How to Use Async Work to Become an AI-Fueled Organization

With the crowding we live in in a post-Covid world, many businesses are working across multiple time zones and locations. Many insightful executives have even begun to use the process of asynchronous or asynchronous work to better utilize the time available between their employees, allowing for greater productivity.

What many don’t realize, however, is that asynchronous work produces large amounts of data that, when properly organized, can transform an organization into an AI-powered rocket ship. Organizations that fully embrace asynchronous work and the data it delivers can use this as a launch pad for greater AI-powered efficiencies.

Related: How to Manage an Asynchronous Workflow

What is asynchronous work?

Companies that adopt remote and distributed work models find themselves with employees in different geographic locations (e.g. 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.

Asynchronous work refers to a remote or hybrid working model that allows employees to work at a convenient time – and schedules, goals, and deadlines are adjusted to accommodate a more flexible working model. Instead of real-time communication and collaboration, the model relies on shared commitment and clear goals and standards.

With asynchronous work, remote workers work effectively and efficiently because they can schedule their schedules to meet goals and deadlines in a timely manner to the satisfaction of the organization without necessarily having to be online at a specific time each day.

Data is the key to asynchronous work

Many companies just let async grow organically, but the most successful companies nurture and encourage asynchronous work to ensure it works at scale. This requires everything to be recorded and documented so employees no longer have to rely on immediate responses from colleagues to get their jobs done.

Asynchronous communication, i.e. documenting everything with Taskmaster apps instead of regular messaging apps, becomes the basis of asynchronous work. This includes writing wiki-style guides for colleagues, from upcoming plans to measuring past results. Everything written in the shared tool also becomes available to future collaborators instead of repeating it dozens of times. Everything employees need to get their jobs done—including video calls, brainstorming sessions, handoffs, calendars, and other relevant company data—is recorded, stored, and organized. Here are some examples that our company uses every day:

• Loom for internal asynchronous video updates (typically three minutes maximum)

• Chorus to record real-time meetings

• Confluence for wiki-style documentation

• Jira for tracking individual project tasks

• Slack for real-time and asynchronous communication

• Google Docs for documents

• Google Slides for, good slides

• Salesforce for customer data

• email (duh)

Additionally, we already have background models using Slack team conversation data to understand their level of cognitive alignment and team sentiment in real time.

If asynchronous work is supported correctly, you will end up with a large amount of data. What you should envision as a company, if you were to consolidate all 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 companies’ transactional systems to understand how the process is currently being performed, and then makes improvements and recommendations.

See also: 5 tips for managing asynchronous work in your organization

Leverage asynchronous work data to power AI initiatives

The first step in any AI or machine learning project is to organize all relevant data. Asynchronous work provides the perfect excuse to convince your entire organization to go data-driven. Now you can use this data to drive AI initiatives. Here are some interesting use cases to consider:

Automate messaging with AI:

A Management Matters report suggests that the use of AI-powered chatbots in HR and IT circles would increase efficiencies by reducing repetitive and time-consuming tasks, freeing employees to focus on critical tasks. Integrating asynchronous communication tools with AI chatbots that can seamlessly collect and analyze data from documentation, conversations, and peer feedback would increase employee engagement and offer a high return on investment.

Pattern Recognition and Prediction:

Asynchronous communication data provides insight into how employees are working, what is causing project delays, and what information is needed for a successful project. AI can help organizations recognize patterns in this data and make predictions to improve management, forecasting, and budgeting. Trello tasks, GitHub check-ins, and customer feedback data can help you identify potential delays or errors before they happen. Slack Communications, Employee Reviews and Manager Insights can be used to automatically create the perfect team for a new project based on the soft and hard skills needed to get the job done.

Generative AI can create working templates:

Large language models like ChatGPT cannot replace workers, but they can speed up the initial planning and template phase of a project. Providing a large language model with the code libraries and project plan templates developed through asynchronous work means that the AI ​​can automatically generate all relevant project resources with a simple query, saving valuable time and allowing new projects to start faster.

Automate manual processes:

Too many companies rely on automation to solve their organizational problems Before They have established solid work processes. Asynchronous work is the ideal time to build workflows and processes into your organization and support them with collaboration and communication tools. Then — Once organized, you can use this data to determine which tasks or elements of those processes can be automated or made more efficient by AI.

Common ways AI-powered companies are realizing value

Implementing some or all of the above use cases can have a tremendous impact on your business. The benefits of AI are well documented. Here are some of the ways AI-powered companies are realizing value:

See also: 5 ways to use AI for your business

action steps

The key to leveraging your asynchronous work to launch AI initiatives is developing a frictionless process for data collection that also gives employees the confidence they need to work autonomously and effectively. To create a fast, flexible, and smooth pipeline that guarantees the right output for your AI needs, you need to take the following actionable steps:

1. Invest in online workflow tools: To facilitate asynchronous communication that allows your remote or distributed team to communicate effectively across different countries and time zones without having to face-to-face, invest in asynchronous collaboration tools designed specifically for chat, file sharing, and video . Leverage cloud platforms and model providers to integrate tools and data.

2. Set up workflows and processes for better data management: Keep your data in mind – always. Collaboration and project management tools only work when they have the data people need to do their jobs. Educate your organization about the value of collecting, storing, protecting, organizing, verifying, and delivering critical data.

3. Be aware of what you want to achieve with AI: Understanding your end goals will help develop the data management and analysis needed to achieve those specific goals. Once everyone is convinced of the benefits of a data-driven organization, look for new ways to leverage your existing data through AI.

4. Start analyzing: The fastest win and best ROI you can get is using AI to power analytics. Automating data analysis, data visualization, and improving data access will have a tremendous impact on business intelligence and gain further acceptance for your AI initiatives.

5. Be ready to invest: Becoming an AI-powered organization requires an investment in AI tools and development resources. If you can agree with the above steps and get some quick results, you may justify further investment in AI.

As more companies adopt global and distributed workforces, asynchronous work is inevitable. Approaching this new work model intelligently and consciously can help your organization reap its full benefits while opening a backdoor to becoming a truly data-driven organization. Once you have the data and processes, there are no limits.