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The 2020 outlook for artificial intelligence

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While 9 out of 10 respondents to the 2019 MIT Sloan Management Review and Boston Consulting Group (BCG) Artificial Intelligence Global Executive Study and Research Report agree that AI represents a business opportunity for their company, fewer than 2 out of 5 report business gains from AI in the past three years.

According to the report, “early AI winners are focused on organization-wide alignment, investment, and integration.”

Forrester’s AI predictions for 2020 focus on this being the year when “companies become laser-focused on AI value, leap out of experimentation mode, and ground themselves in reality to accelerate adoption,” explains VP, Research Director Srividya Sridharan.

As we look to the year ahead, “CIOs will need to better assess the value of their AI bets and prove that ROI to the business,” explains TetraVX Director of Product Management Kara Longo Korte, to business and tech reporter Stephanie Overby in The Enterprisers Project.

And while this promises to be an active year for AI investment, Overby outlines the 10 biggest AI trends to watch for 2020:

Measuring AI impact

As mentioned above, fewer than two in five companies report business gains from AI in the last three years. But as AI investment increases, this needs to change — and it can be done by altering how we measure results. “Think reporting against things like ease of use, improved processes, and customer satisfaction,” writes Overby.

Think Operationalization

“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,” says Jean-François Gagné, CEO and co-founder of software provider Element AI.

Data pipelines

“Next year, the luster of AI and ML will wear off as companies realize it’s not magic, but math,” explains Pat Ryan, executive VP of enterprise architecture at SPR. With high-quality data as a foundation for AI/ML, 2020 will see a “heightened sense of appreciation and need” for everything-data — governance, analysts, engineers, and ML engineers — with a goal of creating a pipeline for continuous data that’ll drive more successful AI projects.

AI innovators in high demand

At 74% annual growth, AI Specialist is #1 on LinkedIn’s top 15 emerging jobs for the US in 2020. “[AI and ML] have both become synonymous with innovation, and our data shows that’s more than just buzz,” says the report.

Data modeling moves to the edge

As Overby explains, “expect a shift from cloud-only to cloud-edge hybrid strategies to enable machine learning (ML) in the next year.” Forrester is predicting that edge cloud service market 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,” says Senthil Kumar, VP, Software Engineering for FogHorn.

The B2B benefits of AI

“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.

Human and machine work together

AI can work as a complement to contact service centre agents and teams, providing better/more timely informed responses. The challenge? “It’s important that organizations keep their customer service experiences human,” minimizing a potentially ‘too automated’ look. (The question can then be asked: When will standalone conversational AI emerge?)

Hyperautomation

One of Gartner’s top 10 strategic technology trends for 2020? Hyperautomation — i.e. “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.” The goal? “More AI-driven decision-making,” explains Gartner.

Heterogenous architectures will emerge

“Today, AI-enabled applications and networks rely on different processing architectures,” writes Overby. But according to ABI Research’s 54 Technology Trends to Watch, that’ll change in 2020. “AI and ML frameworks will be multimodal by their nature and may require heterogeneous computing resources for their operations.”

Mistakes happen

AI is, of course, not perfect. As a final prediction, it isn’t hard to imagine that high-profile mistakes can be anticipated in 2020, but overall trust in AI will not erode. From deep fakes to the misuse of facial recognition, AI has the potential to perpetuate discrimination and cause harm, offence, and general uneasiness. Ultimately? It comes down to the importance of responsible use.

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How to accelerate business change using data and AI

AI poses as many challenges as opportunities. Here are a few common characteristics we’ve seen among businesses that have realized success with data and AI.

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By Bret Greenstein

Most senior leaders would agree that adapting in real time to customer and market needs is vital for achieving their visions and goals. And to develop this capability, they’d also likely agree they need data and artificial intelligence (AI).

The examples are all around us. The city of Chicago uses 12 variables, including high daily temperatures, to prioritize which of the city’s 7,000 “high-risk” restaurants it should send its 35 food inspectors to. The AI solution found violations a week earlier than they otherwise would have.

We helped an insurer create an AI system that provides underwriters with more precise estimates of risk, as well as the likelihood applicants will accept a specific price quote. The insurer can adjust how the AI environment makes its approval and pricing recommendations to ensure compliance with changing corporate priorities around risk vs. revenue.

We’ve also seen AI help predict customers’ emerging needs as markets change. If the economy goes into a recession, informed analysis could help organizations not only cut costs but also provide the products and services customers might need in a downturn.

And, of course, there are the businesses that have used data and AI to create new revenue streams and business models that drive lasting competitive advantage. For game changers like Uber, data is at the core of the company and constitutes value, not cost.

Facing up to AI realities

But there’s another hard fact that would draw consensus from many business leaders today: the unprecedented effort required to move an enterprise in an AI direction. The fact is, AI-enabled business change requires as much alteration to corporate culture, organizational structures and processes, and workforce roles and skills as it does new technology.

For example, too many organizations still treat data as an expense and a security risk. Technical or organizational siloes make it difficult to pool information in flexible data lakes. Many businesses also lack the skills to manage AI-enabled analytics amid rising privacy and ethical concerns. Others are unable to provide audit trails on how AI decisions were made or deliver AI-enabled analytics quickly enough.

AI success factors

Here are a few common characteristics we’ve seen among businesses that have realized success with data and AI:

  • Senior leadership is passionate about the value of data. CIOs can play a big role here. They need to work with the CEO and business leaders to resolve the tension between business managers who want more access to data and the pressure to restrict such access to reduce cost and risk.
  • Data scientists embedded with business users. By working more closely with business users, data scientists can better understand the business context behind their requests. Business managers may say they need “X,” but what they really need is a solution to a problem, which may or may not be “X.”
  • The establishment of an AI oversight role. Because AI systems learn and improve, they can produce different results at different times based on the data they’re given and the algorithms applied. This means they require closer oversight than traditional systems that are coded, tested and summarily released – and then left alone.In some ways, managing an AI system is like raising a child. You need to be sure they’re not being trained with intentionally or unintentionally biased data or by malicious users. If a loan application is denied, for example, you need to know the decision was ethically sound.
  • Plenty of high-quality data. The more high-quality data, the better and more quickly the AI system can learn. To supply that data, organizations must refine their processes for ongoing data preparation, integration and pipelining. This essential groundwork is the hardest part in building an AI system.
  • Modernized infrastructure. An infrastructure based on application programming interfaces (APIs) and Agile development techniques makes it easier to quickly deliver new AI-based applications. This more flexible infrastructure enables organizations to better access needed data from outside the enterprise, and more effectively monetize the resulting insights by sharing them across the ecosystem.For instance, we worked with one of the world’s largest and busiest airports to revamp its technology infrastructure to increase airport efficiency and performance. The organization combined data and AI to unlock more capacity to serve airlines and reduce passenger misconnects.

Our final recommendation: Start now. AI can be difficult and complex, and it’s hard to catch up once you’ve fallen behind. Even modest successes will teach you a lot, and the real danger in digital is being a laggard.

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How BMO branch technology saves employees up to 30 minutes per day

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When it comes to banking, it comes as little surprise that customers are increasingly preferring tellerless interactions. 

A recent customer insight report from Mercator Advisory Group found that those who don’t like using mobile and online banking prefer to use self-service kiosks at physical branch locations.

Even back in 2015, a study by Source Technologies found that self-service retail banking kiosks improve operations, “reducing the time it takes to get an official check from nine minutes (using a teller) to 40 seconds – 13.5 times faster than a teller-conducted transaction.”

When banks invest in features like remote authentication and mobile deposits, it isn’t just customers who benefit — staff are able to better focus on more complex transactions, and developing relationships with clients.

“We see that more and more of our customers are migrating toward self-serve interactions, especially for the simpler, straightforward transactions,” explained Kyle Barnett, BMO’s chief operating officer for US personal and business banking, in an interview with PYMNTS. 

One of technologies implemented by BMO was a faster, real-time process for scanning and depositing cheques, saving customers from having to fill out a paper deposit slip. This has led to deposits clearing within hours instead of days. 

Another BMO implementation was its easy PIN authentication; instead of using a driver’s licenses or state-issued ID, customers use debit cards to verify their identities. The transaction is therefore accelerated, and data is aggregated instantly on the teller’s screen.

Both of these improvements were implemented in more than 500 branches by the end of 2019.

“If a customer walks in and opens up an account [during the] same interaction, they can actually leave with a fully functioning, embossed card that has their name on it,” Barnett said. 

And unlike before, when a customer was issued a temporary card and had to wait for the fully-functioning replacement to arrive in the mail, “they also get the PIN right there as part of the account opening, and can even set up a custom PIN if they want at the ATM.”

With the in-branch experience changing, and customers requiring fewer interactions with tellers, the result has been “really freeing up our branch bankers to have more time to dedicate to customers, and have better holistic conversations, and create more personalized recommendations.” 

One case study found that employees have saved between 15 and 30 minutes per day on processing forms. Multiply that by the number of employees within BMO, and you get a major win for efficiency and time saving. 

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Addressing cybercrime with passwordless authentication

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Cybercrime costs the global economy $2.9 million every minute, according to a RiskIQ report titled The Evil Internet Minute, adding up to $1.5 trillion in a year.

The cause? 80% of attacks are password-related issues.

Just think: How many times have you been prompted to change your password by your bank or email company? How many times have you heard the term “data breach” in the news? How many accounts do you have that require two-step authentication? How many passwords do you have, scattered across various accounts?

PINs, passwords, security questions are a daily part of our lives, but it’s typically nothing more than a hassle when we have to change them. For larger organizations, however, costs add up. Estimates show that nearly 50 percent of IT help desk costs are allocated to password resets.

Enter: Passwordless authentication — harnessing the power of technologies like AI and Machine Learning to save time and money.  

Cyber security is high on the World Economic Forum’s agenda, and in collaboration with open industry association FIDO Alliance, WEF’s whitepaper Passwordless Authentication The next breakthrough in secure digital transformation presents a framework for future authentication systems, illustrating five key passwordless technologies organizations can implement.

As part of Davos 2020, the paper — featuring lead authors Andrew Shikiar (Executive Director and Chief Marketing Officer, FIDO Alliance) and Adrien Ogee (Project Lead, Platform for Shaping the Future of Cybersecurity and Digital Trust, World Economic Forum) — outlines that “passwords are indeed at the heart of the data breach problem,” and presents facial biometric technology, hardware keys, and even QR codes and behavioural analysis as the future of passwordless authentication. 

“While company adoption of platform businesses is increasingly driving business valuation and growth, the problem of digital trust is growing equally fast and eroding confidence across online communities,” explains the introduction.

“Security enhancement is a continuous process, there is no magic bullet. Cyber criminals will adapt and develop new means of attack, but the alternative authentication mechanisms presented here provide greater challenge to them and greater security in the foreseeable future.”

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