How does Machine Learning boost project delivery?

How does Machine Learning boost project delivery?

machine learning


Technology is an invaluable contributor to business efficiency. And the kind you use actively today says a lot about your adaptability to change and market uncertainties. User behavior evolves, influencing product innovation. Tools are subjected to continuous technological updates, whose quality is superior to that of their predecessors. Movements in the Artificial Intelligence industry are being closely followed by experts and researchers alike in order to make sense of the impact it wields on work, workplaces and the workforce.

Machine Learning is the key to find out solutions for complex problems of systems. So if you want to become a proficient programmer then Intellipat offers you the best Machine Learning Training Courses from industry professionals.

Machine Learning has enabled project managers to run more, and better projects on the basis of the data processed. Besides ensuring transparency across people and processes, ML applications save several hours on the clock that would have otherwise been routed towards determining the course of action to be taken.

Here are a few ways Machine Learning boosts project delivery –

1. Resource predictions –

Machine learning helps project managers create project teams on the basis of skills relevance and availability of competent resources. In fact, machine learning within a resource management software even lets you determine the likelihood of going over or under the existing workforce capacity. In other words, whether your workforce strength is sufficient in quantity and quality to staff incoming as well as existing work.

This data includes the skills utilized on previously available hours and future bandwidth estimates for pipelined projects. This way, you’ll know which skills are critical, and whether employees possessing the required credentials are available for the location and time frame in question. Your hiring cycles then remain demand-driven ,letting you tap into potential from within.

2. Updates databases –

Machine learning works by ingesting data and sorting them into clusters. Logic-driven algorithms are then applied to the data in order to generate meaningful output. The data then goes into a centralized database which gets synced and updated periodically. Embedding machine learning intrinsically into your project lifecycle ensures the information you’re working with is useful and relevant to the project phase teams are in.

An automated database not only captures business activities spanning the enterprise, but also ensures bottlenecks are flagged and brought to the concerned authorities’ attention sooner for strategic resolution. In order to reconcile funds allocated to the project against its expected returns, every stakeholder would want status updates which reflect how a particular activity is progressing and the overall project health. And with a machine learning component, project managers have all the answers to questions concerning performance. They also get more time to plan the portfolio pipeline at a strategic level.

3. Catches discrepancies –

While machine learning is a subset of automation learning, what differentiates it from AI is that it requires lesser human intervention. It instead, relies on training models to create identifiable patterns. When patterns are recognizable, so are alternate paths (i.e. there is more than one way to reach a solution) and deviations. When a process runs incorrectly, the effects are visible in the form of lowered efficiency. Spotting the causes behind it lets you carry out damage control.

An area repeatedly disputed by employees and managers alike is the number of effort hours utilized on the bandwidth available. Machine learning has the advantage here, because it can entertain data coming in from both resource and project channels without short circuiting the database. Resource managers can pull insights from an analytics dashboard, and keep billable utilization at an optimal level.

This way, your talent pool’s performance is brought to a unified metric where there is no question of a member doing more, or less than their colleagues. Project delivery gets a boost when the hands on deck are allocated work that makes use of their expertise

4. Re-establishes baselines –

With the shift to iterative agility and a more immersive work culture, changes requested after the project is underway no longer catches project managers and their teams by surprise. There is, however, the question of what trade off will have to be made. After all, one constraint changing impacts the other ones too, similar to the Domino effect. For example, a scope change deemed feasible and sensible to implement would require a recalculation of costs, time and effort investments.

Data points from a machine learning source aggregate constraints and gives you a baseline for each constraint. What’s more, you’ll know which area of the project is likely to overrun originally drawn estimates. Rather than take on a firefighting approach, the spend can be sized up beforehand in order to leave a margin for baseline redistribution.  Put simply, the project won’t suffer from a crunch in skills and budget. Digitally documenting the risks encountered and mitigation plans lets you prevent recurrences in the future, ensuring baselines can be reused.

5. Economizes operating costs –

Operating costs would include the costs to explore an opportunity, acquire staff and train them via the appropriate reskilling and/or upskilling measures. It is counterproductive, therefore, to downsize  your resource pool during peak seasons, or worse, expand your talent pool during a recession. Seasonal fluctuations impact lines of work. When there is less work to go around, more staff are benched. Conversely, when you have a lot of projects converting to actual work but fewer or inexperienced hands to take it on, a skills crunch ensues. You’re then forced to pass up an opportunity or take up one that doesn’t align strategically with your market goals.

A machine learning component prevents you from adding more to your payroll costs by sizing up project requirements comprehensively, i.e. its scale.  It performs a cost-to-benefit analysis for incoming projects as part of its early feasibility study. This way, project selection can be capped on the basis of the returns expected.  Making impactful decisions from the very start complements your experiential judgement, letting you assemble a work-ready workforce. Data science is also helpful in leveraging the right type of talent onto projects, be them external (for a client) or internal. It profiles employee records by their credentials and notifies you of expiration dates on contracts and certifications. Not only are you assured of the project’s duration being utilized correctly but that the team can offer the work quality sought after.

A 2019 report  by PwC revealed AI is slowly moving out of its infancy stage, with 47% of companies actively planning to use aspects of it. As a niche technical area, this number implies that there will be more widespread usage of the machine learning and data analytics subset. An entirely different set of project-oriented roles will be created solely to drive future work. With a prediction engine that teaches humans as much as it learns from us, it is safe to conclude why machine learning is a technological trend that will mold the effectiveness of work itself!

Author Bio:

Aakash Gupta is the subject matter expert in resource management at Saviom Software. An acclaimed columnist in project efficiency space, his publications have drawn significant interest from leaders wishing to do more with productivity scales. Connect with him here to know more.

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