Tag Archives: Deep Blue

Insurers: Start Boosting Your ‘AIQ’

In this blog series, we explore what benefits insurers would gain if they start boosting what we call the Artificial Intelligence Quotient (AIQ), and give an overview of why and how insurers should leverage technology, data and people to get started.

Perhaps some of you remember when an IBM computer called “Deep Blue” defeated world chess champion Garry Kasparov, in 1997. Twenty years later, he said:

“Human plus machine isn’t the future, it’s the present; Don’t fear intelligent machines, work with them.”

Please note, he says “plus” not “versus” or “against.” I believe he is right – there is actually an optimistic story of unlocked potential.

When AI was enlisted to detect breast cancer

A team of Harvard pathologists recently showed how some commentators on the future of artificial intelligence – who either wax enthusiastic about the productivity gains or predict the elimination of jobs – are missing the most important point.

The doctors created an AI-based technique to identify breast cancer cells. It did well, scoring 92% accuracy, but still fell short of human pathologists, who typically achieve precision rates of around 96%. The biggest surprise came when humans and AI combined forces. Together, they accurately identified 99.5% of cancerous biopsies.

Much like Kasparov, I believe this is how AI will realize its fullest potential—when human ingenuity and intelligent machines combine synergistically to deliver more than either can on its own.

How does this relate to Insurance?

There are numerous use cases of AI that can be applied along the insurance value chain, such as enhanced pricing, customized products and services, efficient underwriting and claims administration processes.

Sales & Distribution — Employing machine learning insights to support customer segmentation could lead to benefits such as customized products and services and increased sales.

Underwriting & Risk Management — The extraction of insights from multiple data sources can lead to improved risk evaluation quality and, hence, better pricing.

Servicing — using machine learning to handle external emails and requests can lead to an increased efficiency in administration processes.

Claims — The pre-assessment of claims and automated damage evaluation leads to higher quality and accuracy in claims assessment, management and administration and, hence, to cost savings.

Recruiting — Leveraging contextual analytics and skills-based ontology to score resumes against job descriptions leads to an optimized conversion rate.

There are many benefits for insurers – more efficiency, better pricing, customized products and services.

AI will deliver efficiencies, but its greatest benefit will be to drive growth. Especially when it’s used primarily to augment the capabilities of humans.

Artificial intelligence is set to play a major role in insurance, not only by improving efficiencies but by significantly enhancing the customer experience, boosting innovation and opening up new sources of growth.

Accenture conducted an econometric analysis that forecasts that insurers that invest in AI and human-machine collaboration at the same rate as top-performing businesses could not only profit from becoming a more efficient company but also boost their revenue by an average 17% by 2022.

But the research revealed that, while executives expect AI to completely transform insurance, they believe only 25% of their employees are ready to work with intelligent technologies.

See also: 3 Steps to Demystify Artificial Intelligence  

Insurers acknowledge AI’s importance and its transformative potential. They admit their people are critical to the success of their AI initiatives, and the growing skills gap is the key factor influencing their workforce strategy, but only 4% plan to significantly increase their investment in reskilling programs over the next three yearsSo, there is a major disconnect in insurers’ approach to AI.

Time to Boost Your AIQ

To help insurers take the next steps toward investing in AI, we created two indexes to see what is working. We studied both the FORTUNE Global 100 and what we call the “Intelligent Global 100” – these are pioneers in the development of AI technologies and applications – for the period between 2010 and 2016. For those 200 companies, we looked at both their in-house focus (their AIQ for invention) and their outside focus (AIQ for collaboration). Both are essential.

Organizations with a high AIQ invest significantly in their in-house AI capabilities and collaborate externally. Yet our analysis revealed that fewer than 20% score well on both indexes — those companies we call “collaborative inventors” (who balance in-house innovation with external collaboration) — while 56% were weak on both.

Not surprisingly, financial services (as you can see in the lower left quadrant) currently has one of the weakest scores on this matrix.

How do you build your AIQ?

There are three key ingredients to building a high AIQ: Insurers need to own some of the technology (but define business goals before applying AI), some of the data (recognize the importance of data convergence) and some of the talent (create the future workforce; start reskilling your staff). And they will also need to be deeply involved in a broader ecosystem.

You need to work at all three, both in-house and collaboratively, to achieve success.

Business leaders need to evaluate how they invest in technology, data and people. To start, they need to ask themselves the following tough questions:


  • Does your AI amplify the skills of your people?
  • Are you integrating your AI with internal and external systems?
  • Does owning your own AI differentiate you in the market?


  • Have you partnered with companies that can monetize your data?
  • Can you integrate data with your legacy systems?
  • Do you practice responsible AI?


  • Can you reskill your workforce to stay ahead of automation?
  • Do you offer continuous on-the-job training?
  • Can your leaders manage diverse teams?

Finding honest answers to these questions and taking the next steps forward matters, as they are the basis for leveraging the full potential of AI.

Before embarking on this transformational journey and to answer all these questions, it makes sense to first develop a cross-enterprise AI strategy to clarify strategic goals – the whys, the hows and the whats of the business model.

As we have seen, the majority of insurance executives believe human-machine collaboration is important if they are to achieve their strategic objectives.

Because the workforce is a critical enabler of any AI strategy, insurers need to develop a workforce that is equipped and willing to work at a higher level in collaboration with intelligent machines.

Insurers, in my view, cannot afford to wait until the future is clear and predictable, but need to start now.

There is a choice that insurers need to make. Which company do they want to be? The one that has strategically leveraged intelligent technology, data and upskilled its people – or the one that has not?

Transforming the workforce to collaborate effectively with AI won’t be easy or quick, but it is essential if the potential of artificial intelligence is to be realized. The time to start is now.

See also: Strategist’s Guide to Artificial Intelligence  

As mentioned at the beginning, the use cases for the application of AI along the insurance value chain are numerous, and I will explore some in my next posts. This series is structured to answer a number of key questions:

  • How can you boost your AIQ in sales and distribution?
  • How can you boost your AIQ in underwriting and risk management?
  • How can you boost your AIQ in claims management?
  • How can you boost your AIQ in client services and policy administration?
  • What steps should you take to create the insurance workforce of the future needed to become an AI-driven company?

To learn more about how to raise your organization’s AIQ, download the report: Boost your AIQ.

You can find the article originally published at Accenture.

The Robocalypse for Knowledge Jobs

Long-time Costa Rican National Champion Bernal Gonzalez told a very young me in 1994 that the world’s best chess-playing computer wasn’t quite strong enough to be among the top 100 players in the world.

Technology can advance exponentially, and just three years later world champion Garry Kasparov was defeated by IBM’s chess playing supercomputer Deep Blue. But chess is a game of logic where all potential moves are sharply defined and a powerful enough computer can simulate many moves ahead.


Things got much more interesting in 2011, when IBM’s Jeopardy-playing computer Watson defeated Ken Jennings, who held the record of winning 74 Jeopardy matches in a row, and Brad Rutter, who has won the most money on the show. Winning at Jeopardy required Watson to understand clues in natural spoken language, learn from its own mistakes, buzz in and answer in natural language faster than the best Jeopardy-playing humans. According to IBM, ”more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence and merge and rank hypotheses.” Now that’s impressive — and much more worrisome for those employed as knowledge workers.


What do game-playing computers have to do with white collar, knowledge jobs? Well, Big Blue didn’t spend $1 billion developing Watson just to win a million bucks playing Jeopardy. It was a proof of concept and a marketing move. A computer that can understand and respond in natural language can be adapted to do things we currently use white collar, educated workers to do, starting with automating call centers and, sooner rather than later, moving on up to more complex, higher-level roles, just like we have seen with automation of blue collar jobs.

In the four years since its Jeopardy success, Watson has continued advancing and is now being used for legal research and to help hospitals provide better care. And Watson is just getting started. Up until very recently, the cost of using this type of technology was in the millions of dollars, making it unlikely that any but the largest companies could make the business case to replace knowledge jobs with AIs (artificial intelligence). In late 2013, IBM put Watson “on the cloud,” meaning that you can now rent Watson time without having to buy the very expensive servers.

Watson is cool but requires up-front programming of apps for very specific activities and, while incredibly smart, lacks any sort of emotional intelligence, making it uncomfortable for regular people to deal with it. In other words, even if you spent the millions of dollars to automate your call center with Watson, it wouldn’t be able to connect with your customer, because it has no sense of emotions. It would be like having Data answering your phones.

Then came Amelia…


Amelia is an AI platform that aims to automate business processes that up until now had required educated human labor. She’s different from Watson in many ways that make her much better-suited to actually replace you at the office. According to IPsoft, Amelia aims at working alongside humans to “shoulder the burden of tedious, often laborious tasks.”

She doesn’t require expensive up-front programming to learn how to do a task and is hosted on the cloud, so there is no need to buy million-dollar servers. To train her, you literally feed her your entire set of employee training manuals, and she reads and digests them in a matter of a few seconds. Literally, just upload the text files, and she can grasp the implications and apply logic to make connections between the concepts. Once she has that, she can start working customer emails and phone calls and even recognize what she doesn’t know and search the Internet and the company’s intranet to find an answer. If she can’t find an answer, then she’ll transfer the customer to a human employee for help. You can even let her listen to any conversations she doesn’t handle herself, and she literally learns how to do the job from the existing staff, like a new employee would, except exponentially faster and with perfect memory. She also is fluent in 20 languages.

Like Watson, Amelia learns from every interaction and builds a mind-map that eventually is able to handle just about anything your staff handled before. Her most significant advantage is that Amelia has an emotional component to go with her super brains. She draws on research in the field of affective computing, “the study of the interaction between humans and computing systems capable of detecting and responding to the user’s emotional state.” Amelia can read your facial expressions, gestures, speech and even the rhythm of your keystrokes to understand your emotional state, and she can respond accordingly in a way that will make you feel better. Her EQ is modeled in a three-dimensional space of pleasure, arousal and dominance through a modeling system called PAD. If you’re starting to think this is mind-blowing, you are correct!

The magic is in the context. Instead of deciphering words like insurance jargon when a policyholder calls in to add a vehicle or change an address, IPsoft explains that Amelia will engage with the actual question asked. For example, Amelia would understand the same requests that are phrased different but essentially mean the same thing: “My address changed” and “I need to change my address.” Or, “I want to increase my BI limits” and “I need to increase my bodily injury limits”.

Amelia was unveiled in late 2014, after a secretive 16-year-long development process, and is now being tested in the real world at companies like Shell Oil, Accenture, NNT Group and Baker Hughes on a variety of tasks from overseeing a help desk to advising remote workers in the field.


Chetan Dube, long-time CEO of IPSoft, Amelia’s creator, was interviewed by Entrepreneur magazine:

“A large part of your brain is shackled by the boredom and drudgery of everyday existence. […] But imagine if technology could come along and take care of all the mundane chores for you, and allow you to indulge in the forms of creative expression that only the human brain can indulge in. What a beautiful world we would be able to create around us.”

His vision sounds noble, but the reality is that most of the employees whose jobs get automated away by Watson, Amelia and their successors, won’t be able to make the move to higher-level, less mundane and less routine tasks. If you think about it, a big percentage of white collar workers have largely repetitive service type jobs. And even those of us in higher-level roles will eventually get automated out of the system; it’s a matter of time, and less time than you think.

I’m not saying that the technology can or should be stopped; that’s simply not realistic. I am saying that, as a society, there are some important conversations we need to start having about what we want things to look like in 10 to 20 years. If we don’t have those discussions, we are going to end up in a world with very high unemployment, where the very few people who hold large capital and those with the STEM skills to design and run the AIs will do very well, while the other 80-90% of us could potentially be unemployable. This is truly scary stuff, McKinsey predicts that by 2025 technology will take over tasks currently performed by hundreds of millions of knowledge workers. This is no longer science fiction.

As humans, our brains evolved to work linearly, and we have a hard time understanding and predicting change that happens exponentially. For example, merely 30 years ago, it was unimaginable that most people would walk around with a device in their pockets that could perform more sophisticated computing than computers at MIT in the 1950s. The huge improvement in power is a result of exponential growth of the kind explained by Moore’s law, which is the prediction that the number of transistors that fit on a chip will double every two years while the chip’s cost stays constant. There is every reason to believe that AI will see similar exponential growth. Just five years ago, the world’s top AI experts at MIT were confident that cars could never drive themselves, and now Google has proven them wrong. Things can advance unimaginably fast when growth becomes exponential.


Some of the most brilliant minds of our times are sounding the alarm bells. Elon Musk said, “I think we should be very careful about AI. If I had to guess, our biggest existential threat is probably that we are summoning the demon.” Stephen Hawking warned, “The development of full-artificial intelligence could spell the end of the human race.”