The ways of the modern world are faster and more complex than ever before. Many information, messages, and tasks are being sent to employees from multiple sources. Research shows that context switching amongst the different tasks can cut productivity by up to 40%. The vital projects are delayed, and companies pay time and money as workers are overwhelmed.
However, progress has been made in applying this to artificial intelligence. Artificial intelligence tools aim to analyze workflows, personalize tasks, and schedule them optimally to improve task prioritization and production. With this technology now being used worldwide, workplaces are changing, and people can shift their focus to higher-value, more rewarding work.
Understanding Task Prioritization
Organizing the tasks in the order of importance is called task prioritization. It focuses on assessing:
- Priority. Importance and urgency of each task
- Dependencies. What other tasks must be completed first
- Time and Resource Requirements. Effort, people, and tools needed
- Business Value. How the task impacts key goals
With this analysis, the less critical work gets scheduled later, and more vital tasks get addressed first.
It is often difficult for humans to prioritize correctly on their own. Usually, bias, distractions, fatigue, and multitasking interfere, but leveraging AI & ML development services can help streamline prioritization by analyzing patterns and optimizing workflows. We tend to ignore analyzing what is the most important thing and instead try to fit in with the loudest voice or the latest demand. We don’t normally get a chance to select what’s most important to us sharply; we tend to focus on the loud voice or the most recent demand.
Data-driven task analysis uses an AI assistant to reduce the gaps between emotion and perception, much like how machine learning in manufacturing optimizes production processes by analyzing vast amounts of operational data. The assistant can run hundreds of tasks in seconds based on preset business rules and time learned over time, allowing for the most optimal scheduling for each person’s work style.
The Benefits of AI-Powered Task Re-Prioritization
Increased Productivity
AI takes over determining task prioritization so the workers can stay laser-focused on execution. It increases productivity substantially.
CBMS studies showed that task-switching attention dropped from an average of 3 minutes to 40 seconds. With AI optimization, people can carry out one task to the fullest without being distracted by constant interruptions.
Targeted focus also leads to faster task completion. Each assignment provides workers with all the necessary context and resources, eliminating the need to reload information every time they shift tasks.
Reduced Stress
According to the American Psychological Association, 77% of workers feel stressed often at work. Task overload and ambiguity are top contributors. Staying calm and focused is challenging when the to-do list seems endless and priorities shift.
AI takes on the burden of task prioritization, organization, and preparation. Humans simply have to show up and complete prioritized tasks. Removing the pressure to figure out what to do when makes work far less anxiety-inducing.
Streamlined Teamwork
Collaborating with colleagues also becomes much more manageable. Everyone shares the same up-to-date view of team priorities and assignments.
Rather than playing email or phone tag to coordinate hand-offs, the AI system handles dependencies across the workflow. Alerts automatically inform colleagues when they need to take action to keep projects moving.
This streamlined coordination saves many hours previously wasted trying to sync up as a group. It also minimizes dropped balls since the technology tracks all details.
Accelerated Innovation
With efficient task execution and seamless collaboration in place, teams can raise their sights toward more impactful goals. Workers gain substantial time and mental space to consider the bigger picture strategy and new ideas for growth.
The world of business is becoming more hypercompetitive and changing more quickly than anyone could have imagined. AI optimization allows people to rethink solutions instead of just following a daily task prioritization routine.
Key Capabilities of AI-Powered Prioritization
The most advanced AI platforms combine task information with employee analytics to enable personalized optimization. Core capabilities include:
Task Extraction & Analysis
AI scans emails, chats, documents, and tickets to uncover task-related details. Natural language processing identifies each assignment’s nature, deadlines, stakeholders, and requirements. Data extraction tools further compile related information.
Employee Productivity Profiling
Advanced algorithms study employee patterns, such as when they work best, average task throughput, focus time needed per project type, and role priorities. This creates an individual productivity profile.
Constraint-Aware Prioritization
Once tasks and profiles are understood, the system runs optimization scenarios that account for all interdependencies, employee bandwidth, deadlines, and role-based goals. A constraint-aware methodology ensures priority alignment.
Predictive Scheduling
The AI platform can assign tasks based on priority, workload capacity, and estimated duration. Automatic reprioritization occurs in dynamic shifts of due dates or workloads.
Work Pattern Analysis
The system analyzes the time it took, the delays, and the productivity of employees executing tasks to refine understanding and enhancement recommendations. Users can also directly say what is and isn’t working.
Continuous Optimization
An algorithm running real-time analytics directly adjusts the workloads’ priorities and scheduling to ensure that they stay balanced and aligned with changing demand.
Impact on Key Roles and Functions
The benefits of AI-powered prioritization are universal across knowledge workers. However, certain roles that manage high volumes of tasks and collaborate with many stakeholders gain a particular advantage.
Project Managers
Any project manager has to worry about the triple constraint of scope, schedule, and resources. Overload will inevitably occur when each initiative also has to contend with shifting priorities.
Tasks are completed, and AI continually relocates timelines. It reallocates resources across projects as per needs and reoptimizes portfolios upon the arrival of new initiatives. Many tedious calculations play a role in being automated away.
PMs are free of the weeds of spreadsheets and charts to focus on, making it easier to collaborate and be creative to achieve results. In this role, they become a value-added, not as taskmasters but as strategic oversight.
Engineering Teams
Engineers waste over 20% of their time each week just identifying what to work on next. AI eliminates this inefficiency with a smart backlog that prepares each engineer’s most urgent bug or feature task for execution.
Shared visibility into code dependencies and testing status also minimizes wait time. Engineers don’t get stuck waiting on other teams. The system automatically notifies colleagues to unblock progress.
Cleaner hand-offs and a faster feedback loop allow the engineers to have a more continuous flow. This feature allows for faster shipping without sacrificing quality or suffering.
Executives
For the most part, business leaders don’t have visibility into what their teams are working on daily. Though statuses run rampant, the priorities are still not clear.
AI dashboards provide executives with real-time visibility into portfolio health. They see how projects are mapped to corporate goals and can drill down to shift task prioritization for individual tasks. No more useless meetings or vague directives are needed.
With this bird’s-eye view, executives keep their fingers on the pulse of operations without getting dragged into the weeds. They spend less time managing and more time charting the future.
Implementation Best Practices
Like any exponential technology, workflow AI requires thoughtful change management for impactful adoption across an enterprise. Critical best practices include:
- Getting executive sponsorship. Help leadership understand exactly how AI can enhance performance company-wide before asking them to be advocates. Pilot a prototype with their core teams first as proof.
- Transparent communication. Employees will only trust and use an AI system if they understand what it is optimizing for and how it works. Explain the business rules and logic through open forums.
- Incentive alignment. Consider tying individual incentives to utilizing the AI system, at least initially. This could mean bonuses or eligibility for promotion based on productivity throughput.
- Ongoing user feedback. Open clear channels for regular employee input on what’s working and what needs adjustment in the AI assistant. User sentiment can inform incremental improvements.
- Patience with the adoption curve. Training does not mean that the mind and body will automatically change to the change in workflow. They measure utilization rates but have ~8 weeks before deciding whether it works. Find ways to address the barriers causing the loop to begin to close.
The future of work is bright, and it is possible with careful collaboration between people and technology. AI optimization allows the human spirit to create instead of doing drudgery by taking away the tedium of tasks. The winners will be enterprises that use automation to free their minds for higher thinking. Getting priorities right is the first step to transcending what usually is possible.
Conclusion
Task prioritization used to be a necessary evil for every manager and employee who was distracted by the urgent and could not focus on the important. Now, AI can shoulder this burden to enable human talent.
Intelligent assistants also allow us to simultaneously achieve workflow efficiency, collaboration, and innovation. Rather than being forced to mend what’s damaged, we take measures for what’s to come.
Maslow beckons us to rise in his hierarchy of self-actualization to the future. Before that, however, we needed AI to skillfully clear the clutter that had been piling up at the base. Wings come when intelligent algorithms take away the tasks from our plates.
Photo by Kelly Sikkema; Unsplash
