to define and match complex requirements to ideal trading partners through an intuitive, needs-identification process and a vast understanding of potential trading partner strengths and capabilities,” says Keith Hausmann, chief revenue officer at Globality. “User 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.”
B2B sales and services’ complexity stands to benefit more from AI than the consumer corollaries. “Machine and deep learning are making it possible for users of complex B2B services to define and match complex requirements to ideal trading partners through an intuitive, needs-identification process and a vast understanding of potential trading partner strengths and capabilities,” says Keith Hausmann, chief revenue officer at Globality. “User 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.”
7. Man and machine converge in contact centers
“The consumer push to get faster service across a growing number of digital channels has challenged contact center teams, causing team leaders to solve for long wait times, clumsy customer journeys, and overwhelmed agents,” says Korte of TetraVX. AI can complement agents, enabling them to better provide timely or informed responses across channels.
“As with any new technology implementation, AI in the contact center comes with its own challenges,” Korte says. “It’s important that organizations keep their customer service experiences human, ensuring that customer journeys don’t appear ‘too automated’ from the outside looking in." But beware: Standalone conversational AI could take a hit next year.
Forrester points out that brands have embraced chatbots to reduce the cost of customer service, but “overly ambitious projects fail to resolve customers’ issues or answer their questions. Despite the maturation of the toolsets — including the expansion of prebuilt and vertical-specific intent libraries and higher-power natural language understanding (NLU) engines — by the end of 2020, conversational AI will still power less than 20 percent of successful customer service interactions.”
“The consumer push to get faster service across a growing number of digital channels has challenged contact center teams, causing team leaders to solve for long wait times, clumsy customer journeys, and overwhelmed agents,” says Korte of TetraVX. AI can complement agents, enabling them to better provide timely or informed responses across channels.
“As with any new technology implementation, AI in the contact center comes with its own challenges,” Korte says. “It’s important that organizations keep their customer service experiences human, ensuring that customer journeys don’t appear ‘too automated’ from the outside looking in." But beware: Standalone conversational AI could take a hit next year.
Forrester points out that brands have embraced chatbots to reduce the cost of customer service, but “overly ambitious projects fail to resolve customers’ issues or answer their questions. Despite the maturation of the toolsets — including the expansion of prebuilt and vertical-specific intent libraries and higher-power natural language understanding (NLU) engines — by the end of 2020, conversational AI will still power less than 20 percent of successful customer service interactions.”
8. Automation could go into overdrive
Add a new word to your vocabulary for 2020: Hyperautomation, meaning the application of advanced technologies like AI and ML to automate processes and augment humans across a range of tools and at a higher level of sophistication. Gartner named hyperautomation one of its top ten strategic technology trends in 2020.
The goal, says Gartner, is more AI-driven decision-making, with many organizations creating a digital twin of themselves, which enables them to “visualize how functions, processes, and key performance indicators interact to drive value.”
Add a new word to your vocabulary for 2020: Hyperautomation, meaning the application of advanced technologies like AI and ML to automate processes and augment humans across a range of tools and at a higher level of sophistication. Gartner named hyperautomation one of its top ten strategic technology trends in 2020.
The goal, says Gartner, is more AI-driven decision-making, with many organizations creating a digital twin of themselves, which enables them to “visualize how functions, processes, and key performance indicators interact to drive value.”
9. Heterogenous architectures will emerge
Today, AI-enabled applications and networks rely on different processing architectures. That’s likely to change in 2020, according to ABI Research’s 54 Technology Trends to Watch. “The next generation and AI and ML frameworks will be multimodal by their nature and may require heterogeneous computing resources for their operations,” predict ABI Research analysts, noting the leading chip makers will move away from proprietary software stacks and begin to adopt open Software Development Kits (SDKs) and Application Programming Interface (API) approaches to their tools.
Today, AI-enabled applications and networks rely on different processing architectures. That’s likely to change in 2020, according to ABI Research’s 54 Technology Trends to Watch. “The next generation and AI and ML frameworks will be multimodal by their nature and may require heterogeneous computing resources for their operations,” predict ABI Research analysts, noting the leading chip makers will move away from proprietary software stacks and begin to adopt open Software Development Kits (SDKs) and Application Programming Interface (API) approaches to their tools.
10. AI mistakes will be made
AI can perpetuate discrimination, and “the spread of deepfakes, misuse of facial recognition, and overuse of personalization can harm, offend, or creep out customers and employees,” Forrester analysts write. In the end, these missteps will serve a larger purpose: highlighting the importance of responsible AI development and deployment.
AI can perpetuate discrimination, and “the spread of deepfakes, misuse of facial recognition, and overuse of personalization can harm, offend, or creep out customers and employees,” Forrester analysts write. In the end, these missteps will serve a larger purpose: highlighting the importance of responsible AI development and deployment.