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

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

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