It looks like it will be an active year on the AI front, with a number of relevant trends that are unfolding and that IT leaders should follow:
1. IT leaders will really start measuring the AI impact
Here's a sobering situation: Less than two of the five companies reported business benefits from AI over the past three years, according to the MIT AI survey. That will have to change in the new year, given the significant investments that organizations continue to make in AI capabilities.
One way to achieve this is by changing the way we measure the results. This includes reporting on issues such as ease of use, improved processes and customer satisfaction. “CIOs will also have to continue to use more of their budgets to understand how AI can help their organizations and implement solutions that deliver real ROI,” said Jean-François Gagné, CEO and co-founder of Element AI software vendor. , “or run the risk of falling behind the competitors.”
2. Operationalization will be the name of the game
AI has the potential to become the new operating system for the enterprise. “For the past ten years, organizations have picked up AI know-how and started working with the technology, but successfully putting models into production has remained a challenge,” says Gagné. “This year will be a tipping point for the infrastructure needed to support effective implementations, by providing integrated learning environments and data ecosystems that support adaptive decision making through AI.”
3.Data governance becomes sexy
2020 is all about putting AI into production, agrees with Pat Ryan, executive vice president of corporate architecture at SPR. But then IT has to get started with the organization of the chief data officer. The problem, Forrester says in his 2020 AI prediction report, is “sourcing data from a complex portfolio of applications and convincing various data gatekeepers to play with.”
AI is not magic, but math. You need a strong data pipeline.
Next year the shine of AI and ML will wear off as companies realize that it is not magic, but math, says Ryan. “Organizations now also know the need for high-quality data as the basis for AI / ML, so in 2020 we will see an increased sense of appreciation and need for data governance, data analysts, data engineers and machine learners.”
The goal: to create a data pipeline that is capable of continuous curation to manage more successful AI projects. That is why “companies with chief data officers (CDOs) are already about 1.5 times more likely to use AI, ML, and / or deep learning for their insight initiatives than companies without CDOs” , says Forrester.
4. AI professionals will shine
The AI specialist is at the top of LinkedIn's 15 emerging jobs in the US before 2020. According to LinkedIn, hiring artificial intelligence professionals (including AI and ML technicians) has grown by 74 percent per year over the past four years. “Artificial intelligence and machine learning have both become synonymous with innovation, and our data shows that it's more than just buzz,” said LinkedIn, with hot markets in the San Francisco Bay Area, New York, Boston, Seattle, in particular. and Los Angeles.
5. The modeling of data will move on the edge
Expect a shift from cloud-only to cloud-edge hybrid strategies to enable machine learning (ML) in the coming 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 transportation and ecosystem considerations,” said Senthil Kumar, FogHorn's vice president of software engineering . So far, many organizations have settled for smaller efforts with smaller sample sizes or time-dependent data, which can produce an incomplete or inaccurate picture.
Forrester predicts that the 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 conferences based on raw data sets and provide improved predictive capabilities,” says Kumar. “By executing ” edgified “versions of ML models in real-time, organizations can respond more quickly to real-time events and can act, respond and proactively respond to events that are of interest to the resource. ”
6. AI comes for B2B
The complexity of B2B sales and services benefits more to AI than to the consumer crown. “Through an intuitive, needs identification process and a comprehensive understanding of the strengths and capabilities of potential trading partners, users of complex B2B services can define complex requirements and match them with ideal trading partners,” said Keith Hausmann, chief revenue officer at Globality. “The user experience gets better as AI becomes better aware of individual preferences and business needs in every interaction, especially in intangible areas such as organizational culture and values.”
7. Man and machine come together in contact centers
“Consumers' commitment to faster service through a growing number of digital channels has challenged contact center teams, meaning team leaders have had to solve long waiting times, awkward customer journeys, and overwhelming agents,” said TetraVX Korte. AI can supplement agents, making them better able to respond to the different channels in a timely or informed manner.
“As with any new technology implementation, AI brings its own challenges to the contact center,” says Korte. “It is important that organizations keep their customer service experiences human and ensure that customer journeys do not appear to be” too automated “from the outside.” But beware: Standalone conversation AI may get a blow next year.
Companies have had mixed results with chat service customer service projects.
Forrester points out that brands have embraced chatbots to reduce customer service costs, but overly ambitious projects fail to solve customer problems or answer their questions. Despite the maturation of tool sets – including of the extension of pre-built and vertically-specific intention libraries and higher capacity natural language (NLU) engines – by the end of 2020, conversation AI will still drive less than 20 percent of successful customer service interactions “.
8. Automation can go into overdrive
Add a new word to your vocabulary for 2020: Hyper-automation, that is, the application of advanced technologies such as AI and ML to automate processes and increase humanity in a range of tools and at a higher level of sophistication. Gartner called hyperautomation one of his top ten strategic technology trends in 2020.
The goal, says Gartner, is more AI-driven decision-making, with many organizations creating digital twins of their own, enabling them to “visualize how functions, processes, and key performance indicators interact to create value” “.
9. Heterogeneous architectures will arise
Today, AI-capable applications and networks are dependent on different processing architectures. That is likely to change in 2020, according to 54 Technology Trends to Watch from ABI Research. “The next generation and the AI and ML frameworks will naturally be multimodal and may need heterogeneous computing tools for their activities,” predict ABI Research analysts, noting that leading chip manufacturers will move away from proprietary software stacks and open Software Development Kits (SDKs) and Application Programming Interface (APIs) will start using their tools.
10.AI errors will be made
As Forrester indicates, AI is not perfect; it can maintain discrimination and bias. The analyst firm anticipates some high-profile PR disasters as a result, which may harm some companies, but will ultimately not shame confidence in AI.Tags: #ArtificialIntelligence, #latestNewsAI, #researchAi, #Robotics, AI, Kunstmatige intelligentie, machine learning