- New Enterprise Stack – Part 1 – introduces the old enterprise stack, its relevance to productivity, engagement and culture reinforcement.
- New Enterprise Stack – Part 2 – presents visual representation and facets of Systems of Record, Systems of Micro Experiences and Systems of Engagement.
- New Enterprise Stack – Part 3 – details Systems of Micro Experiences with examples.
In this Part 4 of the series, I’ll cover the design principles for micro experiences and what nudges are and the AI/ML facets that are crucial to delivering micro experiences.
Micro Experiences – Design Principles
Any platform that thinks of providing micro experiences on a System of Engagement (SOE) should adhere to the below design principles:
1. Frictionless – 95-5 thumb rule
Any time a user is required to traverse between multiple systems to get his/her work done, there is a friction created with ensuing cognitive load and loss of productivity. As a thumb rule, 95% of the employees in any enterprise are “normal users” and deserve to be treated as consumers of “day-to-day” work experiences. The remaining 5% are “power users” who use Systems of Record for their day-to-day work.
When thinking of micro experiences, the dominant design paradigm should be “frictionless” i.e. 95% of the users should be able to consume day-to-day work experiences on their default System of Engagement.
2. Unified user experience (UUX)
The frictionless design paradigm creates an opportunity to provide a Unified UX to the normal user since the experiences are delivered on a default System of Engagement. A unified UX can scale both vertically and horizontally i.e. depth and variety of use cases. Further, a unified UX reduces cognitive load for the users thus resulting in increased productivity.
3. Avoid YAP – Yet Another App
It is extremely common for power users within enterprises to demand one more system/tool/app around a work process. Multiple apps increase cognitive load due to UX dissonance and cause app fatigue amongst normal users. Just image ‘How would you feel if HR launched yet another application for LMS which was not linked to Workplace from Facebook, MS Teams, Slack, etc?
When designing micro experiences, it is imperative to avoid this trap and deliver the experiences on a normal users’ platform of choice i.e. the default System of Engagement.
4. Built-in Nudges
A nudge is way of designing choice-architecture that helps humans to make right decisions and avoid any biases, heuristics, fallacies developed due to their automatic system of thinking.
Richard Thaler is a theorist in behavioral economics and has won nobel prize in the year 2017. He co-authored a book called Nudge: Improving Decisions about Health, Wealth, and Happiness, published in the year 2008.
When architecting micro experiences, we should consider what the right choice architecture mechanism should be i.e one that creates incentives, sets defaults, has a built in feedback, expects error and structures complex choices.
5. Contextual CTAs
Any micro-experience with a built-in nudge should elicit the desired response from a user. Deeply contextual call-to-actions (CTAs) are a must have for designing frictionless day-to-day work experiences.
Context CTAs create positive network externalities where actions by normal users create direct or indirect value for other normal users.
6. Flow Augmentation
Flow is a state where a user is “zoned-in” and is at his/her maximum productivity. In the context of enterprise stack, “working in the flow” means high productivity and frictionless state resulting from collaborating, communicating and getting day-to-day work done from a single System of Engagement.
When designing micro experiences, we should always think whether the experience augments the “working in the flow” state.
7. Adoption to Advocacy
We can visualize nudges and the CTAs as a funnel. When designing micro experiences, we should embed funnel metrics into an experience right at the start so that we can understand how the users adopt, repeat and advocate the experiences to other users thus creating a virality around the experience.
8. AI-ML – Under Promise Over Deliver
By this time, you must have realized that nudges and CTAs point towards the use of AI-ML in designing micro experiences. However, use of AI-ML is costly and the economics of embedding AI-ML must be factored into the design. To better understand the economics, here is the link for an excellent article published by the team at Andreessen Horowitz – The New Business of AI (and How It’s Different From Traditional Software)
When trying to embed AI-ML into the design of micro experiences, we should be extremely careful not to over complicate and try to reinvent the wheel.
95% of the employees in an enterprise are normal users –> normal users deserve to be treated as consumers –> consumers prefer simplicity –> delivering simplicity at scale is complex
It is not that engineers at tech giants like Google, Facebook etc. are unable to complicate. However, they have made a choice to keep it simple for the end users. For example, Systems of Engagement like Workplace from Facebook launched predictive text (Google has Smart Compose), live transcribing of videos and such other features to augment the end user experience.
Enterprises have unique culture and the experience design process has to factor these cultural differences. Any design of a platform for delivering micro experiences should essentially offer “deep customization” to power users who are essentially functional business process owners. “No-code” design paradigm allows the flexibility to help power users customize their experiences.
10. NLP & Domain Ontology
Conversational Interfaces (CI) are popular platforms for delivering micro experiences. However, there is a tendency to piggyback on standard libraries for managing intent and context extraction.
When designing micro experiences in the day-to-day work context of employee in an enterprise, it is extremely important to factor in domain ontology.
“A domain ontology (or domain-specific ontology) represents concepts which belong to a realm of the world, such as biology or politics. Each domain ontology typically models domain-specific definitions of terms. For example, the word card has many different meanings. An ontology about the domain of poker would model the “playing card” meaning of the word, while an ontology about the domain of computer hardware would model the “punched card” and “video card” meanings. – Wikipedia“
In Part 5, last part of the series on the New Enterprise Stack, I’ll discuss “How and why conversational interfaces are best suited to deliver the micro experiences?”.