How to Use Async Work as a Launch Pad to Become an AI-Fueled Organization Companies that fully adopt async work and the data that it provides can use this as a launchpad towards greater AI-fueled efficiencies.
By Cory Hymel Edited by Chelsea Brown
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With the displacement that we live in during a post-Covid world, many businesses find themselves working across multiple time zones and locations. Many insightful leaders have even begun to tap into the process of asynchronous or async work to better leverage the time available between their workforces, allowing for more productivity.
What many are failing to realize, however, is that async work produces large amounts of data, which, if organized correctly, can slingshot an organization into becoming an AI-fueled rocket ship. Companies that fully adopt async work and the data that it provides can use this as a launchpad toward greater AI-fueled efficiencies.
Related: How To Manage an Asynchronous Work Flow
What is async work?
Companies adopting the remote and distributed work models find themselves with workers in different geographical locations (for example, London, Los Angeles, Nairobi or Singapore) and time zones. This makes it difficult to rely on the traditional 9-5 culture where everyone is expected to be available on Slack at the same time every day.
Async work refers to a remote or hybrid work model where employees are allowed to work at a convenient time — and schedules, goals and deadlines are adjusted to fit a more flexible working model. Instead of real-time communication and collaboration, the model relies on a shared commitment and clear goals and standards.
In async work, remote workers work effectively and efficiently because they can plan their schedules to meet goals and deadlines promptly to the company's satisfaction without necessarily being online at a specified time daily.
Data is the key to async work
A lot of companies just let async grow organically, but the most successful companies foster and nurture async work to ensure it works at scale. This requires recording and documenting everything so workers no longer rely on instant responses from co-workers to complete their tasks.
Asynchronous communication, which means documenting everything using taskmaster apps instead of regular messaging apps, becomes the bedrock of async work. This includes writing Wiki-style guides for colleagues, from upcoming plans to measuring past results. Anything written in the shared tool becomes available for even future employees instead of repeating it dozens of times. Everything employees might need to complete their tasks — including video calls, brainstorming sessions, handoffs, calendars and other relevant company data — is recorded, stored and organized. Here are some examples that our business uses every day:
Loom for internal async video updates (usually three minutes tops)
Chorus for recording real-time meetings
Confluence for Wiki-style documentation
Jira for individual project task tracking
Slack for real-time and async communication
Google Docs for documents
Google Slides for, well slides
Salesforce for customer data
Email (duh)
On top of this, we already have background models that run off Slack team conversation data to understand in real-time their level of cognitive alignment and team sentiment.
When async work is supported correctly, you end up with a large amount of data. What you should imagine as a business is if you were to aggregate all of this data into a single training set, what could you do? One of the emerging areas of AI is called process mining, in which an AI system analyzes enterprise transactional systems to understand how the process is being performed currently, then makes improvements and recommendations.
Related: 5 Tips for Managing Asynchronous Work in Your Organization
Using async work data to fuel AI initiatives
The first step in any AI or machine learning project is to organize all relevant data. Async work provides the perfect excuse to get your entire organization bought into the idea of becoming data-driven. Now, you can use this data to fuel AI initiatives. Here are some interesting use cases to consider:
Automating messaging with AI:
A report by Management Matters suggests that the use of AI-based chatbots within HR and IT circles would increase efficiency by reducing repetitive and time-consuming tasks, thus enabling employees to concentrate on critical tasks. Integrating asynchronous communication tools with AI chatbots that can seamlessly collect and analyze data from documentation, conversations and colleague feedback would increase employee engagement and provide a high return on investment.
Pattern recognition and prediction:
Asynchronous communication data provides insights on how employees work, what causes project delays and what information is needed for a successful project. AI can help organizations identify patterns in this data and make predictions to improve management, forecasting and budgeting. Using Trello tasks, GitHub check-ins and client feedback data can help identify possible delays or bugs before they happen. Slack communications, employee reviews and manager insights can be used to automatically assemble the perfect team for a new project based on the soft and hard skills needed to get the job done.
Generative AI can create work templates:
Large language models like ChatGPT can't replace workers, but they can speed up the initial planning and templating stages of a project. Providing a large language model with the code libraries and project plan templates developed through async work means AI can automatically generate any relevant project resources with a simple query, saving valuable time and allowing new projects to start faster.
Automate manual processes:
Too many companies look to automation to solve their organizational problems before they have built solid work processes. Async work is the ideal time to build workflows and processes into your organization and support them with collaboration and communication tools. Then — once you're organized — you can use that data to determine what tasks or elements of those processes can be automated or made more efficient through AI.
Common ways AI-fueled companies realize value
Implementing some or all of the above use cases can have a huge impact on your organization. The benefits of AI are well documented. Here are some of the ways AI-fueled companies realize value:
Speed to execution
Cost reduction
Comprehension of complexity
Transformed engagement
Fueled innovation
Fortified trust
Related: 5 Ways to Make AI Work for Your Organization
Action steps
The key to using your async work to launch AI initiatives is developing a smooth process for data collection that also provides the trust employees need to work autonomously and effectively. To create a swift, nimble and even pipeline that will guarantee the correct output for your AI needs, you need to embrace the following actionable steps:
Invest in online workflow tools: To facilitate asynchronous communication that will enable your remote or distributed team to communicate effectively across different countries and time zones without having a face-to-face conversation, invest in async collaboration tools designed explicitly for chat, file sharing and video. Leverage cloud platforms and model providers to integrate tools and data.
Set up workflows and processes for better data management: Keep data front of mind — always. Collaboration and project management tools only work when they have the data needed for employees to get the job done. Educate your organization on the value of collecting, storing, protecting, organizing and verifying essential data and making it readily available.
Know what you want to accomplish with AI: Understanding your end goals will help develop the data management and analytics needed to accomplish those specific goals. Once everyone is on board with the benefits of becoming a data-driven organization, look for new ways to leverage your existing data through AI.
Start with analytics: The quickest win and the best return on investment you can get is to use AI to fuel analytics. Automating data analysis, data visualization and improving access to data will make a huge impact on business intelligence and earn further buy-in for your AI initiatives.
Be prepared to invest: Becoming an AI-fueled organization will require an investment in AI tools and development resources. Getting buy-in and some quick results in the above steps will help you justify further investment in AI.
As more companies adopt global and distributed workforces, asynchronous work is inevitable. Approaching this new work model intelligently and intentionally allows your organization to fully leverage its benefits while also presenting a back door into becoming a truly data-driven organization. Once you have the data and the processes, the sky's the limit.
