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Competing in the age of artificial intelligence


As AI finally begins to realize its long-heralded potential and change the business environment drastically in the process, companies need to develop an understanding of these fundamental changes and develop well thought out, but flexible, strategies to harness the strongest capabilities of both men and machines.

Introduction

After decades of unfulfilled promise, artificial intelligence has finally begun to realize its potential, driven by massive gains in processing power and better data collection methods. Developments in natural language processing and computer vision have played particularly important roles in helping machines perform tasks traditionally reserved for men. AI has also caught the public imagination over the years thanks to well-publicized events like Deep Blue’s chess victory over Kasparov, AlphaGo’s recent victory over Lee Sedol, IBM Watson’s victory in Jeopardy or even Google’s demonstration of self-driving cars. All this has prompted massive investments in AI-related areas in industries like finance, retail and health care.

Differences between human and machine thinking

Because AI systems ‘think’ and interact, they are often compared with people. Humans are fast at parallel processing (pattern recognition) and slow at sequential processing (logical reasoning); exactly opposed to this, while computers have mastered parallel processing in some narrow fields, their strength lies in superfast logical reasoning.

Human intelligence allows for different types of problem-solving capabilities. Compared to this, at current processing power growth rate, artificial general intelligence is a long-term possibility at best. AI excels at performing specific tasks, fast and thoroughly. 

So while investment in AI is critical, it is useful to ask this question: How can business leaders harness AI to take advantage of the specific strengths of man and machine?

AI’s effects on traditional ways of doing business

On notions of competitive advantage

In the 1980s, a technology tool in itself (such as Wal-Mart’s logistics tracking system) could serve as a source of advantage. But, now, because of open source software, algorithms in and of themselves will not provide an edge. Other traditional sources of advantage, like positional advantage and capability, are also being reframed by AI. Companies need a more fluid and dynamic view of their strengths, instead of a focus on static aspects. These three examples show how traditional notions of competitive advantage are changing:

·       Data – This is the raw material for AI systems, where large and early-moving companies like Google, Facebook and Uber have an apparent advantage. They have created massive data repositories, which helps them collect more data, and also leverage it for better ad-targeting or in self-driving cars.  But other companies without these resources can still do well through collaborations, even with competitors, to create their own privileged zones. They need to figure out areas where sharing works, and where it may not.

 

·       Customer Access – Traditional notions of customer access, like well-placed physical stores, are being replaced by AI-driven customer insights that help with personalized marketing and appetite prediction, generating higher revenues at negligible extra cost.

 

·       Capabilities – AI-driven automation is encouraging the replacement of traditional segmented and discrete areas of knowledge by cross-functional capabilities and agile ways of working.

On decision making

·       The speed of decision making is changing rapidly.

·       Predictive analytics and objective data is replacing decisions based on gut feel and experience (as can be seen in stock trading, online ads, supply chain management and retail pricing).

On the role of human employees

Humans will not become obsolete, but will see rapid and major dislocations into new areas of work.

·       They will be needed in large numbers to build the AI systems.

·       They will be needed to help machines with common sense, social skills and intuition as well as for quality checks. 

In such an AI-inspired world, strategic issues are enmeshed with organizational, technological and knowledge issues. Agility, flexibility and continuous education are important for winning strategies.

Getting started towards winning strategies

Companies need to identify the jobs that machines or men are better at, develop complementary roles and redesign processes accordingly. Plus, they should be willing to change and adapt strategies at short notice. This is true in general for all areas in today’s business world, but especially so for AI-enabled processes.

Instead of a brute force implementation of AI everywhere, companies could evaluate through four lenses whether AI can create a significant and durable advantage:

·       Customer needs – Define the fundamental needs of your customers and see if AI can better address them

·       Technological advances – Study if the right technology exists to address your requirements, and if you can make use of work already done instead of building a system from scratch

·       Data sources – Create a holistic architecture that combines existing data with novel sources, even if they come from outside

·       Decomposition of processes – Break down processes into relatively routinized and isolated elements that can be automated, taking advantage of tech advances and data sources identified above

These steps can be challenging for most companies. Setting up a center of excellence can help keep track of current and emerging capabilities, incubate technical and business acumen and disseminate expertise through the organization. It can then collaborate with the functions that would eventually put AI to use.

Thus, the full potential of AI can be achieved only if humans and machines solve problems together and learn from each other.

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7 ways ai will affect your life in 2018


Since at least Turing’s time in 1950, intelligent computers have fascinated people. But it has taken decades for the right combination of factors to come together to move AI from concept to an increasingly ubiquitous reality. After a lot of talk in 2017, it is in 2018 that data generated because of mass use of Internet-connected devices, and algorithms that recognize patterns in this data, will lead to tangible ways in which AI affects all our lives. Some of these expected developments are:

1.     Smart virtual assistants – Personal assistants based on AI will become smarter and more affordable. They will learn our daily routines and handle simple, but useful, chores like ordering groceries.

2.     Multiple voice-based gadgets – The popularity of voice-based assistants will result in many devices at home across platforms. There’s exciting potential, but possible chaos expected as well.

3.     Practical use of facial recognition – Going beyond security and biometric capabilities, facial recognition will start replacing credit cards, driver’s licenses and barcodes. No need to even line up at the payment counter at a store.

4.     Basic AI terminology becoming commonplace – As AI permeates the enterprise, everyone from CEO through middle managers to frontline employees will start becoming conversant in basic terminology. This will help demystify the technology and open up possibilities.

5.     Personalized media – Forget identifying songs that you will like – new services could start creating music from scratch based on your tastes.

6.     Tailored news and market reports – Reports that don’t just recap market performance, but explain your portfolio performance, at any time, will soon be a reality. Newsrooms will also use AI in more innovative ways.

7.     Wide use in healthcare – By end-2018, nearly half of leading healthcare systems will have adopted some form of AI within their diagnostic groups, not just in medical specialties, but even in hospital operations, solutions for population health and clinical specialties. The way patients experience healthcare globally will truly begin its transformation in 2018.

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4 deep learning breakthroughs for business leaders


Gaining an understanding of deep learning is the best place to start for executives interested in keeping up with the rapid developments in AI. An exciting and powerful subfield of AI, deep learning focuses on how computers learn as opposed to how they are explicitly programmed by us.

In this method, researchers place concepts into a hierarchy. At each layer, a machine learns a concept and passes it to the next layer, which in turn uses it to build a more sophisticated concept. The more layers these models have – or the ‘deeper’ they are – the more concepts they can learn, leading to ever-expanding possibilities of applying AI to business problems.

 Here are four deep learning breakthroughs business leaders should be aware of, from the immediately applicable to the most cutting edge. Drastically different applications prove that these can be used innovatively to introduce the next great product or service based on the right set of data.

1.     Image understanding – Deep learning algorithms called convolutional neural networks, which can be trained to identify objects in an image, already do better in image classification than humans.

Applications:

·       Google Image Search

·       Self-driving cars

·       Disease diagnoses

 

2.     Sequence prediction – Recurrent neural networks can be trained to look at huge amounts of past sequences of characters or data, learn their patterns and generate future sequences.

Applications:

·       Producing human-like handwriting

·       Prediction of user demand by Uber

 

3.     Language translation – Discovered in 2014, this technology uses sequence-to-sequence architecture and recurrent neural networks to make machine translation almost as good as human translation. So far used for narrowly defined domains, this area holds significant promise.
Applications:

·       Google Translate and Apple’s Siri

·       Chatbots

4.     Generative models – Creation of models that generate complex data, like images that resemble faces but aren’t actual faces. This is possible due to architectures called generative adversarial networks, which use convolutional neural nets. Though their business applications are limited as of now, research is on to find exciting ways to deploy their power to solve practical challenges.

Applications:

·       Aiding image classification models to distinguish real images from fake ones

 

Open source implementations of the above breakthroughs make it possible to download pre-trained models to apply to your data. For example, pre-trained image classifiers can be purchased to feed your data through to classify new images.

This overview brings you closer to companies like Uber and Google that put deep learning models to good use. The next generation of business applications is yet to come, and will be driven by new ways discovered by you to apply these techniques to your own data.

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Devising a supply-side strategy to win in the ai market


To capture value in this market, suppliers need to clearly understand the key trends in the industry, and devise a go-to-market strategy accordingly.

Introduction

With better algorithms, increased stores of data and improved hardware performance, AI is finally beginning to achieve its potential. Sophisticated technologies like Deep Learning further increase the need for investments in leading-edge hardware and software. While several companies have taken early steps to win in this market, the industry is still nascent, leaving the field open for supply side companies to capture value and see a return on their huge AI investments.

The supply side landscape

Before moving on to a strategy, it is important to clearly understand the chaotic supply landscape in AI. The offerings in AI can be seen as a nine-layered technology stack:

Positioning to win in AI

To capture value in the growing AI market, companies on the supply side need to heed the following six points:

1.     Value capture in the consumer sector will be limited initially – Early AI offerings are product and service enhancements that appeal to consumers and increase their engagement level, but do not contribute to the bottom line, e.g. online translation, digital voice assistants. These are typically offered by large tech companies, who have bigger pockets and access to more data, leaving little opportunities for smaller companies initially.
But fee-based offerings, like home assistants and self-driving cars, will create more opportunities in the next wave of innovations.

2.     Winners will focus on microverticals in promising industries – Instead of getting confused by the hundreds of opportunities available, suppliers should focus on specific industries.
To select the right industries, following criteria can be helpful:

·       Size of the industry

·       Potential for disruption within the industry

·       Maturity (or readiness to embrace new solutions)

Based on these criteria, the strongest opportunities appear to be available in the public sector, banking, retail and automotive industry.
Once an industry is identified, suppliers should identify specific verticals where solutions can result in high ROI. The value proposition is not compelling for broad, horizontal, industry-wide solutions.

3.     Suppliers will need to provide end-to-end solutions – Customers look for solutions across all nine layers of the technology stack from the same supplier. This saves effort spent in getting different components to work together, and also provides suppliers with strategic foothold with customers. Such wide-ranging capabilities could be completely in-house, through acquisitions or through collaborations. Nvidia, for example, offers its Drive PX platform for cars as a module, not just a chip, combining processors, software, cameras, sensors and other components.

 

4.     Most value will come from services and hardware – Bucking the general trend of commoditization of hardware, about 40-50% of value to AI vendors will come from this part of the technology stack. This is because, in AI, every use case has slightly different requirements, needing partially customized hardware (head nodes, inference accelerators, training accelerators).
Another 40-50% value will come from services (solutions and use cases). System integrators, who often have direct access to customers, will capture most of these gains by bringing solutions together across all layers of the stack.
Software, surprisingly, is unlikely to be a differentiator. Since data – another important component of AI – comes from the customer itself in most cases, it will not provide much value either. Though a market for third-party data might emerge in the future.

 

5.     Specific hardware architectures will be critical differentiatorsCloud will continue to be the favored option for many applications, given its scale advantage. Within cloud hardware, customers and suppliers vary in their preference for application-specific integrated circuit (ASIC) technology over graphics processing units (GPUs), and the market is likely to remain fragmented.
There will be a growing role for inference at the edge, where low latency or privacy concerns are critical, or when connectivity is problematic. At the edge, ASICs will win in the consumer space because they provide a more optimized user experience, including lower power consumption and higher processing, for many applications. Enterprise edge will see healthy competition among field programmable gate arrays, GPUs, and ASIC technology. However, ASICs may have an advantage because of their superior performance per watt, which is critical on the edge. 

 

6.     The need to act is now – To win in AI, reliance on status quo won’t work. Strategy revision and big bets to develop solid offerings right now is the key, instead of waiting two to three years. Unconventional strategies are already seeing high returns. For instance, Nvidia is increasing its AI R&D expenditure and creating an end-to-end product ecosystem, gaining market share and seeing exceptional returns from AI offerings.

 

Keeping in mind the above points, these three questions could be useful in devising a strategy for the AI market:

·       Where to compete – Look at industries and microverticals, select a use case that suits your capabilities and address the customers’ most pressing needs

·       How to compete – Look for partners to provide end-to-end solutions; Be open to creative pricing options

·       When to compete – Instead of striving for perfection, focus on solutions that help you establish a presence now

 

 

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Artificial intelligence and new challenges for salespeople


The advent of artificial intelligence is throwing up challenges for many sections of the industry. This is a revolution with many possibilities, but it also means that the human workforce will have to adapt smartly to extract its full potential.

One of the more obvious changes we are seeing is how robotic systems are beginning to handle many transaction services, like taking food orders at a drive-thru food outlet or handing out money at a bank ATM. Not just low-paying entry-level jobs, but even mid-level positions will see increasing presence of AI as the technology develops, especially in sales.

AI vs. Human intelligence

Does this mean that all sales workforces will be replaced by robots completely? Before answering this, let’s consider the key arguments that AI has over a human worker. One, salespersons take home salaries, which an employer saves on when working with a machine (even after considering the maintenance and programming costs). Two, automation allows customers access to services at any time – think waking up an admin guy at 3 am against switching on a robot. Three, even the most ardent opponents of AI will concede that there are some ways in which artificial intelligence scores over human intelligence. So, in a competitive world, employers are not completely unjustified in giving AI a chance.

What history teaches us?

But it’s not all doom and gloom. Consider the automobile industry as a comparison. Despite early beliefs in the role of the human element, the machine revolution of the 80s and 90s did put a large number of workers out of business. At the same time, those who learned to work the robots stayed behind. And other opportunities opened up – gas boom picked up; so did the mortgage industry – absorbing workers from the auto industry and creating new jobs too.

The way forward

Thus, the challenge for salespeople also brings up an opportunity in its wake. The future of sales is learning automation, the tech side and artificial intelligence so that you can work with the robots. And as other sectors of the industry open up, you need to keep your eyes out for them. It is all about embracing the new technology, so that instead of getting washed away by the wave, you can ride it successfully.

 

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