In several forums on digital transformations that I attended in the last two years, I would ask the panelists on whether they are incorporating Generation Z millennials into their product development strategy. In such forums you never get a yes or no answer and that is understandably ok, but the silence prior to saying anything makes it clear. It takes an effort to think about it when it should not be. Digital first or mobile first strategy should begin by clustering customers (and employees because they can influence your customers) by generation (silent gen, boomers, X, Y, and Z). Spend more research in determining differences in behavior toward your current or future products. The Z generation could ultimately drop your product altogether, so it important to maintain such outlook and strategize accordingly.
The corporate world should seriously embrace Generation Z into their workforce. Those born after 1997 are entering the workforce this year. Their approach in IT is to build applications with immediate real-world implications. Check what two GenZ engineers Nisha and Clarisse have to say at a HackerRank interview. Also two completely unrelated companies, BBVA Compass, a financial bank, and Skratch, a platform for teenagers, partnered together in Dallas to let teenagers use a mobile app to make money from community activities such as scooping ice cream at school fairs and teaching kids how to mix music like DJs. (Read more about it.)
Generation Z is on track to be the best educated and most diverse generation yet. (Check Pew Research article). 48% of them are racial or ethnic minorities and have similar liberal-leaning political as well as social views as Millennials (born between 1980–1995). (See Pew Research article.) However, similar to Nisha and Clarisse, Generation Z has its take on society and the workplace. SalesForce updated their blog post on “Millennials vs. Gen Z: How Are They Different?“. I quote from the article:
More millennials than Gen Zers will pay extra for customer experience
Gen Z sets a higher bar for expecting innovation from companies
Gen Z is less likely than the millennial generation to trust companies — but can be swayed
Gen Z is pragmatic; millennials are idealistic
Gen Z focuses on saving money; millennials are more focused on the experience
Millennials liked authenticity, but Gen Z takes it to a new level
Gen Z prefers in-store shopping; millennials shop online
Millennials cozy up to brands; Gen Z wants to be independently themselves
I recommend to check the SalesForce article since each of the bullets above is described in detail.
I think that for companies to be more successful in the digital world, it is essential that their products and services are not assuming all generation of customers (and employees) as one or as merely dividing in half between old vs. new. Having a clear understanding of the differences between millennial and post-millennials who is about to form the largest workforce can make a huge difference in not only companies’ bottom line but for the positive social change across the world. The latter is what all generations should be caring about except that Gen Z is actively caring more about it than everyone else.
I believe that an effective strategy for b2b (business to business) companies to innovate and grow is by powering their talented resources as if they are b2c (business to consumer) clients. That’s because the current generation of talents, millennials and post millennials, are digital consumers armed with the power of social networking and digital-everything. If b2b companies makes them feel cool and hype, the products they develop will be cool and hype, and the customers, young or old, could double down on such businesses because such companies are meshing modern and trustworthy professional experience. The only caveat is that b2b companies must have an organic feeling of doing this by seriously perfecting new talent acquisition and more seriously transforming their products and services quickly. Anyway, saying it versus actually doing it is not the same.
It was a pleasure and honor to represent Thomson Reuters at the panel on “scaling without stagnating” as part of corporate innovation. The event was hosted by Capital One and sponsored by Dallas Innovates as part of #dallasstartupweek. The panelists Dalia Powers (CBRE VP/CIO & panelist moderator), Sterling Mah Ingui (Head of Go To Markets Fidelity Labs), Scott Emmons (The Current Global CTO), Sean Minter (AmplifAI CEO), Charlie Lass (MIT investor) and myself Tarek Hoteit (Thomson Reuters Labs) took turns discussing leadership, people, organization, process/change management, and technology to support innovation in the corporate world. For me it was also an opportunity to let startups in Dallas to know about Thomson Reuters, Thomson Reuters Labs (http://labs.tr.com), and our community engagements in Dallas. I even shared my personal journey on a major transformation of a product as part of corporate innovation hoping to encourage everyone not to give up and do the same and more. We also answered questions from the audience such as how startups can interact with corporate (my answer: persistence is key but if someone from corporate is ignoring you, find another contact. Don’t give up)
Scott Adams, creator of Dilbert and computer game software pioneer, once said “man is a game-playing animal, and a computer is another way to play games”. The breakthrough in personal computer also started with games thanks to the homebrew nerds that collaborated together in the late 70s. I think if we maintain a philosophy of what we do for our customers in the enterprise is with a game playing mindset where the winner must always be the customer and the goal is to ensure customers are first then it would be a win win for everyone – the customer, the business, and us.
Last Thursday we presented “Applied AI/ML in the Workplace – Geek Food for Thought” at the University of Texas in Austin Computer Science department. Thomson Reuters is one of the Friends of the University of Texas at Austin that gives students an excellent opportunity to engage with the industry and learn more about companies that offers internships or job opportunities.
The speakers were me and Katherine Li, data scientist in my team. Special thanks to our co-workers and UT Austin alumni, Cameron Humphries, Director of software engineering, and Matthew Hudson, software engineer at Thomson Reuters. Also special thank you to Jennifer Green, senior talent acquisition partner in Thomson Reuters HR, and Ana Lozano, events program coordinator at UT Austin who helped set up the talk. More importantly, thank you UT Austin students for attending the event knowing that we missed more students because of conflicting class schedule and mid term exams.
I first talked about Thomson Reuters the company with a 100-year history, a global company, and its top-notch technology and careers development programs. I ran a video of our CEO and president, Jim Smith, explaining what makes Thomson Reuters Thomson Reuters. I then highlighted who are founding fathers of Thomson Reuters, beginning with Paul Reuter who founded Reuters News in 1851 and Roy Herbert Thomson who founded the company in the 1930’s which later became known as Thomson Corporation. Both companies later merged in 2008 and became Thomson Reuters. I hope to have made my point to the young audience that the Thomson Reuters founding fathers, Paul Reuter, who pioneered telegraphy and news reporting starting with pigeon posts, and Roy Herbert Thomson, First Baron Thomson of Fleet, were both entrepreneurs at a similar age as them.
I then provided an overview of Thomson Reuters Labs and listed some of the key innovative products including the latest WestLaw Edge, the most advanced legal research platform ever. I then moved to talk about AI and ran a video for our TR Labs CTO, Mona Vernon, speaking to The Economist early this year about AI and machine learning revolution. That was a great segue way to the main topic of the presentation, and that is applied AI in the workplace.
Through a couple of slides, I tried to make the point that students in the field of machine learning and artificial intelligence need to consider applying existing algorithms for their projects or their next start-up idea instead of building everything from scratch. It is quite understandable that students need to understand or even seek too contribute to the advancement of the core algorithms in the artificial intelligence. That is great and is very important but, unfortunately, it does not always lead to the next innovation or the next best product out there. The markets are hungry for applying artificial intelligence in the quickest time possible and in all the different ways that would have a societal impact. To illustrate the point, Katherine Li and I showcased four projects that leverage machine learning and natural language processing algorithms. We managed to get the applications working in a short time because we leveraged available cloud-based solutions notably Amazon AWS and Google Cloud and added our code for the projects. By spending less time building machine learning algorithms, we were able to focus more on the ideas and tie the different components into working prototypes.
You can check the presentation on this DropBox link (note: please download the deck and run the slides in presentation mode so that you can access the videos)
Looking forward to presenting “Applied AI/ML in the Workplace – From Labs to Product, Real Use Cases at Thomson Reuters” at The University of Texas at Austin computer science department tomorrow. We will highlight some cool products that recently came out of the Thomson Reuters Labs and showcase how easy, beneficial, and cheaper nowadays for students to power their software with AI/ML algorithms. Check the event details
I love my University of Texas in Dallas MIS Club audience! I started with running videos of key events in the history of data science and AI since the 60s. I never imagined that I would show Charlie Chaplin in the 80s IBM PC commercial or talk about the first desktop computer of the 60s, the Olivetti Programma 1, but I did :). I then talked on how the Python programming is a common instrument for both types of data scientists, the analytical and the AI product building. I promoted Jupyter Labs over Jupyter notebooks and encouraged the audience to leverage cloud notebooks using Google Collab. Then we went onto the cool stuff that I prepared for the event. I showed case my quick and easy implementation of building a Twitter sentiment analysis product with Python, Tweepy APIs, Django, Google Cloud NLP, and Docker containers. I then walked the audience in how I built a production-ready customers reviews rating model in less than 4 hours using Google AutoML for language processing and a Kaggle dataset that I found through Google new dataset search engine. After that, we had fun recognizing objects in the auditorium with Amazon DeepLens camera after I deployed a pretrained neural network model for object detection. Time went so fast when it is all love for computers. #loveoflearning