Over the years, the influence of artificial intelligence (AI) has spread to almost every aspect of the travel and the hospitality industry. Thirty percent of hospitality businesses use AI to augment at least one of their primary sales processes, and most customer personalisation is done using AI. The proliferation of AI in the travel and hospitality industry can be credited to the humongous amount of data being generated today. AI helps analyse data from obvious sources, brings value in assimilating patterns in image, voice, video, and text, and turns it into meaningful and actionable insights for decision making. Trends, outliers, and patterns are figured out using machine learning-based algorithms that help in guiding a travel or hospitality company to make informed decisions.

“Discounts, schemes, tour packages, and seasons and travellers to target are formulated using this intelligent data combined with behavioural science and social media attribution to know customers behaviour and insights. “

Let’s take a close look at the AI-driven application areas in the travel and hospitality industry and the impact on the ensuing business value chain:

Bespoke and curated experiences

There are always a few trailblazers who are up for a new challenge and adopt new-age exponential technologies. Many hotel chains have started using an AI concierge. One great example of an AI concierge is Hilton World wide’s Connie, the first true AI-powered concierge bot. Connie stands at two feet high and guests can interact with it during their check-in. Connie is powered by IBM’s Watson AI and uses the Way Blazer travel database. It can provide succinct information to guests on local attractions, places to visit, etc. Being AI-driven with self-learning ability, it can learn and adapt and respond to each guest on personalised basis.

In the travel business, Mezi, using AI with Natural Language Processing technique, provides a personalised experience to business travellers, who usually are strapped for time. It talks about bringing on a concept of bleisure (business+leisure) to address the needs of the workforce. The company’s research shows that 84 percent of business travellers return feeling frustrated, burnt out, and unmotivated. The kind of tedious and monotonous planning that goes into the travel booking could be the reason for it. With AI and NLP, Mezi collects individual preferences and generates personalised suggestions so that a bespoke and streamlined experience is given and the issues faced are addressed properly.

Intelligent travel search

Increased productivity now begins with the search for the hotel, and sophisticated AI usage has paved the way for the customer to access more data than ever before. Booking sites like Lola.com provides on-demand travel services and have developed algorithms that can not only instantly connect people to their team of travel agents who find and book flights, hotels, and cars, but have been able to empower their agents with tremendous technology to make research and decisions an easy process.

Intelligent travel assistants

 Chatbot technology is another big strand of AI, and not surprisingly, many travel brands have already launched their own versions in the past year or so. Skyscanner is just one example, creating an intelligent bot to help consumers find flights in Facebook Messenger. Users can also use it to request travel recommendations and random suggestions. Unlike ecommerce or retail brands using chatbots, which can appear gimmicky, there is an argument that examples like Skyscanner are much more relevant and useful for everyday consumers. After all, with the arrival of many more travel search websites, consumers are being overwhelmed by choice – not necessarily helped by it. Consequently, a chatbot like Skyscanner is able to cut through the noise, connecting with consumers in their own time and in the social media spaces they most frequently visit.

Recently, Aero Mexico started using Facebook Messenger chatbot to answer very generic customer questions. The main idea was to cater to 80 percent of questions, which are usually repeat ones and about common topics. Thus, AI is of great application to avoid a repetitive process. Airlines hugely benefit from this. KLM Royal Dutch Airlines uses AI to respond to the queries of customers on Twitter and Facebook. It uses an algorithm from a company called Digital Genius, which is trained on 60,000 questions and answers. Not only this, Deutsche Lufthansa’s bot Mildred can help in searching the cheapest fares.

Intelligent recommendations

International hotel search engine Trivago acquired Hamburg, Germany machine learning startup Tripl as it ramps up its product with recommendation and personalisation technology, giving them a customer-centric approach. The AI algorithm gives tailored travel recommendations by identifying trends in users’ social media activities and comparing it with in-app data of like-minded users. With its launch, users could sign up only through Facebook, potentially sharing oodles of profile information such as friends, relationship status, hometown, and birthdays.

Persona-based travel recommendations, use of customised pictures and text are now gaining ground to entice travel. KePSLA’s travel recommendation platform is one of the first in the world to do this by using deep learning and NLP solutions. With 81 percent of people believing that intelligent machines would be better at handling data than humans, there is also a certain level of confidence in this area from consumers.

Knowing your traveller

Dorchester Collection is another hotel chain to make use of AI. However, instead of using it to provide a front-of-house service, it has adopted it to interpret and analyse customer behaviour deeply in the form of raw data. Partnering with technology company, Richey TX, Dorchester Collection has helped to develop an AI platform called Metis.

Delving into swathes of customer feedback such as surveys and reviews (which would take an inordinate amount of time to manually find and analyse), it is able to measure performance and instantly discover what really matters to guests. Métis helped Dorchester to discover that breakfast it not merely an expectation – but something guests place huge importance on. As a result, the hotels began to think about how they could enhance and personalise the breakfast experience.

Intelligent forecasting: flight fares and hotel tariffs 

Flight fares and hotel tariffs are dynamic and vary on real-time basis, depending on the provider. No one has time to track all those changes manually. Thus, intelligent algorithms that monitor and send out timely alerts with hot deals are currently in high demand in the travel industry.

Trivago and Make my trip are screening through swamp of data points, variables, and demand and supply patterns to recommend optimised travel and hotel prices. The AltexSoft data science team has built such an innovative fare predictor tool for one of their clients, a global online travel agency, Fareboom.com. Working on its core product, a digital travel booking website, they could access and collect historical data about millions of fare searches going back several years. Armed with such information, they created a self-learning algorithm, capable of predicting future price movements based on a number of factors, such as seasonal trends, demand growth, airlines special offers, and deals.

Optimised disruption management: delays and cancellations 

While the previous case is focused mostly on planning trips and helping users navigate most common issues while traveling, automated disruption management is somewhat different. It aims at resolving actual problems a traveller might face on his/her way to a destination point. Mostly applied to business and corporate travel, disruption management is always a time-sensitive task, requiring instant response.

While the chances of getting impacted by a storm or a volcano eruption are very small, the risk of a travel disruption is still quite high: there are thousands of delays and several hundreds of cancelled flights every day. With the recent advances in AI, it became possible to predict such disruptions and efficiently mitigate the loss for both the traveller and the carrier. The 4site tool, built by Cornerstone Information Systems, aims to enhance the efficiency of enterprise travel.

The product caters to travellers, travel management companies, and enterprise clients, providing a unique set of features for real-time travel disruption management. In an instance, if there is a heavy snowfall at your destination point and all flights are redirected to another airport, a smart assistant can check for available hotels there or book a transfer from your actual place of arrival to your initial destination.

Not only are passengers are affected by travel disruptions; airlines bear significant losses every time a flight is cancelled or delayed. Thus, Amadeus, one of the leading global distribution systems (GDS), has introduced a Schedule Recovery system, aiming to help airlines mitigate the risks of travel disruption. The tool helps airlines instantly address and efficiently handle any threats and disruptions in their operations.  

Future potential: So, reflecting on the above-mentioned use cases of the travel and hospitality industry leveraging Ai to a large extent, there will be few latent potential areas in the industry that will embrace AI in the future :

“Undoubtedly, we will witness many travel and hospitality organisations using AI for intelligent recommendations as well as launching their own chatbots. There’s already been a suggestion that Expedia is next in line, but it is reportedly set to focus on business travel rather than holidaymakers.”

Due to the greater need for structure and less of a desire for discovery, it certainly makes sense that AI would be more suited to business travellers. Specifically, it could help to simplify the booking process for companies, and help eliminate discrepancies around employee expenses. With reducing costs and improving efficiency two of the biggest benefits, AI could start to infiltrate business travel even more so than leisure in the next 12 months.

Lastly, we can expect to see greater development in conversational AI in the industry. With voice-activated search, the experience of researching and booking travel has the potential to become quicker and easier than ever before. Similarly, as Amazon Echo and Google Home start to become commonplace, more hotels could start to experiment with speech recognition to ramp up customer service. This means devices and bots could become the norm for brands in the travel and hospitality industry.

The travel and hospitality industry transformation will morph into experience-driven and asset-light business, and wide adoption of AI will usher a new-age customer experience and set a benchmark for other industries to emulate. Fasten your seat belts … AI will redefine the travel and hospitality industry.

The extra ordinary promise of AI : Global & Indian enterprises have a lot to gain from unleashing innovation with AI —but harnessing their potential demands focused investment and a new way of working with external partners.

Here are few salient features of how AI has become game changing trend in spurring innovation; existing challenges and few strategic approaches of unlocking innovation with AI :

• 22% growth : From 2015 through 2019, disclosed private investment in seven deep tech sectors grew an average of 22% per year, equaling nearly $60 billion in total investment. Corporate venture capital is also playing an increasingly active role.
• Total investment : Nearly $60 Billion Invested in Deep Tech’s Fastest-Growing Sectors in 2019; Artificial intelligence corners close to $25 Bn
• About 1800 AI led startups in the US accounted for roughly half of this total investment, but other countries are catching up fast.

Existing Challenges

• Complex ecosystems : Multiple types of players including startups, venture capital firms, governments, universities and research centers, and early-adopter user groups
• Dynamic Interactions : Few central orchestrators; business relationships based on informal networks rather than formal contracts

Strategic approaches of unlocking innovation with AI :

• Cooperate in order to compete : Think beyond the enterprise’s immediate goals; commit to a long-term vision for the development of the ecosystem as whole
• Identify capabilities that add value : Define what the enterprise can offer to nurture the ecosystem and bring AI to market—not only money but also access to customers, data, networks, mentors, and technical experts
• Don’t pick winners in advance : AI startups are evolving rapidly. Continuously monitor the ecosystem to identify successful startups, applications, and business models as they emerge
• Blur the boundaries with partners : Make it easy for AI partners to navigate your corporate system. Define a clear role for them in your innovation strategy, ensure senior-executive sponsorship, and engage the core businesses
• Streamline decision making and governance : Success requires partnering more nimbly with fast-moving AI startups. Embrace agile ways of working.
• Develop breakthrough solutions by combining expertise from previously unconnected fields or industries. Be alert for game hanging opportunities that deliver both economic and social value.

AI will transform business and society in the future. The time to craft a AI strategy for unleashing innovation is now.

AIQRATE works closely with global & Indian enterprises , GCCs , VC/PE firms and has an extensive yet curated database of 1000 + global AI startups , boutique and niche firms benchmarked on our “Glow Curve” assessment.

(AIQRATE advisory & consulting is a bespoke AI advisory and consulting firm and provide strategic advisory services to boards , CXOs, senior leaders to curate , design building blocks of AI strategy , embed AI@scale interventions and create AI powered enterprises . visit : www.aiqrate.ai ; reach out to us at consult@aiqrate.ai )

Business transformation programs have long focused on productivity enhancements —taking a “better, faster, cheaper” approach to how the enterprise works. And for good reason: disciplined efforts can boost productivity as well as accountability, transparency, execution, and the pace of decision making. When it comes to delivering fast results to the bottom line, it’s a proven recipe that works.

The problem is, it’s no longer enough. Artificial Intelligence enabled disruption are upending industry after industry, pressuring incumbent companies not only to scratch out stronger financial returns but also to remake who and what they are as enterprises.

Doing the first is hard enough. Tackling the second—changing what your company is and does—requires understanding where the value is shifting in your industry (and in others), spotting opportunities in the inflection points, and taking purposeful actions to seize them. The prospect of doing both jobs at once is sobering.

How realistic is it to think your company can pull it off? The good news is that AIQRATE can demonstrate that it’s entirely possible for organizations to ramp up their bottom-line performance even as they secure game-changing portfolio wins that redefine what a company is and does. What’s more, AL led transformations that focus on the organization’s performance and portfolio appear to load the dice in favor of transformation results. By developing these two complementary sets of muscles, companies can aspire to flex them in a coordinated way, using performance improvements to carry them to the next set of portfolio moves, which in turn creates momentum propelling the company to the next level.

Strategic Steps towards AI led Transformation:

This aspect covers AI led “portfolio-related” moves. The first is active resource reallocation towards building AI led transformation units, which I define as the company shifting more than 20 percent of its capital spending across its businesses or markets over ten years. Such firms create 50 percent more value than counterparts that shift resources at a slower clip.

Meanwhile, a big move in programmatic M&A driven by AI led spot trending—the type of deal making that produces more reliable performance boosts than any other—requires the company to execute at least one deal per year, cumulatively amounting to more than 30 percent of a company’s market capitalization over ten years, and with no single deal being more than 30 percent of its market capitalization.

Making big moves tends to reduce the risk profile and adds more upside than downside. The way I explain this to senior executives is that when you’re parked on the side of a volcano, staying put is your riskiest move.

AI led Transformations that go ‘all in’ by addressing both a company’s performance and its portfolio yield the highest odds.

The implication of these transformation stories is clear: approaches that go all in by addressing both a company’s performance and its portfolio yield the highest odds of lasting improvement. Over the course of a decade, companies that followed this path nearly tripled their likelihood of reaching the top quin tile of the AI transformation power curve relative to the average company in the middle.

Play to win with AI

Life would be simpler if story ended here. However, you’re not operating in a competitive vacuum. As I described earlier, other forces influence your odds of success in significant ways—in particular, how your industry is performing. Research studies have indicated that companies facing competitive headwinds would face longer odds of success than those with tailwinds.

Companies that combined big performance moves with big portfolio moves (including capital expenditures, when not the only portfolio move employed) saw a big lift in their odds. Life is still challenging for these companies—their net odds are dead even—yet this is superior to the negative odds of the other situations.

Winning thru competitive advantage with AI

In an improving industry, the returns to performance improvement are amplified massively. This runs contrary to the very human tendency of equating performance transformations with turnaround cases

The takeaway from all this is that two big rules stand out as commonly and powerfully true whatever your context: first, get moving with AI , don’t be static; second, go all in if you can with AI led transformation programs —it’s always the best outcome (and also the rarest).

Running the AI led transformation program

In my experience, the companies that are most successful at transforming themselves with AI ,sequence their moves so that the rapid lift of performance improvement provides oxygen and confidence for big moves in M&A, capital investment, and resource reallocation. And when the right portfolio moves aren’t immediately available or aren’t clear, the improved performance helps buy a company time until the strategy can catch up.

To illustrate this point, consider the anecdote about Apple that Professor Richard Rumelt describes in his book, Good Strategy/Bad Strategy. It was the late 1990s; Steve Jobs had returned to Apple and cleaned house through productivity-improving cutbacks and a radically simplified product line. Apple was much stronger, yet it remained a niche player in its industry. When Rumelt asked Jobs how he planned to address this fact, Jobs just smiled and said, ‘I am going to wait for the next big thing.’

While no one can guarantee that your “next big thing” will be an iPod-size breakthrough, there’s nothing stopping you from laying the groundwork for a successful AI led transformation. To see how prepared, you are for such an undertaking, ask yourself—and your team—the following five questions. I sincerely hope they provoke productive and transformative discussion among your team.

1.Where is the new business value chain that’s driven by AI

Achieving success with big, portfolio-related moves requires understanding where the business value flows in your business and why. The structural attractiveness of markets, and your position in them, can and does change over time. Ignore this and you might be shifting deck chairs on the Titanic. Meanwhile, to put this thinking into action, you must also view the company as an ever-changing portfolio. This represents a sea change for managers who are used to plodding, once-a-year strategy sessions that are more focused on “getting to yes” and on protecting turf than on debating real alternatives. Get high-powered decision-making algorithms to navigate you thru this transformation.

2. Put your money in building an AI led strategy

Only 10% of the US fortune 200 companies have AI led strategy; this is an impending strategic aspect that cannot be ignored. The dimensions of reimagining customer experience, building innovative products and services and transforming the businesses need to have an AI led strategy move by the CXOs

3.Are you ready for disruption?

Increasingly, incumbent organizations are getting to the pointy end of disruption, where they must accelerate the transition from legacy business models to new ones and even allow potentially cannibalizing businesses to flourish. Sometimes this requires a very deliberate two-speed approach where legacy assets are managed for cash while new businesses are nurtured for growth.

4.Will our company take this seriously?

Embracing AI led transformative change requires commitment, and gaining commitment requires a compelling change story that everyone in the company can embrace. Philips recognized this in 2011 when it launched its “Accelerate” program. Along with productivity improvements and portfolio changes (including a big pivot from electronics to health tech), the company shaped its change story around improving three billion lives annually by 2030, as part of a broader goal of making the world healthier and more sustainable through innovation. Massive thrust and investment was laid by Phillips leadership team on AI led transformation programs.

5.Is the leadership ready for the transformation?

Leading a successful AI led transformation requires a lot more than just picking the right moves and seeing them through. Among your other priorities: build momentum, engage your workforce, and make the change personal for yourself and your company. All of this means developing new leadership skills and ways of working, while embracing a level of commitment as a leader that may be unprecedented for you.

In the end, AI led strategy for transformation is a process and start of a journey …. embrace it or feel the heat of leaving behind. The new age competition is agile and nimble and AI led transformation strategy is a right move to thwart the competition.

It’s time for boards to craft an AI led strategy . Three strategic aspects can help them and senior leaders to augment decision making process in the board meetings

In the boardroom, and the head of a major global conglomerate is in the hot seat. A director with a background in the manufacturing industry is questioning the economics, an assumption underlying the executive’s industry forecast: that the industry’s ratio of forecast will remain relatively constant. The business leader appears confident about the assumption of stability, which has implications for both the competitive environment and for financial results. But the director isn’t convinced: “In my experience, the forecast changes continuously with the economic cycle and needs to bake in assumptions,” he says, “and I’d feel a whole lot better about these estimates if you had some facts to prove that this has changed.” and the rest of the board doesn’t have it. Finally, the chairman intervenes: “The question being raised is critical and not just for our manufacturing business but for our entire strategy. We’re not going to resolve this today, but let’s make sure it’s covered thoroughly during our strategy off-site and he added , “let’s have some good staff work in place to inform the discussion.”

If the preceding exchange sounds familiar, it should: in the wake of the financial crisis, we find that uncomfortable conversations such as this one are increasingly common in boardrooms around the world as corporate directors and executives come to grips with a changed environment. Ensuring that a company has a great strategy is among a board’s most important functions and the ultimate measure of its stewardship. Yet even as new governance responsibilities and faster competitive shifts require much more—and much better—board engagement on strategy, a great number of boards remain hamstrung by familiar challenges.

Enter AI led strategy for boards

For starters, there’s the problem of time: most boards have about six to eight meetings a year and are often hard pressed to get beyond compliance-related topics to secure the breathing space needed for developing strategy. A recent survey of board members to learn where they’d most like to spend additional time, two out of three picked strategy. A related finding was that 44 percent of directors said their boards simply reviewed and approved management’s proposed strategies. Why such limited engagement? One likely reason is an expertise gap: only 10 percent of the directors felt that they fully understood the industry dynamics in which their companies operated. As a result, only 21 percent of them claimed to have a complete understanding of the current strategy .

What’s more, there’s often a mismatch between the time horizons of board members and of top executives , and that lack of alignment can diminish a board’s ability to engage in well-informed give-and-take about strategic trade-offs. “The chairman of my company has effectively been given a decade,” says the CEO of a company “and I have three years—tops—to make my mark. If I come up with a strategy that looks beyond the current cycle, I can never deliver the results expected from me. Yet I am supposed to work with him to create long-term shareholder value. How am I supposed to make this work?” It’s a fair question, particularly since recent shows that major strategic moves involving active capital reallocation deliver higher shareholder returns than more passive approaches over the long haul, but lower returns over time frames of less than three years.

Compounding these challenges is the increased economic volatility prompting many companies to rethink their strategic rhythm, so that it becomes less calendar driven and formulaic and more a journey involving frequent and regular dialogue among a broader group of executives. To remain relevant, boards must join management on this journey, and management in turn must bring the board along—all while ensuring that strategic co-creation doesn’t become confusion or, worse, shadow management. This is where curating AI strategy for competitive advantage and informed decision making comes to the picture.

Three strategic aspects to ponder on AI led strategy for Boards :

While no one-size-fits-all solution can guide companies as they set out, board members and senior managers ask themselves three simple questions as they approach the development of AI strategy. Using it should raise the quality of decision making , overall engagement and help determine the practical steps each group must take to get there. The usual annual strategic refresh is unlikely to provide the board with an appreciation of the context it would need to address the questions fully, let alone to generate fresh insights in response.

1.Can AI make the boards understand the industry dynamics

Most boards spend most of their strategic time reviewing plans, yet relatively few directors feel they have a complete understanding of the dynamics of the industries their companies operate in or even of how those companies create value. To remedy this problem and to avoid the superficiality it can engender, boards need time—some without management present—so they can more fully understand the structure and economics of the business, as well as how it creates value. They should use this time to get ahead of issues rather than always feeling a step behind during conversations on strategy or accepting management biases or ingrained habits of thought.AI can lay out comprehensive picture of industry and competitive industry dynamics with historical and future forward looking scenarios to make the job of the boards simpler.

2. Can AI trigger enough board–management debate before a specific strategy is discussed?

Aided thru AI and armed with a foundational view based on a clearer understanding of industry and company economics, boards are in a better position to have the kinds of informed dialogue with senior managers that ultimately help them prepare smarter and more refined strategic options for consideration. Board members should approach these discussions with data driven mind-set and with the goal of helping management to broaden its thinking by considering new, even unexpected, perspectives.

During such debates, management’s role is to introduce key pieces of content: a detailed review of competitors, key external trends likely to affect the business, and a view of the specific capabilities the company can use to differentiate itself. The goal of the dialogue is to develop a stronger, shared understanding of the skills and resources the company can use to produce strong returns, as opposed to merely moving with the tide. This is where boards can evangelize and seep in AI in the senior executives group for broader knowledge augmentation . 

3.Can AI bring in all strategic options and approaches to the table for board and management ?

Very often, the energizing discussions between the board and management about the business, its economics, and the competition represent the end of the debate. Afterward, the CEO and top team go off to develop a plan that is then presented to the board for approval. Instead, what’s needed at this point is for management to take some time—go thru the self-learning enabled algorithm —to formulate a robust set of strategic options, each followed through to its logical end state, including the implications for the allocation of people, capital, and other resources. These strategic options through the revised algorithmic exercise can then be brought back to the board for discussion and decision making.

Developing AI led strategy is a new phenomenon and will take time to mature —yet will become more powerful algorithmic based decision making process and with board’s increased involvement, which introduces new voices and expertise to the debate and puts pressure on management teams and board members alike to find the best answers. Yet this form of AI led strategy development, when done well, is invaluable. It not only leads to clearer strategies but also creates the alignment necessary to make bolder moves with more confidence and to follow through by committing resources to key decisions. AI led decision making for the boards is here….

(AIQRATE advisory & consulting is a bespoke AI advisory and consulting firm and provide strategic advisory services to boards , CXOs, senior leaders to curate , design building blocks of AI strategy , embed AI@scale interventions and create AI powered enterprises . visit : www.aiqrate.ai )

While some predict mass unemployment or all-out war between humans and artificial intelligence, others foresee a less bleak future. A future looks promising, in which humans and intelligent systems are inseparable, bound together in a continual exchange of information and goals, a “symbiotic autonomy.” If you may. It will be hard to distinguish human agency from automated assistance — but neither people nor software will be much use without the other.

Mutual Co-existence – A Symbiotic Autonomy

In the future, I believe that there will be a co-existence between humans and artificial intelligence systems that will be hopefully of service to humanity. These AI systems will involve software systems that handle the digital world, and also systems that move around in physical space, like drones, and robots, and autonomous cars, and also systems that process the physical space, like the Internet of Things.

I don’t think at AI will become an existential threat to humanity. Not that it’s impossible, but we would have to be very stupid to let that happen. Others have claimed that we would have to be very smart to prevent that from happening, but I don’t think it’s true.

If we are smart enough to build machine with super-human intelligence, chances are we will not be stupid enough to give them infinite power to destroy humanity. Also, there is a complete fallacy due to the fact that our only exposure to intelligence is through other humans. There are absolutely no reason that intelligent machines will even want to dominate the world and/or threaten humanity. The will to dominate is a very human one (and only for certain humans).

Even in humans, intelligence is not correlated with a desire for power. In fact, current events tell us that the thirst for power can be excessive (and somewhat successful) in people with limited intelligence.

You will have more intelligent systems in the physical world, too — not just on your cell phone or computer, but physically present around us, processing and sensing information about the physical world and helping us with decisions that include knowing a lot about features of the physical world. As time goes by, we’ll also see these AI systems having an impact on broader problems in society: managing traffic in a big city, for instance; making complex predictions about the climate; supporting humans in the big decisions they have to make.

Intelligence of Accountability

A lot of companies are working hard on making machines to be able to explain themselves — to be accountable for the decisions they make, to be transparent. A lot of the research we do is letting humans or users query the system. When Cobot, my robot, arrives to my office slightly late, a person can ask , “Why are you late?” or “Which route did you take?”

So they are working on the ability for these AI systems to explain themselves, while they learn, while they improve, in order to provide explanations with different levels of detail. People want to interact with these robots in ways that make us humans eventually trust AI systems more. You would like to be able to say, “Why are you saying that?” or “Why are you recommending this?” Providing that explanation is a lot of the research that is being done, and I believe robots being able to do that will lead to better understanding and trust in these AI systems. Eventually, through these interactions, humans are also going to be able to correct the AI systems. So they are trying to incorporate these corrections and have the systems learn from instruction. I think that’s a big part of our ability to coexist with these AI systems.

The Worst Case Contingency

A lot of the bad things humans do to each other are very specific to human nature. Behavior like becoming violent when we feel threatened, being jealous, wanting exclusive access to resources, preferring our next of kin to strangers, etc were built into us by evolution for the survival of the species. Intelligent machines will not have these basic behavior unless we explicitly build these behaviors into them. Why would we?

Also, if someone deliberately builds a dangerous and generally-intelligent AI, other will be able to build a second, narrower AI whose only purpose will be to destroy the first one. If both AIs have access to the same amount of computing resources, the second one will win, just like a tiger a shark or a virus can kill a human of superior intelligence.

In October 2014, Musk ignited a global discussion on the perils of artificial intelligence. Humans might be doomed if we make machines that are smarter than us, Musk warned. He called artificial intelligence our greatest existential threat.

Musk explained that his attempt to sound the alarm on artificial intelligence didn’t have an impact, so he decided to try to develop artificial intelligence in a way that will have a positive affect on humanity

Brain-machine interfaces could overhaul what it means to be human and how we live. Today, technology is implanted in brains in very limited cases, such as to treat Parkinson’s Disease. Musk wants to go farther, creating a robust plug-in for our brains that every human could use. The brain plug-in would connect to the cloud, allowing anyone with a device to immediately share thoughts.

Humans could communicate without having to talk, call, email or text. Colleagues scattered throughout the globe could brainstorm via a mindmeld. Learning would be instantaneous. Entertainment would be any experience we desired. Ideas and experiences could be shared from brain to brain.

We would be living in virtual reality, without having to wear cumbersome goggles. You could re-live a friend’s trip to Antarctica — hearing the sound of penguins, feeling the cold ice — all while your body sits on your couch.

Final Word – Is AI Uncertainty really about AI ?

I think that the research that is being done on autonomous systems — autonomous cars, autonomous robots — it’s a call to humanity to be responsible. In some sense, it has nothing to do with the AI. The technology will be developed. It was invented by us — by humans. It didn’t come from the sky. It’s our own discovery. It’s the human mind that conceived such technology, and it’s up to the human mind also to make good use of it.

I’m optimistic because I really think that humanity is aware that they need to handle this technology carefully. It’s a question of being responsible, just like being responsible with any other technology every conceived, including the potentially devastating ones like nuclear armaments. But the best thing to do is invest in education. Leave the robots alone. The robots will keep getting better, but focus on education, people knowing each other, caring for each other. Caring for the advancement of society. Caring for the advancement of Earth, of nature, improving science. There are so many things we can get involved in as humankind that could make good use of this technology we’re developing

Artificial intelligence is getting ubiquitous and is transforming organizations globally. AI is no longer just a technology. It is now one of the most important lenses that business leaders need to look through to identify new business models, new sources of revenue and bring in critical efficiencies in how they do businesses.

Artificial intelligence has quickly moved beyond bits and pieces of topical experiments in the innovation lab. AI needs to be weaved into the fabric of business. Indeed, if you see the companies leading with AI today, one of the common denominators is that there is a strong executive focus around artificial intelligence. AI transformation can be successful when there is a strong mandate coming from the top and leaders make it a strategic priority for their enterprise.

Given AI’s importance to the enterprise, it is fair to say that AI will not only shape the future of the enterprise, but also the future for those that lead the enterprise mandate on artificial intelligence.

Curiosity and Adaptability

To lead with AI in the enterprise, top executives will need to demonstrate high levels of adaptability and agility. Leaders need to develop a mindset to harness the strategic shifts that AI will bring in an increasingly dynamic landscape of business – which will require extreme agility. Leaders that succeed in this AI era will need to be able to build capable, agile teams that can rapidly take cognizance of how AI can be a game changer in their area of business and react accordingly. Agile teams across the enterprise will be a cornerstone of better leadership in this age of AI.

Leading with AI will also require leaders to be increasingly curious. The paradigm of conducting business in this new world is evolving faster than ever. Leaders will need to ensure that they are on top of the recent developments in the dual realms of business and technology. This requires CXOs to be positively curious and constantly on the lookout for game changing solutions that can have a discernible impact on their topline and bottom-line.

Clarity of Vision

Leadership in the AI era will be strongly characterized by the strength and clarity with which leaders communicate their vision. Leaders with an inherently strong sense of purpose and an eye for details will be forged as organizations globally witness AI transformation.

It is not only important for those that lead with AI to have a clear vision. It is equally important to maintain a razor sharp focus on the execution aspect. When it comes to scaling artificial intelligence in the organization, the devil is very often in the details – the data and algorithms that disrupt existing business processes. For leaders to be successful, they must remain attentive to the trifecta of factors – completeness of their vision for AI transformation, communication of said vision to relevant stakeholders and monitoring the entire execution process. While doing so, it is important to remain agile and flexible as mentioned in my earlier section – in order to be aware of possible business landscape shifts on the horizon.

Engage with High EQ

AI transformation can often seem to be all about hard numbers and complex algorithms. However, leaders need to also infuse the human element to succeed in their efforts to deliver AI @ Scale. The third key for top executives to lead in the age of AI is to ensure that they marry high IQs with equally or perhaps higher levels of EQ.

Why is this so very important? Given the state of this technology today, it is important that we build systems that are completely free of bias and are fair in how they arrive at strategic and tactical decisions. AI learns from the data that it is provided and hence it is important to ensure that the data it is fed is free from bias – which requires a human aspect. Secondly, AI causes severe consternation among the working population – with fears of job loss abounding. It is important to ensure that these irrational fears of an ‘AI Takeover’ are effectively abated. For AI to be successful, it is important that both types of intelligence – artificial and human – symbiotically coexist to deliver transformational results.

AI is undoubtedly going to become one of the sources of lasting competitive advantage for enterprises. According to research, 4 out of 5 C-level executives believe that their future business strategy will be informed through opportunities made available by AI technology. This requires a leadership mindset that is AI-first and can spot opportunities for artificial intelligence solutions to exploit. By democratizing AI solutions across the organization, enterprises can ensure that their future leadership continues to prioritize the deployment of this technology in use cases where they can deliver maximum impact.

The failure to manage cyber risks will disrupt digital business in the current era and expose organization to possible impacts beyond opportunity loss. The degree to which CIOs involve in digital risk management will be a critical factor to circumvent such perils.

Digital advancements and change in the technological paradigm such as cloud, IoT and mobility have made cyber security an absolute necessity to safeguard enterprises from ransom ware.

The problem in front of CIOs is not only unregulated IoT devices in the enterprise , but also the nature of the devices themselves. Security needs to be improved in the design process and is the top strategic pillar of priority.

In the face of increasing cyber-attacks and more multifaceted, stringent data privacy laws, security has become a priority discussion in the boardrooms of organisations across different industries.

In this blog, I would like to explore the key drivers to implement a cyber security strategy and some of the preventive measures in case of threat to business. It also illustrates some latest information on cyber security solutions and the organizations response to dealing with the cyber security skills gap. It also analyses on how CIO’s are handling and prioritizing the changing cyber-security landscape.

As CIOs decide on risk levels they’re equipped to accept and pursue their security objectives, as information/data becomes critical for businesses.

Executive engagement towards cyber security

Cyber security accountability must lie with the CIO, but the culture of security needs to be adopted by the whole enterprise. Principal causes of cyber security occurrences result from employee negligence. CIO’s efforts endure to flounder against the number and variations of different cyber-attacks which keeps increasing continuously.

To combat and recognize these threats effectually, CIOs and IT executives need to cement an effective IT security strategy that enables the right tools and technologies at the same time foster a culture of security.

Several mechanisms together with a charter, policy, strategy and governance mechanisms form a digital cybersecurity program that delivers the suppleness required to enable business plans, notify risk trade-offs and respond to ever-changing threat environments.

There are no prescriptive approach  organizations that give comprehensive assurance that all rational steps have been implemented. CIO’s plays the imperative role  for setting direction for the organization to evaluate their own situations and assess a number of factors to make an informed judgment according to different scenarios.

The CIO becomes the key anchor emphasizing the linkage between business and cyber risk. This needs to be accomplished across, technical, non-technical staff, with the influence from the board. This is a critical time for CIOs to be thoughtful in their implementation and communication framework of cyber risk management issues across the stakeholders in the business. Prioritizing organization’s restrained business design and environmental factors, the CIO will be in a position to cover external threats and regulatory requirements

CIOs can’t shield the organizations on all type of risk and is practically not viable. It is imperative to  create a sense of balance between sustainable set of controls to protect their businesses with their need to run them. Taking a risk-based method will be a critical point to establish target levels of cybersecurity readiness. Budgeting alone does not create an environment for improved risk posture, CIOs must prioritize security investments to ensure that there is a true value for budget assigned on the right things this needs to be based on business outcomes.

Attacks and compromise are inevitable, and, by 2020, 60% of security budgets will be in support of detection and response capabilities.” — Paul Proctor, Gartner vice president and distinguished analyst

Cyber Security Sequence CIO’s could consider:

Consider a robust Risk-Based Method to Improve Business Outcomes: Cybersecurity issue requires judicious risk management that can be done effectively. This approach should be measurable and most importantly enable decision making and executive engagement.

Establish Cybersecurity and Risk Governance to enhance Information Security:

Effective governance is a cornerstone of security programs, CIO should ensure there is right leadership for risk management to support and implement governance and mitigate the risks for assurance.

CIOs Should Mitigate Cybersecurity risk have aligned to the Lens of Business Value:

Postulates that CIOs should address cybersecurity challenges like a business function. This will enable them to bring levels of protection that support business outcomes in accordance with the business value.

Cybersecurity is complex, it requires a specifically designed program that enables resilience, agility and accountability

 Organizations that rely on obsolete, basic approaches towards security program management will continue to experience incompetence and internal disconnects. This will reflect in failure to deliver optimum business results. Organizations that roadmap more complex, but agile approach will position themselves for digital business success and resilience.

 The cyber threat landscape continues to evolve with significant attacks happening, especially over the last decade. The changing paradigm of businesses in adopting IoT has a surge in these attacks. Greater amounts of threats coming into that space has a direct relation to consumer related devices, in the form of machine to machine traffic for businesses.

A CIO has an imperative role to instate security across  the organization and business lines. The responsibility extends for effectively handling risk mitigation that span the spectrum across the entire organization. This needs a laser focused approach that is ingrained into the daily operations of the IT setup but as well for the enterprise, products they deliver in the form of digital services.

The CIO’s role in security makes them suitable by the fact that they understand the consequences of technology. As enterprises endure digital transformation, CIO’s recognize that a lot of value comes in the information and delivery of those digital assets. The CIO is equipped with top notch expertise within the organization to comprehend different risk scenarios and successfully implement it across multiple cross-functional areas.

For the past few years, Artificial Intelligence has initiated unlocking value gains through the automation and augmentation of routinized operational activity. But are we underestimating the potential of machine intelligence? Does it make sense to relegate a powerful technology to perform tactical tasks? Or can AI move further upstream and help corporate boards make more accurate, strategic decisions?

The possibility of AI to enable better decision-making has been heavily discounted thus far. However, with Artificial Intelligence capably enabling more informed decisions in the realm of healthcare and investment banking – two of the most complex arenas where AI has been deployed – the possibility of having machine cognition in the boardroom no longer sounds too far-fetched. At the end of the day, corporate boards make complex decisions, that have huge ramifications for the future of their organizations. It is important that these decisions are based in fact, rather than judgement. AI can help corporate boards make faster, more accurate and unbiased decisions. AI can help inform strategy by giving executives a better understanding of their internal and external environments. Let us look at some key areas where senior executives in organizations can look at making better decisions using Artificial Intelligence.

AI for Executive Decision-Making

Corporate boards and top executives are charged with maintaining the health and competitiveness of an organization. They are responsible for the long-term sustainability and success of their organizations. This, in turn, requires them to stay ahead of the curve and understand their business landscape and intelligently deploy capital across inorganic and organic growth channels. Executives also own the key metrics for their organizations – and ensure that the overall return for the shareholder capital employed continuously beats industry expectations. Let us look at how AI can help transform the activity of executives in these areas.

Executive leaders often rely on numerous reports around key organizational metrics to make decisions that can have massive implications for their businesses. Is a particular segment of the business growing rapidly? Are some cost centers underperforming on their efficiency metrics? Are there laggards in the product portfolio of the enterprise that are dragging performance down? All these numbers have to figuratively be at the tip of an executive’s tongue – so that in key meetings decisions that affect the future of the business can be made more accurately and quickly.

AI-powered smart assistants would be extremely critical to help push the needle on making executive decisions with accuracy and speed. With intelligent bots, executives can be provided updates on the most critical metrics that they care for at the right time when they need them. With AI, it is possible to personalize the insights that are sent to executives – so that they are able to drill down and understand the basis for each metric.

Unbiased Capital Allocation on R&D and M&A Activities

Corporate boards and executives also need to take the long term view of how their companies evolve to thrive in the future. This requires intelligent bets to be taken on budgetary spending – for both organic and inorganic activities. How much money needs to be realistically spent on Research and Development activity and how it can it help corporations maintain larger moats against their competition? Can corporations look at inorganic acquisitions to accelerate the growth of synergistic capabilities that can form much more compelling value propositions?

AI will soon be able to provide comprehensive answers to such questions. By leveraging data from multiple sources combined with intelligent algorithms, AI will be able to weigh these multiple options and identify which one is best suited for each unique situations. In this way again, AI can help executives forecast which decisions can have maximum impact on financial metrics and model the long-term health of the organization.

As corporate boardrooms take serious cognizance of having robotic counterparts augmenting the decision-making process, it is important to consider certain caveats. For AI to work to its full potential, it is important to ensure that it is provided high quality data and continuously refined algorithms. We have seen the fallouts of algorithms going awry before. Biased algorithms working off bad data sets create issues that could potentially disrupt the fabric of the organization. It is therefore important that organizations ensure the implementation of explainable AI that can provide the rationale and take accountability of the decisions that it powers. Finally, it is important that executive leaders also create the right culture within their organizations for AI to thrive. A combination of human intelligence and artificial intelligence is the future and hence it is critical that companies relook at their culture to ensure that both can amicably survive together and put the organization on the right path.

According to research by McKinsey, it is estimated that 16 percent of board of directors did not fully understand how the dynamics of their industries were changing and how new technologies could impact their businesses. This gives AI a huge window of opportunity to permeate through global boardrooms and power better decisions. Decisions that can keep their organizations financially healthy, focused on the long-term and competitively differentiated against their competitors.

The definition of Data Breaches in current times have evolved from, happening under ‘malicious intent’, to also cover those which have been occurring as a consequences of bad data policies and regulation oversight. This means even policies that have been deemed legally screened might end up, in certain circumstances, in opening doors to some significant breach of data, user privacy and ultimately user trust.

For example, recently, Facebook banned data analytics company Cambridge Analytica from buying ads from its platform. The voter profiling firm allegedly procured 50 million physiological profiles of people through a research application developer Aleksandr Kogan, who broke Facebook’s data policies by sharing data from his personality-prediction app, that mined information from the social network’s users.

Kogan’s app, ‘thisisyourdigitallife’ harvested data not only from the individuals participating in the game, but also from everyone on their friend list. Since Facebook’s terms of services weren’t so clear back in 2014 the app allowed Kogan to share the data with third parties like Cambridge Analytica. This means policy wise it is a grey area whether the breach could be considered ‘unauthorized’, but it is clear that it happened without any express authorization from Facebook. This personal information was subsequently used to target voters and sway public opinion

This is different than the site hackings where credit card information was actually stolen at major retailers, the company in question, Cambridge Analytica, actually had the right to use this data. The problem is they used this information without permission in a way that was overtly deceptive to both Facebook users and Facebook itself.

Fallouts of Data Breaches: Developers left to deal with Tighter Controls

Facebook will become less attractive to app developers if it tightens norms for data usage as a fallout of the prevailing controversy over alleged misuse of personal information mined from its platform, say industry members.

India has the second largest developer base for Facebook, a community that builds apps and games on the platform and engage its users. With 241 million users, the country last July over took the US as the largest userbase for the social network platform.

There will be more scrutiny now. When you do, say, a sign on. The basic data (you can get) is the user’s name and email address, even which will undergo tremendous scrutiny before they approve it. That will have an impact on the timeline. The viral effect) could decrease. Now, without explicit rights from users, you cannot reach out to his/her contacts. Thus, the overhead goes on to the developers because of such data breaches, which shouldn’t have occurred in the first place had the policies surrounding user data were more distinct and clear.

Renewed Focus to Conflicting Data Policies and Human Factors

These kinds of passive breaches that happen because of unclear and conflicting policies instituted by Facebook provides us a very clear example of how active breaches (involving malicious attacks) and passive breaches (involving technically authorized but legally unsavoury data sharing) need to be given equal priority and should both be considered pertinent focus of data protection.

While Facebook CEO Mark Zuckerberg has vowed to make changes to prevent these types of information grabs from happening in the future, many of those tweaks will be presumably made internally. Individuals and companies still need to take their own action to ensure their information remains as protected and secure as possible.

Humans are the weakest link in data protection, and potentially even the leading cause for the majority of incidents in recent years. This debacle demonstrates that cliché to its full extent. Experts believe that any privacy policy needs to take into account all third parties who get access to the data too. While designing a privacy policy one needs to keep the entire ecosystem in mind. For instance, a telecom player or a bank while designing their privacy policy will have to take into account third parties like courier agencies, teleworking agencies, and call centers who have access to all their data and what kind of access they have.

Dealing with Privacy in Analytics: Privacy-Preserving Data Mining Algorithms

The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithms to leverage this information. A number of algorithmic techniques such as randomization and k-anonymity, have been suggested in recent years in order to perform privacy-preserving data mining. Different communities have explored parallel lines of work in regards to privacy preserving data mining:

Privacy-Preserving Data Publishing: These techniques tend to study different transformation methods associated with privacy. These techniques include methods such as randomization, k-anonymity, and l-diversity. Another related issue is how the perturbed data can be used in conjunction with classical data mining methods such as association rule mining.

Changing the results of Data Mining Applications to preserve privacy: In many cases, the results of data mining applications such as association rule or classification rule mining can compromise the privacy of the data. This has spawned a field of privacy in which the results of data mining algorithms such as association rule mining are modified in order to preserve the privacy of the data.

Query Auditing: Such methods are akin to the previous case of modifying the results of data mining algorithms. Here, we are either modifying or restricting the results of queries.

Cryptographic Methods for Distributed Privacy: In many cases, the data may be distributed across multiple sites, and the owners of the data across these different sites may wish to compute a common function. In such cases, a variety of cryptographic protocols may be used in order to communicate among the different sites, so that secure function computation is possible without revealing sensitive information.

Privacy Engineering with AI

Privacy by Design is a policy concept was introduced the Data Commissioner’s Conference in Jerusalem, and over 120 different countries agreed they should contemplate privacy in the build, in the design. That means not just the technical tools you buy and consume, [but] how you operationalize, how you run your business; how you organize around your business and data.

Privacy engineering is using the techniques of the technical, the social, the procedural, the training tools that we have available, and in the most basic sense of engineering to say, “What are the routinized systems? What are the frameworks? What are the techniques that we use to mobilize privacy-enhancing technologies that exist today, and look across the processing lifecycle to build in and solve for privacy challenges?”

It’s not just about individual machines making correlations; it’s about different data feeds streaming in from different networks where you might make a correlation that the individual has not given consent to with personally identifiable information. For AI, it is just sort of the next layer of that. We’ve gone from individual machines, networks, to now we have something that is looking for patterns at an unprecedented capability, that at the end of the day, it still goes back to what is coming from what the individual has given consent to? What is being handed off by those machines? What are those data streams?

Also, there is the question of ‘context’. The simplistic policy of asking users if an application can access different venues of their data is very reductive. This does not, in an measure give an understanding of how that data is going to be leveraged and what other information about the users would the application be able to deduce and mine from the said data? The concept of privacy is extremely sensitive and not only depends on what data but also for what purpose. Have you given consent to having it used for a particular purpose? So, I think AI could play a role in making sense of whether data is processed securely.

The Final Word: Breach of Privacy as Crucial as Breach of Data

It is undeniably so that we are slowly giving equal, if not more importance to breach of privacy as compared to breach of data, which will eventually target even the policies which though legally acceptable or passively mandated but resulted in compromise of privacy and loss of trust. Because there is no point claiming one is legally safe in their policy perusal if the end result leads to the users being at the receiving end.

This would require a comprehensive analysis of data streams, not only internal to an application ecosystem, like Facebook, but also the extended ecosystem involving all the players it is channeling the data sharing to, albeit in a policy-protected manner. This will require AI enabled contextual decision making to come to terms as what policies could be considered as eventually breaching the privacy in certain circumstances.

Longer-term, though, you’ve got to write that ombudsman. We need to be able to engineer an AI to serve as an ombudsman for the AI itself.

AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo’s success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer — independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It’s not implausible to imagine that one day a “strategist in a box” could autonomously develop and execute a business strategy. We’ve spoken to CXOs and leaders who express such a vision — and companies such as Amazon and Alibaba are already beginning to make it a reality.

Business Processes – Increasing productivity by reducing disruptions

AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures,

As Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge. The plan was to increase the production rate of that aircraft faster than ever before. To do that, they needed to address issues like responding quickly to disruptions in the factory. Because they will happen. Airbus turned to artificial intelligence. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems.

AI led to rectification of about 70% of the production disruptions for Airbus, by matching to solutions used previously — in near real time.

Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organizations. Other large companies, such as BP, Infosys, Wells Fargo, and Ping An Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

Integrated Strategy Machine – The Implementation Scope Augmented AI

The integrated strategy machine is the AI analog of what new factory designs were for electricity. In other words, the increasing intelligence of machines could be wasted unless businesses reshape the way they develop and execute their strategies. No matter how advanced technology is, it needs human partners to enhance competitive advantage. It must be embedded in what we call the integrated strategy machine. An integrated strategy machine is the collection of resources, both technological and human, that act in concert to develop and execute business strategies. It comprises a range of conceptual and analytical operations, including problem definition, signal processing, pattern recognition, abstraction and conceptualization, analysis, and prediction. One of its critical functions is reframing, which is repeatedly redefining the problem to enable deeper insights.

Amazon represents the state-of-the-art in deploying an integrated strategy machine. It has at least 21 data science systems, which include several supply chain optimization systems, an inventory forecasting system, a sales forecasting system, a profit optimization system, a recommendation engine, and many others. These systems are closely intertwined with each other and with human strategists to create an integrated, well-oiled machine. If the sales forecasting system detects that the popularity of an item is increasing, it starts a cascade of changes throughout the system: The inventory forecast is updated, causing the supply chain system to optimize inventory across its warehouses; the recommendation engine pushes the item more, causing sales forecasts to increase; the profit optimization system adjusts pricing, again updating the sales forecast.

Manufacturing Operations – An AI assistant on the floor

CXOs at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. They have something called the BP well advisor that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. They  are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.

Customer-facing activities – near real time scoring

Ping An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, is improving customer service across its insurance and financial services portfolio with AI. For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 million faces in various uses and now complements Ping An’s cognitive AI capabilities including voice and imaging recognition.

AI Strategy for Different Operational Models

To make the most of this technology implementation in various business operations in your enterprise, consider the three main ways that businesses can or will use AI:

Assisted intelligence

Now widely available, improves what people and organizations are already doing. For example, Google’s Gmail sorts incoming email into “Primary,” “Social,” and “Promotion” default tabs. The algorithm, trained with data from millions of other users’ emails, makes people more efficient without changing the way they use email or altering the value it provides. Assisted intelligence tends to involve clearly defined, rules-based, repeatable tasks.

Assisted intelligence apps often involve computer models of complex realities that allow businesses to test decisions with less risk. For example, one auto manufacturer has developed a simulation of consumer behavior, incorporating data about the types of trips people make, the ways those affect supply and demand for motor vehicles, and the variations in those patterns for different city topologies, marketing approaches, and vehicle price ranges. The model spells out more than 200,000 variations for the automaker to consider and simulates the potential success of any tested variation, thus assisting in the design of car launches. As the automaker introduces new cars and the simulator incorporates the data on outcomes from each launch, the model’s predictions will become ever more accurate.

Augmented intelligence

Augmented Intelligence, emerging today, enables organizations and people to do things they couldn’t otherwise do. Unlike assisted intelligence, it fundamentally alters the nature of the task, and business models change accordingly.

For example, Netflix uses machine learning algorithms to do something media has never done before: suggest choices customers would probably not have found themselves, based not just on the customer’s patterns of behavior, but on those of the audience at large. A Netflix user, unlike a cable TV pay-per-view customer, can easily switch from one premium video to another without penalty, after just a few minutes. This gives consumers more control over their time. They use it to choose videos more tailored to the way they feel at any given moment. Every time that happens, the system records that observation and adjusts its recommendation list — and it enables Netflix to tailor its next round of videos to user preferences more accurately. This leads to reduced costs and higher profits per movie, and a more enthusiastic audience, which then enables more investments in personalization (and AI).

Autonomous intelligence

Being developed for the future, Autonomous Intelligence creates and deploys machines that act on their own. Very few autonomous intelligence systems — systems that make decisions without direct human involvement or oversight — are in widespread use today. Early examples include automated trading in the stock market (about 75 percent of Nasdaq trading is conducted autonomously) and facial recognition. In some circumstances, algorithms are better than people at identifying other people. Other early examples include robots that dispose of bombs, gather deep-sea data, maintain space stations, and perform other tasks inherently unsafe for people.

As you contemplate the introduction of artificial intelligence, articulate what mix of the three approaches works best for you.

• Are you primarily interested in upgrading your existing processes, reducing costs, and improving productivity? If so, then start with assisted intelligence, probably with a small group of services from a cloud-based provider.
• Do you seek to build your business around something new — responsive and self-driven products, or services and experiences that incorporate AI? Then pursue an augmented intelligence approach, probably with more complex AI applications resident on the cloud.
• Are you developing a genuinely new technology? Most companies will be better off primarily using someone else’s AI platforms, but if you can justify building your own, you may become one of the leaders in your market.

The transition among these forms of AI is not clean-cut; they sit on a continuum. In developing their own AI strategy, many companies begin somewhere between assisted and augmented, while expecting to move toward autonomous eventually.