Hiring For The AI (Artificial Intelligence) Revolution — Part II

For a recent Forbes.com post, I wrote about the required skillsets for hiring AI talent. No doubt, they are quite extensive — and in high demand.

Consider the following from Udacity:

“We’ve seen a tremendous rise in interest and enrollment in AI and machine learning, not just year over year but month over month as well. From 2017 to 2018, we saw over 30% growth in demand for courses on AI and machine learning. In 2018, we saw an even more significant rise with a 70% increase in demand for AI and machine learning courses. We anticipate interest to continue to grow month over month in 2019.”

Despite all this, when hiring AI people, you will still need to do your own training. And it must be ongoing. If not, there is a big risk of failure with a new AI hire.

So let’s see how various top companies are handling training:

Ohad Barnoy,VP of Customer Success, Kryon Systems:

Our AI developers start with an in-depth training itinerary in order to gain a deep understanding of our platforms. They do this via our home-grown on-line Kryon Academy, a program that helps further AI training in parallel with on-the-job training. The developer is assigned a three-week course in each one of our development pods and with QA.

Chris Hausler, Data Science Manager, Zendesk:

Research and technology in AI is moving so quickly that constant learning and upskilling is required to keep up with the state-of-the-art and do your job well. At Zendesk, we run a weekly paper club where we discuss emerging research related to our work and have frequent “lab days” where the team has time to experiment with new ideas.

From Atif Kureishy, Global VP, Emerging Practices at Teradata:

Though more and more people are retooling their skillsets by acquiring deep learning knowledge through avenues like massive open online courses (MOOCs) or Kaggle, it is rare to find people who can do it in practice – and this difference is important. The classroom or competitions are certainly a step in the right direction, but it does not replace real-life experience.

Organizations should deploy and rotate their AI teams across various business units to gain exposure and understand challenges that the line of business is facing in building AI capabilities. This will enable experiential knowledge that can be brought together in a Center of Excellence but carries forward experiences from across their Enterprise.

Guy Caspi, CEO and co-founder at Deep Instinct:

At Deep Instinct, we focus our training primarily on two areas: Comprehensive understanding of deep learning, machine learning and big data, plus one additional area: the domain our product is in. For instance, our cybersecurity experts are consistently sharing their knowledge with our deep learning experts during the training process. The reason is that a deep (or machine) learning expert who is saturated with knowledge specific to the domain (in our case cybersecurity) during training will operate more effectively and be better adapted to real-world use cases.

Yogesh Patel, CTO & Head of AI Research, Callsign:

The line between Data Engineers, Software Engineers and Data Scientists is blurring when it comes to big data. There is a clear pull towards the latter, with more Data Engineers and Software Engineers seeking to become Data Scientists. With the introduction of deep learning, there is less and less need to spend huge amounts of time dealing with data exploration, data cleansing and feature engineering — at least in theory. Correspondingly, we are seeing more people claiming to be Data Scientists, but who are really just applying a brute force approach to machine learning.

Furthermore, we have training companies claiming that no prior knowledge in data curation is required and that no background in statistics is required. While that may be true in some domains, in the domain of cybersecurity we need more people with a solid understanding of the domain, as well as data science concepts. This means understanding the meaning and statistical properties and relationships between data attributes across a variety of data sources. It also means understanding how those data attributes and data sources might impact a given algorithm, especially when dealing with issues such as the imbalanced classification problem. For example, for the task of credit fraud detection, it means having an intuitive grasp about how, when and where a given transaction type occurs — a prerequisite for formulating and testing experimental hypotheses. In the same example, it also means understanding exactly how a given classification algorithm might be impacted when few to no examples of a given transaction type are available, and tuning or adapting the classification algorithm as necessary.

Alex Spinelli, CTO, LivePerson:

Managers and leaders must learn the concepts. They must learn what is and is not an applicable use of AI.

For example, AI is powered by data and examples. Problems that have limited history are often not good examples of ones easily solved by AI tools. This is referred to as the cold start problem.

Outputs from AI are not always predictable. This means that the linear nature of product design and workflows will change. It is not easy to reverse engineer why an AI system provided a specific answer. Another critical component to the training process is to develop new skills on product design that leverages AI. Product designers and leaders must understand statistics and probability in new ways.

Corey Berkey, Director of Human Resources, JazzHR:

Many companies are investing in training their workers to ensure they are staying current with technology and advancements in the industry. While math and computer technology serve as the backbone of AI-focused roles, continuing education in the field is a must. Many online learning solutions today offer a variety of AI-related certifications from top-tier universities to help workers expand their knowledge in areas such as programming, machine learning, graphical modeling, and advanced mathematics. It’s critical that companies focus on providing development opportunities these transformative hires so they are able to fine-tune their skills and learn best practices from peers.

Hiring For The AI (Artificial Intelligence) Revolution – Part I

In the coming years, Artificial Intelligence (AI) is likely to be strategic for a myriad of industries. But there is a major challenge: recruiting. Simply put, it can be extremely tough to identify the right people who can leverage the technology (even worse, there is a fierce war for AI talent in Silicon Valley).

To be successful, it’s essential for companies to understand what are the key skillsets required (and yes, they are evolving). So let’s take a look:

Dan O’Connell, Chief Strategy Officer & Board Member, Dialpad:

I think it’s critical for “AI” teams (natural language processing, machine learning, etc.) to have a mix of backgrounds — hiring Ph.D’s and academics who are thinking about and building the latest innovations, but combining that with individuals who have worked in a business environment and know how to code, ship product and are used to the cadence of a start-up or technology company. You can’t go all academic, and you can’t go all first-hand experience. We found the mix to be important in both building models, designing features, and bringing things to market.

Sofus Macskassy, VP of Data Science, HackerRank:

Many don’t realize that you do not need a large team of deep learning experts to integrate AI in your business. A few experts, with a supporting staff of research engineers, product engineers and product managers can get the job done. There is much more to AI than deep learning, and businesses need to find a candidate with strong machine learning fundamentals. Many candidates with a theoretical background in machine learning have the tools they need to learn the job. Training AI talent on the specific needs for your business is cheaper and faster than training someone to be an AI expert. Hire strong research engineers that can take academic papers and equations and turn them into fast code. These are often engineers with a technical foundation in computer science, physics or electrical engineering. Together with your AI expert(s), they will make a powerful AI team. Add a product manager to tell them what product to build and you have a powerhouse.

Chris Hausler, Data Science Manager at Zendesk:

Any person working in the field of AI needs to be able to code and have solid mathematical and statistical skills. It’s a misnomer that you need a PhD to work in AI, but genuine curiosity and an eagerness to learn will help you keep up with this fast moving field. Having the skills to implement and validate your own experimental ideas is a huge advantage.

We have found success hiring people from disciplines that focus on experimentation and problem solving. The Data Science team at Zendesk has a diverse background with people coming from Genetics, Economics, Pharmacy, Neuroscience, Computer Science and Machine Learning to name a few.

Atif Kureishy, Global VP, Emerging Practices at Teradata:

One could argue that the skills for AI are similar to data science; math, computer science and domain expertise, but the truth is that AI models are predicated on two things, automation and data – and lots of it.

Increasing sophistication in automating key aspects of building, training and deploying AI models (such as model selection, feature representation, hyper parameter tuning, etc.) mean the skillset needed must be focused on model lifecycle and model risk management principles to ensure model trust, transparency, safety and stability. Typically, these are spread across roles in organizations that touch on policy, regulation, ethics, technology and data science. But these will need to converge to build AI at scale.

Guy Caspi, CEO and co-founder at Deep Instinct:

People who have strong academic backgrounds sometimes lean towards one of two directions: either they cannot leave a project until it’s perfect, often missing important deadlines – or the opposite: they’re satisfied with basic academic-level standards that may not meet an organization’s production requirements. We search out people who have both a strong academic background, but also have a strong product/operational inclination.

How To Get Customers To Renew…And Expand Their Accounts

Tien Tzuo is one of the pioneers of the SaaS (software-as-a-service) industry. He was employee No. 11 at Salesforce.com, where he became the Chief Marketing Officer. He would then co-found Zuora, which operates a leading subscription platform.

Yet while the SaaS model is powerful – and has transformed companies like Microsoft and Adobe – it does demand much planning and organization. This is why Tien crated the PADRE operation model, a workflow to help companies better manage the process. And an important part of this is renewals:

“Acquiring new subscribers is critical, but in the Subscription Economy the vast majority of customer transactions consist of changes to existing subscriptions: renewals, suspensions, add-ons, upgrades, terminations, etc.”

The bottom line is that implementing a strong system for renewals not only helps reduce churn but also allows for the opportunity for higher growth, in terms of expansion of existing accounts and upsells.

So what can you do to improve the process for your own company? Well, I reached out to another top player in the SaaS field, Zendesk. The company, which has a market cap of about $7 billion, provides customer service and engagement cloud services. During the latest quarter, revenues jumped by 38% to $154.8 million.

“The renewals process starts with one key question: who is responsible for the renewal?” said Jaimie Buss, who is the VP of Sales for Zendesk. “The answer depends on what type of product you sell. With top-down large enterprise sales, a more senior account manager will most likely need to handle the renewal, as the objective of those renewals will likely be to extend the contract to multiple years, products, and include contract restructuring and heavy negotiation. For the mid-market, renewal objectives are a bit more straightforward; that is, to extend the contract term, minimize contraction by selling other products, and reducing or removing the discount offered at the initial sale. In this case, renewals could be supported by the Success organization or a renewals specialist.”

Regardless, the key is that you need to be proactive.  Waiting until a few days before the contract expires can mean losing business.

Here’s what Jaimie recommends:

  • 90-61 days before the renewal: You should begin the initial engagement. First of all, you want to confirm that the primary contact is still with the organization. Next, have a discussion about pricing, discount reductions and term length options. Then once you gather all the feedback, you should evaluate the potential growth of the account and the churn risk. “Churn risks should immediately be flagged and you should have all hands on deck: sales, success and sales engineers,” says Jamie.
  • 60-31 days before the renewal: This is when you get down to brass tacks. In other words, you want to confirm the contract term, pricing, billing frequency, and payment type. You will also want to confirm the paper process and timing of signatures with the customer.
  • 30-0 days before the renewal: The order should be processed and signed. “Once closed, a hand-off back to success or sales should occur if the renewal is driven by a renewals specialist,” said Jamie.

During this process, there are definitely some potential issues to keep in mind. For example, if you have an “auto-renew” option in the terms and conditions, the renewal specialist or sales person needs to coordinate with the collections team. If not, there’s the risk that a customer may receive an invoice during the contract negotiation! No doubt, this could be a deal killer.

Finally, there needs to be a clear-cut incentive structure for those people who are responsible for renewals.  According to Jamie, it must be focused on expansion of bookings.  And even if a customer does not want to add new subscribers, there should still be incentives to increase the term length, improve the billing frequency and reduce the discounts.

Cool AI Highlights At CES

AI was definitely the dominant theme at CES this week. According to a keynote from LG’s president and CTO, Dr. I.P. Park, this technology is “an opportunity of our lifetime to open the next chapter in … human progress.”

Wow! Yes, it’s heady stuff. But then again, AI really is becoming pervasive. Consider that Amazon.com recently announced that more than 100 million Alexa devices have been sold.

OK then, for CES – which, by the way, had about 180,000 attendees and more than 2.9 million net square feet of exhibit space in Las Vegas — what were some of the standout innovations? Let’s take a look:

3D Tracking: Intel and Alibaba announced a partnership to allow for real-time tracking of athletes. The technology, which is based on AI-capable Intel Xeon Scalable processors, creates a 3D mesh of a person that captures real-time biomechanical data. Note there is no need for the athlete to wear any sensors. Essentially, the AI and computer vision systems will process the digital data.  Oh, and Intel and Alibaba will showcase the technology at next year’s Tokyo Olympic Games.

Intel executive vice president and general manager of  the Data Center Group, Navin Shenoy, notes: “This technology has incredible potential as an athlete training tool and is expected to be a game-changer for the way fans experience the Games, creating an entirely new way for broadcasters to analyze, dissect and reexamine highlights during instant replays.”

AI For Your Mouth: Yes, Oral-B showcased its latest electric toothbrush, called Genius X. As the name implies, it does have whiz-bang AI systems built in. They are focused on tracking a person’s brushing styles so as to provide personalized feedback. The device will hit the markets in September.

Connected Bathroom: Baracoda Group Company thinks there is lots of opportunity here. This is why it has leveraged its CareOS platform – which uses AI, Augmented Reality (AR) and 4D, facial/object recognition – to create a start mirror. Called Artemis, it has quite a few interesting features. Just few of them include the following:

  • Visual Acuity Test: This tracks the changes in your vision.
  • AR Virtual Try-on: You can digitally apply beauty products like lipstick and eyeliners.
  • AR Tutorials: You can get coaching on hairstyles, makeup and so on.
  • Voice Commands: You can talk to the mirror to change the lights, control the mirror and adjust the shower settings.

Artemis will hit the market sometime in the second half of this year. However, the device will not be cheap – retailing at $20,000.

Cuddly Robot: AI is key for many robots.  Yet there are problems.  After all, robots are usually far from lifelike because of their stiff movements and metallic exteriors.

But Groove X takes a different tact. The company has developed Lovot, which looks like a teddy bear. Think of it as, well, a replacement for your pet.

There is quite a bit of engineering inside the Lovot, which has more than 50 sensors and uses deep learning (Groove X calls it Emotional Robotics).  Basically, the focus is to bring the power of love to machines.

As for when the Lovot will launch, it will be some time in 2020. The price tag will also be about $3,000.

Voice Identity: There has continued to be lots of innovation in this category. For example, at CES Pindrop launched a voice identity platform for IoT, voice assistants, smart homes/offices and connected cars. This technology means that you no longer have to use pin codes to gain access to your accounts or devices. Instead, Pindrop will be able to instantly provide authentication when you start to talk.

Rebranding Your Company: Why And How To Do It

This week WeWork announced a rebranding. The company will now be referred to as the We Company. This also has come along with a $2 billion investment from SoftBank Group.

“The We Company is an ambitious strategy to broaden the company’s aspirations from places to people,” said John Gerzema, who is the CEO of The Harris Poll. “And it’s a sound move because WeWork remains as a famous sub-brand the same way Google is to Alphabet. This strategic evolution should please both investors and customers.”

Yet it is still risky. “A rebrand tends to fail when the name has no discernable relationship to the companies beneath the new umbrella,” said Brady Donnelly, who is the Executive Director of Consumer Experience at FIG. “I think the most famous recent example is probably when Tribune Publishing changed its name to Tronc, which was an awful word. Then, after being ridiculed, management changed it back. The reaction was made worse by the fact that the business itself was failing, and the name change just seemed like an attention grab.”

Why Should A Company Do A Rebrand?

According to Joe Walsh, who is a senior partner at Finn Partners, there are three main reasons for a rebrand:

  • Your company and/or its markets have changed and the current brand does not reflect who the company is or aims to become.
  • There is a merger. In fact, this is the most common reason.
  • The company’s suit of clothes is out of date, unbecoming or otherwise out of step with the times.

An example is Logitech in 2015, when the company was in the midst of a turnaround and needed to refresh its brand. “Part of our turnaround was putting design at the core of every product we created, and it was critical that our brand communicated that shift to consumers,” said Alastair Curtis, who is the chief design officer for Logitech. “Through the rebranding process, we re-imagined our logo and established a new logomark – Logi – with bold colors and an updated look. By evaluating every aspect of our brand, its history, and where we wanted the company to go, we were able to align our business turnaround with the bold transformation of our brand that consumers know today.”

Planning For The Rebrand

A rebrand should not be done hastily. It’s just too important of a decision.

To improve the odds of success, it’s a good idea to conduct a brand audit. This means having surveys, customer interviews, brand sentiment analysis and comparisons of NetPromoter Scores.  Yet it’s also critical that the decisionmaking not get bogged down either.  Yes, it can be a tough balance to strike.

Consider Bombfell, which went through a rebrand in 2011. The main reason was that the company had undergone a transformation of its service offerings and client base over the years.

“We first conducted several valuable exercises across the entire organization to codify our core values,” said Sandro Roco, who is the Director of Strategic Initiatives at Bombfell. “We wanted to hear directly from our employees and in their voice what made Bombfell unique. That said, while it was important to include as many voices as we could early in the process, we learned the hard way that the decision on the final brand direction had to be left to just a few key decision makers. For us — and we imagine most smaller companies — this ultimately came from the founders.”

All in all, it was the right choice, as the rebrand was a key for growth.

“Our belief is that the strongest brands today stake out a unique position and rally the company, customers and other evangelizers behind that voice,” said Sandro. “Unfortunately, while well-meaning, we found the rule-by-consensus approach too often smoothed out the edges necessary to creating a distinct brand.”

Pitching A VC: How To Size The Market Opportunity

When it comes to obtaining venture capital, you must have a solid understanding of the market opportunity. The main reason is that a VC firm needs a few deals to generate substantial returns, so as to offset the inevitable losers.

This focus on a large market opportunity has become even more important in recent years, as the the funds have gotten much larger.

“The size of the market is essential for your pitch,” said Kara Sweeney Egan, who is a principal at Emergence. The firm is a leading investor in early stage enterprise companies. Some of its breakout deals include Salesforce.com and Veeva Systems.

To estimate the market size, Kara recommends that you start with a “top-down” approach. Consider that there are numerous research firms, such as IDC, Forrester and Gartner, that publish analysis on markets. From these sources, you can easily come up with the TAM (Total Addressable Market). For example, a quick Google search shows that the CRM category comes to nearly $40 billion (the estimate is from Gartner).

But of course, you should go deeper then this — that is, you need to use a “bottoms-up” approach as will. At the heart of this is calculating the Total Obtainable Market (TOM), which is the total number of target customers multiplied by the average each will pay for your product.

Let’s take an example:  Suppose you are targeting accountants in the US. By looking up Labor Department data, you’ll see there are 660,000 CPAs. If your product sells for $1,000 per year, then your TOM would be $660 million.

“Your TOM should range from $500 million to $1 billion,” said Kara. “If it is smaller, then you’ll need a clear plan to expand that market.”

Now there is often lots of tweaking of the numbers. And it will continue over time, as you learn more about your market.

This was the case with NGINX, which offers a suite of technologies for developing and delivering modern applications. “We had the ability to start with a very broad footprint of open source users,” said Gus Robertson, who is the CEO. “When we founded the company, we had about 35 million websites already running the software. We applied assumptions around how many companies those sites represented, and applied some assumptions around the percentage that would buy a commercial offering. As NGINX matured, it became clear exactly what products and solutions our customers valued. We built specific offerings with features that were above and beyond the open source features — a proven open core business model. Some use cases remain adequate for our open source project; other use cases favor moving to our commercial product.”

But what if there is not much data available? This is actually common. But there are creative approaches to deal with this.

Just look at NuORDER, which operates a B2B wholesale ecommerce platform.

“To size the market opportunity,” said Heath Wells, who is the co-founder and co-CEO of the company, “we needed to get a sense of how many brands there are that sell products to retailers. But this is data that isn’t available through 3rd party research or government stats. Instead we used two other data sources to help us size the market.”

First, Health visited the websites of the major retailers in each of the categories his company wanted to target. “From this, we were able to determine the number of brands who wholesale,” he said. “Next, we honed in on the fact that brands have used tradeshows as a major means of finding and engaging lots of retail buyers. Thus, by looking at the number of brands sponsoring and attending major conferences – conferences such as MAGIC, Outdoor Retailer, The Running Event and others – we were able to gather another key data point in sizing the market.”

It definitely worked out. Since 2012, NuORDER has raised about $40 million.

The Big Picture

Just because there is a big market opportunity does not mean VCs will necessarily be interested. They also want to see that there are emerging megatrends, which should allow for the adoption of new technologies.

John Vrionis, a venture capitalist at Unusual Ventures, points out one pitch that did this extremely well.  “The co-founder of AppDynamics, Jyoti Bansal, had a simple picture that showed there’s a huge market for making sure apps run right,” he said. “It clearly showed that a tidal wave was approaching. With the growth in virtualization, the traditional approaches of monitoring would simply not work. Jyoti said he knew this because he was the lead architect of one of the top companies in the space. His compelling argument was that there would need to be a re-write of everything.”

It was definitely spot-on. In early 2017, AppDynamics would sell to Cisco for $3.7 billion.

For the most part, VCs want to get a sense of your thinking about the market dynamics, not just the numbers. “A well though-out view of the the market can help build excitement about your company, demonstrate your industry expertise, and highlight your key insights into your customers,” said Kara.

AI Wars: Will China Defeat The US?

This week’s plunge in Apple’s shares was another sign of the impact of the relations between the US and China. It does look like President Trump’s tariffs are taking a toll and that the tensions maybe lasting.

So this why Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, is so timely. He provides a detailed look at how China is poised to win one of the most important markets. Keep in mind that – according to a research report by PWC – AI is forecasted to add $15.7 trillion to global GDP by 2030. The main reason is that this technology is general purpose, having applications that span industries like healthcare, transportation, financial services, energy and so on. Some consider AI to be on par with what we saw with the revolution of electricity during the 20th century.

Lee certainly has the credentials to make convincing arguments. He got his Ph.D. in computer science from Carnegie Mellon University in 1988, where he focused on AI. Lee worked on leveraging concepts like Bayesian networks for games and voice recognition.

He would then move on to the corporate world. Some of the companies he worked for included Apple, Silicon Graphics, Microsoft and Google. As of now, he’s a venture capitalist with Sinovation Ventures, which is focused on AI opportunities in China.

The Book

Lee notes that China has AI fever, galvanized by heavy investments from the government and VCs.  Yet success is more than just about money.  Lee points out that there are four key factors for AI:  “abundant data, hungry entrepreneurs, AI scientists and an AI-friendly policy environment.”

In light of this, it’s actually easy to see why China is in a strong position to benefit.  After all, the ubiquity of smartphones has meant the accumulation of gigantic amounts of data. But China has also seen more usage of real-world applications, such as with bike-sharing, mobile payments and ride-hailing. Part of this has been due to massive private/public investments. But China’s willingness to be permissive with issues of privacy has been another big factor.
Data is essential for AI because it is required for developing sophisticated neural networks. This makes it possible to better understand language, recognize objects or come up with useful insights.

Once the data threshold is met, which seems to be the case with China (it is the largest producer in the world and the country has more than 700 million internet users), then there is less of a need for top-notch engineers. Lee writes: “Algorithms tuned by an average engineer can outperform those built by the world’s leading experts if the average engineer has access to far more data.”

What Now?

Now the US is not doomed to failure.  We still have many advantages. The university system is standout and there are mega companies that are pushing innovation (Google, Microsoft, Facebook, Amazon.com and Apple).

But much still needs to be done. According to Jason Tan, who is the co-founder and CEO of Sift Science (a company that uses AI to help online businesses prevent fraud and abuse in real time), he believes there needs to be the following:

  • Make much more investments in homegrown STEM talent.
  • Make it attractive for overseas STEM talent to remain in the US
  • Get more government support for entrepreneurship — creating workplace opportunities for that talent to be applied
  • Get more government support for AI research — so that we continue to push the frontier

No doubt, all these would make a difference.  But unfortunately, there has been little progress on these fronts.  So unless there is major change – and soon – Silicon Valley may no longer be the center of gravity in the next 10 years.