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In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture.  Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems.  To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it.  AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.

In the age of the bionic enterprises, AI continues to dominate the technology & business landscape.  Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres.  Traditionally, human decisions are to a large extent based on intuition, gut and historical data.  In the age of AI, several of our decisions will be taken by algorithms.  Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business.  All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage.  If you have not started yet, you are already behind the competition, however large or pedigreed you might be.

So, where are you placed on your AI journey?  At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.

At AIQRATE, we are here to help you with the art of the possible with AI.  Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses.  Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.

We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator.  AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.

Enclosing here our journey walk through of 2020.

The buzzword among the business and tech communities in China for the past year has been ‘AI’, or artificial intelligence. Artificial intelligence, which allows software to “learn” human ways of thinking, is being incorporated into the largest e-commerce platforms, including Baidu, Alibaba, and Tencent, as well as into data-intensive traditional sectors. With strong government backing and concentrated research in this area, AI is poised to drive China’s economy forward toward higher levels of growth.

China is developing artificial intelligence in improving the capabilities of robotics, developing driverless cars, divining consumer preferences, inventory forecasting, selling enhanced products, and marketing goods and services. According to Liu Lihua, Vice Minister of Industry and Information Technology, China has thus far applied for 15,745 AI patents.

China plans to launch a national AI plan, which will strengthen AI development and application, introduce policies to contain risks associated with AI, and work toward international cooperation. The plan will also provide funds to back these endeavors. Some municipalities also support AI research programs. Beijing, for example, is home to the CAS Institute of Automation, a consortium of universities and firms that provides venture capital funding of 1 billion RMB ($150 million) to AI development. Zhejiang province has also embraced AI programs. Already, Geely Automobile in Zhejiang is using intelligent manufacturing and internet marketing services based on AI to boost sales.

BAT – Chinese AI Frontier Giants

China’s BAT, or Baidu, Alibaba and Tencent, is leading the way for AI in China. Baidu was the first Chinese company to embark upon research in AI, using a system known as Duer to be used in home devices and driverless cars. Driverless auto software provided by Baidu will be made available to car manufacturers under the Apollo Project. Alibaba is using AI to forecast regional order quantities and to improve logistics efficiency, while Tencent has released a platform for deep learning using social data.

Baidu, Alibaba and Tencent have been vying for top talent in AI in order to become leaders in this area. Making headlines several days ago, Alibaba lured Ren Xiaofeng from Amazon.com to lead its own technology lab, which aims to make headway in artificial intelligence. Tencent brought Baidu’s AI expert Zhang Tong on board in March. In 2014, Baidu poached Andrew Ng from the Google Brain project to lead the Baidu Research Institute (though he recently stepped down).

Bay Area dominates this year’s AI funding

Venture investment in startups that are applying artificial intelligence or machine learning has more than tripled in the U.S. since 2013, according to PitchBook Data, with about 60 percent of that coming to founders in the Silicon Valley Bay Area.

The Seattle investment research firm put together a ranking of the top 20 AI deals done around the world this year for me while I was researching this week’s Silicon Valley Business Journal cover story. Almost half of the startups that were funded and nearly three-quarters of the investors involved were from San Francisco and the Silicon Valley region.

The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

Silicon Valley’s financiers and entrepreneurs are digging into artificial intelligence with remarkable exuberance. The region now has at least 19 companies designing self-driving cars and trucks, up from a handful five years ago. There are also more than a half-dozen types of mobile robots, including robotic bellhops and aerial drones, being commercialized.

Funding in A.I. start-ups has increased more than fourfold to $681 million in 2015, from $145 million in 2011, according to the market research firm CB Insights. The firm estimates that new investments will reach $1.2 billion this year, up 76 percent from last year.

Even Silicon Valley’s biggest social media companies are now getting into artificial intelligence, as are other tech behemoths. Facebook is using A.I. to improve its products. Google will soon compete with Amazon’s Echo and Apple’s Siri, which are based on A.I., with a device that listens in the home, answers questions and places e-commerce orders. Satya Nadella, Microsoft’s chief executive, recently appeared at the Aspen Ideas Conference and called for a partnership between humans and artificial intelligence systems in which machines are designed to augment humans.

The auto industry has also set up camp in the valley to learn how to make cars that can do the driving for you. Both technology and car companies are making claims that increasingly powerful sensors and A.I. software will enable cars to drive themselves with the push of a button as soon as the end of this decade — despite recent Tesla crashes that have raised the question of how quickly human drivers will be completely replaced by the technology.

AI is in it for the long-haul

Whenever there is a new idea, the valley swarms it. But you have to wait for a good idea, and good ideas don’t happen every day. Silicon Valley’s new A.I. era underscores the region’s ability to opportunistically reinvent itself and quickly follow the latest tech trend. This is at the heart of the region’s culture that goes all the way back to the Gold Rush. The valley is built on the idea that there is always a way to start over and find a new beginning.

For more than a decade, Silicon Valley’s technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie.

Robots after the “Like” Button

Now Silicon Valley has found its next shiny new thing. And it does not have a “Like” button.

The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

The shift was evident in a Lowe’s home improvement store here this month, when a prototype inventory checker developed by Bossa Nova Robotics silently glided through the aisles using computer vision to automatically perform a task that humans have done manually for centuries.

The robot, which was skilled enough to autonomously move out of the way of shoppers and avoid unexpected obstacles in the aisles, alerted people to its presence with soft birdsong chirps. Gliding down the middle of an aisle at a leisurely pace, it can recognize bar codes on shelves, and it uses a laser to detect which items are out of stock.

Silicon Valley’s financiers and entrepreneurs are digging into artificial intelligence with remarkable exuberance. The region now has at least 19 companies designing self-driving cars and trucks, up from a handful five years ago. There are also more than a half-dozen types of mobile robots, including robotic bellhops and aerial drones, being commercialized.

The Surge after the Static – The Social Way

There has been a slow trickle in investments in robotics all this while, and suddenly, there seem to be a dozen companies securing large investment rounds focusing on specific robotic niches. Funding in A.I. start-ups has increased more than fourfold to $681 million in 2015, from $145 million in 2011, according to the market research firm CB Insights. The firm estimates that new investments will reach $1.2 billion this year, up 76 percent from last year.

By contrast, funding for social media start-ups peaked in 2011 before plunging. That year, venture capital firms made 66 social media deals and pumped in $2.4 billion. So far this year, there have been just 10 social media investments, totaling $6.9 million, according to CB Insights. Last month, the professional social networking site LinkedIn was sold to Microsoft for $26.2 billion, underscoring that social media has become a mature market sector.

Even Silicon Valley’s biggest social media companies are now getting into artificial intelligence, as are other tech behemoths. Facebook is using A.I. to improve its products. Google will soon compete with Amazon’s Echo and Apple’s Siri, which are based on A.I., with a device that listens in the home, answers questions and places e-commerce orders. Satya Nadella, Microsoft’s chief executive, recently appeared at the Aspen Ideas Conference and called for a partnership between humans and artificial intelligence systems in which machines are designed to augment humans.

The auto industry has also set up camp in the valley to learn how to make cars that can do the driving for you. Both technology and car companies are making claims that increasingly powerful sensors and A.I. software will enable cars to drive themselves with the push of a button as soon as the end of this decade — despite recent Tesla crashes that have raised the question of how quickly human drivers will be completely replaced by the technology.

AI Outdoes the Silicon Valley Reset Trend

Silicon Valley’s new A.I. era underscores the region’s ability to opportunistically reinvent itself and quickly follow the latest tech trend. This is at the heart of the region’s culture that goes all the way back to the Gold Rush. The valley is built on the idea that there is always a way to start over and find a new beginning.

The change spurred a rush for talent in A.I. that has become intense. It is unusual that the number of people trying to get the students to drop out of the class halfway through because now they know a little bit of this stuff is crazy. The valley’s tendency toward reinvention dates back to the region’s initial emergence from the ashes of a deep aerospace industry recession as a consumer-electronics manufacturing center producing memory chips, video games and digital watches in the mid-1970s. A malaise in the personal computing market in the early 1990s was followed by the World Wide Web and the global expansion of the consumer internet.

A decade later, in 2007, just as innovation in mobile phones seemed to be on the verge of moving away from Silicon Valley to Europe and Asia, Apple introduced the first iPhone, resetting the mobile communications marketplace and ensuring that the valley would — for at least another generation — remain the world’s innovation center.

In the most recent shift, the A.I. idea emerged first in Canada in the work of cognitive scientists and computer scientists like Geoffrey Hinton, Yoshua Bengio and Yann LeCun during the previous decade. The three helped pioneer a new approach to deep learning, a machine learning method that is highly effective for pattern recognition challenges like vision and speech. Modeled on a general understanding of how the human brain works, it has helped technologists make rapid progress in a wide range of A.I. fields.

The Road Ahead

How far the A.I. boom will go is hotly debated. For some technologists, today’s technical advances are laying the groundwork for truly brilliant machines that will soon have human-level intelligence. Yet Silicon Valley has faced false starts with A.I. before. During the 1980s, an earlier generation of entrepreneurs also believed that artificial intelligence was the wave of the future, leading to a flurry of start-ups. Their products offered little business value at the time, and so the commercial commercial enthusiasm ended in disappointment, leading to a period now referred to as the “A.I. Winter.” The current resurgence will not fall short this time, and the economic potential in terms of new efficiency and new applications is strong.

One might be tempted to think we are living in a startup bubble, with investors being largely optimistic about startups and investing millions of dollars in them, with many startups crossing the billion dollar valuation on a regular basis. But managing a startup is tough, with almost unreal targets set in between funding rounds. The founders need to, at all times, be focused on the direction in which they need to head, and be sure of the selective performance indicators that they need to keep watch of. Creating data has become easy at current times. Though acquiring data from multiple sources has its potential benefits, but for a company at its seminal stage, dealing with multiple KPIs is a huge risk. Startups can easily get side-tracked by following the wrong KPI. In an ideal scenario, startups should keep only one performance indicator and keep scaling up in that direction before achieving a milestone and involving others in the development plan. With a large number of startups around, existence of a red ocean, and ample amount of data giving number of insights and scope for strategies, implementing analytics can be a sure shot way to keep startups focused on the optimal way to scale up, and in extension create organic buzz and visibility to scale further. Analytics has the capacity to point out which should be the root nerve of a startup and how to scale further in that direction. The seminal stages of creating a high visibility in the market and expansion of a startup involves a repeated cycle of – Building and improving of the Core Competency, and measuring the effect in KPIs and increase in adoption by customers. Let’s see where and how analytics can be optimally implemented for these scenarios.

Building Core Competency

The starting point for understanding core competences is understanding that businesses need to have something that customers uniquely value if they’re to make good profits. “Me too” businesses (with nothing unique to distinguish them from their competition) are doomed to compete on price: the only thing they can do to make themselves the customer’s top choice is drop price. And as other “me too” businesses do the same, profit margins become thinner and thinner.

This is why there’s such an emphasis on building and selling USPs (Unique Selling Points) in business. If you’re able to offer something uniquely good, customers will want to choose your products and will be willing to pay more for them. Here are three ways to turn analytics insights into actions that make your company more competitive:

Gain control along with visibility of patterns

People often use analytics to understand what’s already happened, but don’t look beyond “what”, to ask “why.” By understanding why certain patterns emerge in your data, you gain greater visibility and control over what’s happening right now.

For example, when you understand why certain factors affect your margins, your sales team is better able to address underperforming products and customers, identify potential revenue opportunities and design more optimal coverage models for your reps.

Put analytics in the right functional areas to drive change

To get results, you need a way to deliver analytical information to sales reps at the product and account level. This empowers reps to negotiate from an informed position and use data to have strategic conversations with customers.

Also, when reps have good access to customer analytics, they’re better able to invest coverage resources in high-quality leads. It helps them to identify opportunities with large value and position sales offers in the context of a dynamic market. For example, if there’s a lot of variability in a commodity and price wars break out, you want to quickly reposition your offer in relation to that dynamic market.

Build an ecosystem

To get the best results from your analytics, you need the ability to monitor what’s happening and use that data to adapt. As you build this process into your company’s DNA, constantly evaluate the criteria you’re using to ensure they stay relevant: Are you looking at the right variables and assessing the marketplace effectively? By maintaining the quality of this information, you’re developing a competitive advantage through pricing and sales sophistication.

Measuring Business Traction

Traction Analytics

Analytics helps your business determine what is working well, and what needs to be improved. We can always go off of a hunch, but the real power comes when we know the hard data behind our marketing or business management efforts, and can make informed decisions that improve our business over and over. Seasoned entrepreneurs know just how important analytics are in growing your business. Without a serious analytics strategy, you are simply relying on hope and luck to grow your company.

In a startup you are constantly under pressure and have way too many distractions. Having a set of metrics that you watch & that you feel are the key drivers of your success helps keep clarity. And the more public you can make your goals for these key metrics the better. Make them widely available inside the company and share your most important goals with your board. Transparency of goals drives performance because it creates both a commitment and a sense of urgency.

If you don’t have a stability goal stated for the company and if you don’t regularly measure how you’re doing against this goal you won’t have your resources focused on the right priorities in the company.

Most companies have some measurements, but I would argue that people often measure the wrong stuff, measure with the wrong precision. The best way is to start by asking yourself at management team level: what are our company objectives and how do we best measure them? Because it can be hard to define or agree company objectives at an early stage I believe most people avoid them.

Customer Acquisition

At the highest level you’ll obviously want to track how many customers you’re adding every month (and for some businesses that have hit scale this is measured on a daily basis). If you can break this down by channel that you’ve acquired them from this is obviously better.

How many additions came through organic SEO? How many through affiliate deals? How many through SEM? Do you have a customer referral program? If so, make sure you can track which leads come from this. Measuring viral adoption is obviously important.

Usually you have a catch-all bucket for “direct” or similar that often came through PR or word-of-mouth.

If you have multiple versions of your product, how many are web vs. mobile? How do the mobile customers break down by device type?

The next step after measuring the customers you’re adding is to add the “cost to acquire” by channel. This is important because it will later tell you whether you have a scalable business or not. In the early phases if you can’t acquire customers cost effectively enough you’ll need to diagnose why and how to fix it.

The Final Question of Scalability

The repeated cycles of Building and re-engineering and Core Competency and Measuring the Market environment effects takes the startups further and further into the final stages of having a scalable model. Like I mentioned at the beginning, there could be n number of directions a startup can head towards, as many as there are number of significant KPIs that need to be improved. But finding the right nerve and chasing the wrong performance indicator is the difference between ending up with a scalable business model and ending up with a marginally incremental model. In this current age of every changing topography of the market with disruptive ideas entering and washing off many hopeful businesses, only having an optimal analytics solution to track their locus can make sure startups sustain and succeed.