Fluid Talent and the SWAT team concept


This is an interesting article: The Industries That Are Being Disrupted the Most by Digital.

The idea that you bring in someone in a catalytic role to drive the change, and then hand over to hybrid managers, or emerging new positions, as the organisation matures.

One of the ideas that I had been discussing with ex-colleagues is the concept of a roving SWAT team or individuals, whose job is to look strategically at an organisation and focus on solving specific problems that are diagnosed. One of the KPIs is redundancy – a measure of their success is that new initiatives are sustainable and handed over smoothly to existing or newly appointed teams. Then they are off to solve the next problem.

The advantage of this is that you maximize individual capabilities. We expect employees to be all-rounders and perfect, when that is not the case. Employees who excel at catalytic roles are always on the hunt for the next problem to solve, and don’t necessarily handle routine and the day-to-day grind well. Or they can handle it, but get bored very easily, which makes retention of such top talent difficult. But you also need employees who are good at the day-to-day grind, to keep the business running, who may not be the best visionaries, but they put food on the table.

Having such an approach also reduces strain on teams who are often tasked with dual objectives of keeping the business going and innovating at the same time.

In the new economy, we have to deal with the concept of fluid talent, that increasingly you will have multi-talented employees who have skillsets that allow them to be applied in different contexts that cannot be bound by one title. This is where adaptability and learning agility are most critical in an employee.

If you’ve read all the way here, ask me about employee kites and kite-handlers when we next meet.


Chatbots in HR: Improving the Employee Experience


After months of trial and error in building my chatbot prototypes, I finally finished my thesis for my master’s in Digital Management from Hyper Island, titled “Chatbots in HR: Improving the Employee Experience.”

What have I found?

  1. There is currently a lot of excitement and hype around chatbots that outstrips what chatbots can actually do right now
  2. Nevertheless, due to this recent surge of interest in chatbots, the developments in the industry are moving at a extremely rapid pace, with different chatbot products being built every day, in different industry sectors. Features and functions are being added and improved on at lightning pace
  3. Based on the testing of the chatbot prototype that I built for this thesis, there is a lot of potential in using chatbots in HR to improve the employee experience.
  4. Chatbots can free up time from the HR team, and in the long run, even help the HR function to move up the value chain
  5. If designed well, HR chatbots can create significant cost savings due to time saved in completing HR tasks (by employees) and answering queries (by HR team)
  6. In addition to providing friendly, instant information for employees, other areas of value-add for chatbots in improving the employee experience could lie in:
    • Helping employees to avoid embarrassment, or maintain their personal reputation, when they need to ask certain types of questions
    • Providing anonymity when employees want to read up on sensitive employee/company policies
  7. The most important issues that need to be resolved to make a customised chatbot for employees is the back-end integration with HR enterprise software as well as clear parameters over employee privacy and data collection. Some enterprise software vendors have partnered chatbot developers to create solutions for their software
  8. However, even without customisation, chatbots can still provide significant value in generic information services
  9. As use of technology increases in HR, HR leaders need to review the skillsets required in their HR team to enable the function to thrive in a new digital age

Read the full thesis here: Chatbots in HR: Improving the Employee Experience


Chatbots in HR

This is a review of current literature on the application of chatbots to HR. It attempts to explore critical questions that HR leaders may ask when evaluating investments in HR chatbots. This is still a work-in-progress, and this blog post will be updated from time to time.

In what situations could chatbots be used in HR?

  • Carry out simple administrative tasks, freeing up HR’s time for more value added work
  • Customer service, where employees are the customers
  • Carry out repetitive or routine tasks / questions from employees, where answers can be automated, and reduce frustration for HR teams  (Bhaduri, 2016)
  • Provide 24/7 availability to answer employee queries
  • Improve speed of response to HR queries
  • Training  (Bhaduri, 2016) where individual (not group) learning is the focus

Aspects of HR that chatbots can be applied:

  1. Information service
  2. Recruitment
    1. To shortlist candidates (Bhaduri, 2016)
    2. Q&A facility for candidates (Sullivan, 2016). However, this may not be relevant for the communications agency environment, as much of the employment information on the company is typically considered confidential, due to competitive poaching, and would not be published on public forums. The highly customised nature of compensation and benefits in the industry also rule out the use of chatbots in this aspect as chatbots can only provide standardized responses
  3. Onboarding (Boulton, 2016) – use of a chatbot to contact new employees with onboarding information and links to the required HR forms, in place of a HR generalist
  4. Self-service registration / employee information update (Ames, 2016)
    • Benefits enrollment
    • Editing personal or family information

Considerations for implementing chatbots in HR:

  1. Deploy in current tools of communications that employees already use – Integrate into existing platforms of communication, rather than force employees to adopt another tool (Ames, 2016)
  2. Security of information – due to the nature of the function, exchanges with employees will involve confidential and/or personal data. Is the environment where the bot is hosted secure? Can it be integrated into an existing HR software pre-approved (security-wise) by the company? (Ames, 2016; Boulton, 2016)
  3. Data collection and privacy – what kind of data will the chatbot collect, and how does it impact privacy of employees, particularly when they may ask sensitive questions, or questions that may influence others’ perception of them? Will the questions asked be able to be directly traced to individual employees? (Maass, 2014a)

Some examples of organisations developing HR Chatbots

  • U.S. Army: Sgt. Star, commissioned by the US Army from NextIT, to help with recruitment efforts. Status: Launched since 2007. (Maass, 2014bNextit.com, 2016)
  • Intel: Ivy, developed by NextIt, is a virtual HR agent that uses a combination of natural language processing, artificial intelligence and optimized search hosted on their Intranet to help answer Intel employees’ questions about their pay, stock, benefits, or other HR programs (Ask Ivy, 2013, Boese, 2013)
  • Talla: Employee onboarding. Status: to be launched Oct 2016 (Talla, 2016; Knight, 2016)
  • ADP: HR task automation, such as sending a job to a prospective hire to alerting employees to use their accrued vacation time. Status: not available commercially yet (Boulton, 2016)
  • Aspect: Mila replaces the employee hotline for sick employees at overstock.com and allows change of work schedules. Status: available internally since July 2016 (Greenfield, 2016; Hobson, 2016Aspect.com, 2016)
  • RAMCO: HR chatbot to respond to queries and helping in tasks like checking leave status and calendar before applying for leave. (Sangani, 2016)
  • WIPRO: using chatbots to judge the mood of the employee. (Bhaduri, 2016); currently being trialled
  • FirstJob, Inc: Mya, the chatbot, vets and interviews job candidates as well as administering tests, providing application status updates and tips to candidates. Status: in private beta (Deutscher, 2016Trymya.io, 2016Dishman, 2016)

Now, how do I design a bot?

After my small success yesterday, I started thinking about what I need to do next to further my understanding of building a chatbot.

Of course, what I managed to do yesterday just skims the surface and there are a lot more chatbot functionalities that I need to learn to be able to build a protoype. However, what struck me, when I was writing the bot, was that I was learning how to execute, but I hadn’t learnt how to design one.

What is the logic that drives the design of a bot? Even if a bot had excellent functionality, it would still be useless (or not used) if the user experience is bad.

So, this will be an additional branch of reading that I will be looking to explore. Some initial resources that I’ve found:

  1. Twine
  2. Why does your chatbot suck?
  3. The chatbot design playbook
  4. When bots go bad: Common UX mistakes in chatbot design
  5. Principles of bot design
  6. Designing a Chatbot | UX Design Process | Case Study
  7. Chatbots made easy

I built a bit of bot!

I want to order screenshot

After a frustrating afternoon, I finally made a personal breakthrough.

I built a bit of bot!

I tried building a chatbot from scratch on my own using DIY blog posts as guides, but it was too hard because I don’t have programming knowledge, and those instructions start with setting up a server, when I was raring to have a go at building the bot itself.

So after hours of trying today, I decided to take a different approach and take a shot at using a chatbot toolkit. I chose to use wit.ai as it was recommended by a friend, and also the fact that Facebook bought it.

Following instructions from the wit.ai starter guide, I managed to write a simple yes-no response. On top of that, I also “trained” the bot to respond to variations of yes/no, such as “yeah”, “yup”, “nope”; similar to how a person might respond over WhatsApp or FB Messenger. This is the most basic of basics, but looking at where I started just a few weeks ago, with my fears and concerns, this is really a leap forward.

So here it is, a screencast of my efforts today:


Chatbots: The Basics


* I will be continually adding onto this blog post periodically

What is a Chatbot?

  • A chatbot is a service, powered by rules and sometimes artificial intelligence, that you interact with via a chat interface. (Schlicht, 2016)
  • Automated computer program that simulates online conversations with people to answer questions or perform tasks. (Knowledge@Wharton, 2016)
  • A chatbot system is a software program that interacts with users using natural language. Different terms have been used for a chatbot such as: machine conversation system, virtual agent, dialogue system, and chatterbot. The purpose of a chatbot system is to simulate a human conversation; the chatbot architecture integrates a language model and computational algorithms to emulate informal chat communication between a human user and a computer using natural language (Shawar & Atwell, 2007)

The opportunity for, and discussion around, Chatbots

  • People are using messenger apps, where chatbots reside, more than they are using social networks. (BI Intelligence, 2016)
  • Opportunity to reduce costs for companies. The chatbots envisioned by the tech industry combine artificial intelligence with voice recognition that relies on the way humans naturally speak. The goal is to create a situation where customers feel they are communicating with another human, rather than a piece of highly intelligent software, and in an environment that calls for little to no human operator intervention (Knowledge@Wharton, 2016). Indeed, research firm Gartner sees 33% of all customer service interactions as still needing a human intermediary by 2017,(Gartner.com, 2015) down from nearly 60% in 2014
  • In general, the aim of chatbot designers should be: to build tools that
    help people, facilitate their work, and their interaction with computers using natural
    language; but not to replace the human role totally, or imitate human conversation perfectly (Shawar & Atwell, 2007)
  • Bots are the beginning of micro apps on the backs of massive platforms which will lead to more focus and reach for startups and more delighted users (Batalion, 2015)
    • Spend less time in development, E.g. by building a microapp on FB Messenger allows you to build one experience (and do away with writing for different mobile phone sizes, or OS) and access FB user rich profiles instantly
    • Able to leverage richer UIs and multiple SaaS products to process input and focus more time on creating just enough of an experience to delight users
  • Attracts users to stay within the platform eco-system where the chatbot resides – messaging is now the new platform. (Newman, 2016; Bayerque, 2016; Raziano, 2016)
  • Increasing friction in getting consumers to download and use an app and quite costly as cost-per-install and paid acquisition marketing are increasing – the average global mobile user has = ~33 apps installed on his or her device and 12 apps used daily. 80% of the average global mobile user’s time is spent on 3 apps (Raziano, 2016)

Challenges surrounding chatbots

  • Hype: There is so much hype around the potential of chatbots, that the general public’s expectations of what chatbots can do will exceed the reality of what they can actually do (Hobson, 2016). The reality also is that not every bot needs to be sophisticated, and it will depend on the objective / outcome that the chatbot is built for. If it is meant as an informational service, there is very little point in building a bot to conduct a conversation with a user, when the bot is meant to be transaction-based
  • Developing a chatbot MVP (Minimum Viable Product): Software companies are used to developing a MVP, or a minimum standard version of the product that consumers would be willing to buy, to test in the market. However, for a good user experience, the product will likely need to have more accurate natural language processing and information before a MVP can be developed, which may mean that chatbots could require more capital than a traditional web or mobile app, where good frameworks are more commonly available (May, 2016)

What do consumers want in chatbots?

Based on a survey of 1,000 consumers in the UK in May 2016, the top perceived benefits of chatbots (MyClever Agency, 2016) was getting instant response and quick answers to simple questions. Interestingly, friendliness and approachability was not an important benefit for consumers, which indicates that customers want bots to enable efficient and accessible transactions. 


Barriers to chatbot adoption by consumers

Supporting that view is consumers’ feedback on barriers to chatbot usage, and their biggest concern was that chatbots would not understand their questions. Chatbots that were incapable of friendly ‘chat’ was not a barrier at all for respondents.


Types of Chatbots

  • Rule-based 
    • Only responds to very specific commands. If you say the wrong thing, it doesn’t know what you mean. Only as smart as you program it to be  (Schlicht, 2016)
  • Machine-learning
    • Understands language, not just commands. Gets continuously smarter as it learns from the conversations it has with people (Schlicht, 2016)

Who’s trying to attract/facilitate Chatbot developers? 

  • Google with Allo (Fulay and Adan, 2016), incorporating
    • Smart Reply – respond to messages without typing a single word. Learns over time and will show suggestions that are in the user’s style
    • Google Assistant – help to find information and complete tasks, e.g. book a dinner reservation, flight status
  • Facebook with bots for Messenger (Marcus, 2016Facebook Developers, 2016)
    • Send / Receive API – ability to send and receive text, images, and rich bubbles with Call To Actions (CTAs)
    • Generic Message Templates – structured messages with CTAs, horizontal scroll, urls, and postbacks.
    • Natural Language Assistanceusing the wit.ai Bot Engine to create conversational bots that can automatically chat with users
    • Tools to enable discovery of bots – plugins for websites, usernames and Messenger Codes, prominent search surface in Messenger, ability for Facebook News Feed ads to enable the opening of threads on Messenger, a new customer matching feature will allow messages that are usually sent through SMS to be sent on Messenger
  • Microsoft with Microsoft Bot Framework, a set of tools to help developers build artificial intelligence bots (Knowledge@Wharton, 2016), comprising (Foley, 2016):
  • Kore Bots Platform, that includes (Kore, 2016)
    • A Natural Language Processing engine 
    • Enterprise Administration and Security
    • Kore Bot Store – pre-loaded with more than 130+ ready-to-use enterprise and personal bots that perform thousands of tasks. Enterprises can also select, customize and create an approved collection of bots for their organization’s own private enterprise bot store.

History and development of Chatbots

  • Eliza, the first chatterbot ever coded, was then invented in 1966 by Joseph Weizenbaum. Eliza, using only 200 lines of code, imitated the language of a therapist. He intended ELIZA to be a  parody of human conversation, yet suddenly users were confiding their deepest thoughts in Eliza
  • What was made clear from these early inventions was that humans have a desire to communicate with technology in the same manner that we communicate with each other, but we simply lacked the technological knowledge for it to become a reality at that time
  • For more on the history of Chatbots:

Applications of Chatbots

Questions to ask when considering developing a Chatbot  (Knowledge@Wharton, 2016; Jalali, 2016

  • Strategic
    • How do they impact the customer experience/interaction?
    • How do they fit with the positioning of the brand?
    • How relevant is it to the business?
    • Have we evaluated the resources that need to be allocated?
    • What will the success metrics look like?
  • User Acquisition
    • How will users discover the bot?
  • User Experience / Functionality
    • How well do they work?
    • What will the UX interface be like?
    • How frequent will your bot updates be?
    • How will you create a “Minimum Viable Onboarding (MVO)”, i.e. show users how to interact with the bot and understand what the bot is good at, to set expectations on how “human” the bot will be?

Tips for Building Chatbots (Schlicht, 2016)

  1. Decide what problem your chatbot is going to solve
  2. Choose which platform your bot will live on (Facebook, Slack, etc)
  3. Set up a server to run your bot from
  4. Choose which service you will use to build your bot

Chatbot Toolkits

Other areas of reading to beef up this post:

  1. Application of chatbots in HR and its implications
  2. Who else is facilitating chatbot development (Slack, WeChat etc)
  3. Comparison of the top 5 chatbot toolkits
  4. Deeper evaluation of chatbots
  5. Chatbot architectures and languages
  6. Loebner Prize
  7. Turing Test
  8. https://blog.prototypr.io/so-you-wanna-build-a-chatbot-ff64623fea1#.651ryqmgl

Jumping in feet first into bots


This blog was created as a requirement for my part time masters course in digital media management at Hyper Island Singapore .

Now that I’m embarking on my final project, I’m re-purposing this blog to document my learnings and observations on my project, which revolves around the research question “How might I better understand, and test, the potential of chatbots and its application in the HR function?”

Why chatbots? Well, why not?

Initially I had a lot of hesitation on embarking on this (I still do), because I have no technical knowledge, but I’m fortunate to have friends who do have that background, and have pointed to resources and toolkits that will help me build a prototype, should I need to.

In the working world, where it may sometimes be difficult to launch projects that you are uncertain about, this is the best platform to try something new and unknown. After all, it affects only me, if I fail. And even if I fail, what is the worst that can happen? A delayed project submission? Not getting my masters? (although I’m going to try my best to not let that happen).

The experience I gain would be far more valuable than a piece of paper. So, wish me luck and success on this journey.

Is it fear of failure or the fear of the lack of absolute success?


What does it take to make a company more entrepreneurial, more agile?

This is a question that I’ve discussed with colleagues periodically from time to time, and I remember having this discussion with a colleague who has established several start-ups.

Memorable, because start-ups are seen as examples of entrepreneurial spirit, agility and speed at its best, and I was curious to learn the difference between his experience versus my own; someone who has worked predominantly in “traditional” business environments and structures her entire career.

In context of our company culture that espouses entrepreneurship (in fact Publicis Groupe recently announced Publicis90, a global initiative to foster it), we got to talking about how the willingness to fail is a critical element of successful entrepreneurship, because it allows risk-taking and speed in decision-making.

The mantra of “fail fast, fail often” is well known, discussed widely in books, forums, reported endlessly on.

But here, I offer a different viewpoint. In this new world of complexity, failures – and its antonym ‘successes’ – need to be viewed as a scale, in shades of greys.

Where in a past, pre-digital, pre-agile environment, you would deem anything that is not a success a failure (in fact, the first definition of the word “failure” in Merriam-Webster dictionary is “to not succeed”),  new ways of working requires us to re-think what it means to fail and succeed.

As a result of digital, businesses now need to move faster than before to keep ahead of the curve. Companies now aim to get products to the market faster so that they are able to shorten the customer feedback loop and quickly improve on their product, as opposed to developing a perfect product over years and have it fail upon launch because of lack of customer testing and feedback. A good example is how Microsoft releases beta versions of its software, although there are many companies now who do it even faster.

In this context then, what constitutes success and failure? When you get a Minimum Viable Product out in the market, the aim is to learn from customers, which implies that you accept that there might/will be aspects of the product that can be improved. So when the product only performs to 80% of its intended purpose, is it a success or a failure? Or 70%? 60%? 50%?

If you launch a communications or marketing campaign, and it doesn’t perform as well at launch, but with rapid iteration, it steadily improves in performance. Was that a success or a failure?

With products, processes and people in permanent beta now, what does the phrase “fail fast, fail often” really mean?

Coming back to the discussion with my colleague, it got really heated, and I was then asked: “Have you failed before?”

That stopped me in my tracks, and that is what got me thinking about what failure means. I have failed miserably before, although those have been (thankfully) few.

But, have I not succeeded before? Yes, plenty of times. Many projects that I’ve run don’t go as expected, especially when your outcomes are reliant on the decisions of others (whether it be pitching to influencers, media, or in my current job working through a dotted line network across business units).  Does it mean that I fail when things don’t go as expected? It just means I constantly adjust until I get the result I need (or at least as close to) with the resources I have.

What if trumpeting this mantra of “fail fast, fail often” is actually inhibiting entrepreneurship and risk-taking? Which individual, investor or stakeholder wants to fail, and fail often at that? What if we reframed it as “Test fast, learn fast”?

Perhaps if we re-thought how we thought about success and failure, and re-thought how we talked about the elements of entrepreneurship, we might get better results.

As always, my thoughts are in permanent beta, so I welcome your views as well.

The Importance of Reframing the Problem

Design Thinking Definition

Something that the lecturer said that struck me during the Design Thinking Module: Many clients already know the solution when they send the brief to us. All they are asking for is the ‘how’. It may be worth reframing the problem as it may open up a bigger space for opportunities.

A very simple analogy that was raised in class:

  1. Develop a design for a chair in which a person can sit comfortably
  2. Develop a way in which a person can sit comfortably

With the first statement, you are confined to designing a chair. The statement already tells you what solution is.

The second opens a whole new set of possibilities. It could be a chair, a bean bag, and swing or so much more.

How can we open a new set of possibilities when meeting our clients’ briefs?

Getting to the Heart (and Brain) of the Matter

Jonathan Briggs introducing Professor Calvert at the beginning of the talk
Jonathan Briggs introducing Professor Calvert at the beginning of the talk

I attended a fascinating neuromarketing talk at Hyper Island by Professor Gemma Calvert, Director for Research & Development at the Institute on Asian Consumer Insight at the Nanyang Technological University.

Neuromarketing is the application of neuroscience to marketing to uncover consumers’ subconscious needs, preferences and biases.

Three things that I found most enlightening that triggered some thoughts relating to my work in Learning & Development:

  1. People don’t do what they say they do
  2. Speed of an emotional response trumps the speed of a rational one
  3. A congruent multi-sensory experience has significantly more impact than one-faceted experience

People don’t do what they say they do.

Some standard marketing research tools may not be effective because of three things we know about people:

  1. They don’t always tell the truth
  2. They don’t think how they feel
  3. They don’t do what they say

In the case of 1. it can happen, particularly in Asia, when we don’t want to embarrass or offend the other party, or admit to a flaw or an undesirable behavior, or it is an uncomfortable or taboo topic that we want to avoid discussing.

Many also don’t think of how they feel. Consumers who are asked about how they feel about – or why they prefer – a product may make up an answer to rationalize an emotionally-led decision. In an agency environment, where everything moves extremely fast, a lot of colleagues move on instinct, especially in people / HR matters. Part of my job requires a lot of understanding of how people work, and I often ask leaders to tell me about what they look for in new hires, why they approach situations in a particular way etc. And I have discovered that quite a few find it hard to articulate their feelings and rationale for their behavior, even though they are extremely successful in their business, because they’ve never consciously thought of it.

Finally, many don’t do what they say, because they make up an answer to rationalize an instinct- or emotion-led decision.

In the area of Talent, this brings to mind the various surveys we do to detect the ‘pulse’ of the workforce: training surveys, engagement surveys, you name it. Employees may be overthinking their responses, or responding because they think that’s they way they should respond. So how accurate is the data we collect, and subsequently how effective and impactful is our talent planning as a result?

During the talk, Professor Calvert also spoke about Implicit Reaction Time tests, which are tests conducted at a speed that bypasses the conscious brain. These can be mobile or web based and are scalable. What if we applied this to our staff engagement surveys to uncover what they really think about the company? I wonder if results would be significantly different.

Speed of an emotional response trumps the speed of a rational one

There are two brain systems that control our behavior. One is Unconscious Emotion, which is very fast, involuntary and associative. The other is Conscious Thinking, which is slow, considered, and rule following.

In managing people, particularly in difficult and conflict situations, facts are important. However, managers often neglect to address the emotion behind it. So they may have addressed the situation, but may not have solved the problem. The team member continues to be unhappy even if the solution is the right one. Knowing that emotion drives our decisions, and that we rationalize them, addressing the emotion might be as equally important as discussing the facts and next steps.

A congruent multi-sensory experience has significantly more impact than one-faceted experience

The brain is built to integrate information coming in from different senses. Receiving sensory information that are complimentary to each other can be significantly more powerful than receiving it only from one source.

An example: Pringles taste 15% fresher and crisper when high frequency sounds were boosted in real time. So the crispness of the packaging enhances perception of crispness and freshness of potato chips.

Extrapolating this to the workplace, perhaps we need to start paying attention to the employee experience. In many companies, systems are not integrated, or are not viewed holistically, so employees do not gain a consistent message or experience in the company. If a company prizes collaboration, is collaborative working integrated into the infrastructure, rewards, and even the way training workshops are run? If a company prizes innovation, how is it encouraged and rewarded? How is innovation reflected in the corporate policies and the business operations, and not just innovation only for its consumers products or services? If a company wants to increase its digital revenues, how should its IT infrastructure change to support it?

How would making these changes impact performance in the workplace?