3 Areas of The Product Development Process Impacted by AI

Artificial intelligence (AI) is breaking down business barriers at staggering rates.

Research firm Markets and Markets estimates that the AI market will reach $190 billion by 2025, mainly driven by increased adoption of cloud-based applications and services, growing big data, and increased demand for intelligent virtual assistants.

Businesses across the globe are already being impacted by artificial intelligence in some way, shape, or form.

For those that aren’t, it’s only a matter of time.

One of the key business concentrations we’re seeing most impacted is the product development process.

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From realtime scoping and planning to smarter, unbiased hiring, and more meaningful performance management, AI is redefining the product development process.

In this post, we’ll look three challenge-prone areas of the product development process and how AI is helping innovators, developers, and product owners overcome them.

1) Project Planning

The Problem

When building our end-to-end product development platform, we spent a lot of time talking to potential users.

We spoke with business owners, product executives, PMs, engineers, and almost everyone else with product dev influence about challenges they face. There was one clearcut leader at the end of our discussions and it was project planning.

This commonly results in inaccurate project plans that lack transparency, making it extremely difficult to staff and monitor performance effectively – more on these two challenges later on in this post.

Poor planning – regardless of root cause – creates a chain reaction typically ending in time wasted, budgets exceeded, and finished products deemed as failures.

Most of the following primary causes of project failure could be avoided with better planning up-front.

(Source: PMI’s Pulse of the Profession (2017) report)

Avoiding these mishaps completely is nearly impossible without some level of machine involvement.

This is where AI comes in.

The AI Solution

The best way to speed up and plan more accurately, mitigate human error, and avoid scope creep is to learn from other projects similar to yours.

According to Project Management Institute’s Next Practices: Benefits of Disruptive Technologies on Projects (2018) report, cloud solutions, IoT, and artificial intelligence are the top 3 disruptors used for competitive advantage.

PMI’s take on AI from a competitive advantage standpoint:

AI makes it possible for machine processing to provide faster and more reliable decision making based on large amounts of stored information. The information-gathering capabilities of AI can help reduce human error and biases when it comes to creating budgets, predicting cost overruns, and developing schedules. AI-assisted tools could mean that project monitoring and schedule changes require less time and fewer resources. These efficiencies will allow project management to excel in areas where AI falls short, such as people skills and team building. The tools could also help project professionals devote more time to ensuring that projects remain in tune with the business case and aligned with organizational goals.

Let’s say you’re building an iOS application in the health and wellness space. Would you feel better about planning if you had access to hundreds of thousands of iOS application planning documents?

What about health and wellness related planning docs specifically?

The Data is There

The open web is home to an abundance of product development related data. Project scopes, planning documents, resource allocation, timelines, and everything in between.

The problem with all of this data though is processing and making anything of it. Thanks to artificial intelligence, we can train machines to process and analyze pre-existing project data.

Processing and analyzing data surrounding specific product builds allows us to identify common obstacles, stumbling points, and known challenges – helping us predict their surfacing and enabling us to proactively detect and handle with very little to no human interaction.

2) Resource Allocation

The Problem

Just as there are known challenges when scoping out a new product build, resource planning can be just as tedious and unforgiving.

Leading reasons for software project failure according to developers worldwide include under-resourcing (40%), poor team management (37%), and talent churn (23%). Tweet these stats.

View the full chart below.

It’s difficult – if not impossible – to predict each and every potential outcome when diving into a new product build. This makes it equally difficult to recruit, interview, train, and maintain not only the right staff, but the correct level of bandwidth, too.

Many companies tend to have lengthy hiring processes when it comes to technical talent in particular. This creates a whole new set of challenges for project managers responsible for task management and workflows.

Add to all of this tech’s known hiring biases and we have ourselves a real issue.

Resource Allocation Issues in Tech

Tara AI CEO and Co-founder Iba Masood went on CNBC recently to shine light on HR and resource management issues that are all too common in the tech space. She touches on the massive pool of untapped talent, constantly overlooked as a result of human biases and a lack of sufficient technical knowledge amongst HR professionals in America.

Watch the full interview below.

It’s no wonder resource management contributes so heavily to a project’s success or failure.

If you’ve ever been in a position where it is your responsibility to manage resources, you’ve likely encountered one or more – or all of the following.

  • You miscalculated your resource requirements completely
  • Key resource on an important project are lost
  • You’re asked to shorten the initial timeline significantly enough to cause panic
  • You received misinformation up front from a client or your colleagues
  • The resources provided to you simply didn’t work out

If you’re nodding your head, read on and learn how AI is helping solve these challenges.

The AI Solution

Data is unbiased.

Data doesn’t care if a candidate went to Stanford or a Community College. It doesn’t care who candidates know or where they grew up.

What we train our machines to care about is experience. Have candidates successfully contributed to and/or completed projects like the one you’re trying to staff for right now?

Race, gender, age, and sex are thrown out the window when we leave talent acquisition to our algorithms. This widens the talent pool as we know it drastically, making it much easier to find the right talent at the right time – and doing so A LOT faster than we’re used to.

What this creates is an unprecedented level of confidence surrounding resource allocation – something product owners and project managers don’t typically experience when planning for and maintaining complex, technical product builds. AI is changing all of this.

Resources on Demand

Let’s talk about resources on demand. If you’ve experienced any of the aforementioned circumstances, this is another key area where AI is having a positive impact.

One of the easiest and most efficient ways to overcome resource-related challenges without further hindering progress involves artificial intelligence.

Resources on demand isn’t a new concept. There are countless freelance websites where individuals create profiles, highlight their skills, projects worked on, etc. Companies looking for a developer with specific talents can use these websites to search for potential candidates. No different than traditional hiring, we’re up against time, human opinions and biases, and a lack of the appropriate levels of technical knowledge needed to properly vet candidates.

Throw AI into the mix and something really special happens.

Just as we touched on the impact AI has on project planning earlier in this post, the same holds true for its impact on resource allocation. Structured project data makes it possible to scope projects with far more precision than ever before. A result of this is equal levels of precision on the resource allocation front.

Tara AI was able to find a developer with incredibly specific talent in under 24 hours for Cisco. Read the full case study on how artificial intelligence played a role in the hiring process.

3) Performance Monitoring

The Problem

Another area of the product development process ripe for evolution via technology is performance monitoring and reporting.

I spent over a decade with companies that handled performance monitoring and reporting manually. At the end of the day, not only is this inefficient and extremely difficult to scale, but we found ourselves reporting on the same sets of data for most of our customers – out of habit versus what was best for the customer. This method of performance monitoring was completely inefficient and useless to the customer.

According to PMI’s Pulse of the Profession (2017), we’re seeing upward trends in the number of projects meeting original goals while being completed within budget for the first time since 2011.

Product Management Process

(Source: PMI’s Pulse of the Profession (2017) report)

While these are solid indicators of steps in the right direction, we still have a way to go.

Businesses tackling these performance metrics through disruptive technologies like AI are leading the pack.

The AI Solution

If you’re perking up reading about the positive influence AI is having on planning and resource management, you’ll appreciate how it’s impacting performance monitoring and reporting, too.

When paired with methodologies like agile, AI makes it possible to create added layers of transparency on even the most complex product builds – right down to the most granular of tasks.

Wouldn’t it also be nice to proactively spot roadblocks and potential setbacks?

With AI, this is a reality. Through the continuous consumption of project data, we can train machines to spot these things based on past experiences and publicly accessible data. As time goes on and we continue passing through more and more project data, the predictive capabilities become stronger and stronger.

On the flipside of this, we can also identify opportunities for continuous improvement by following the same ideology as we would when attempting to spot nuances before they happen.

While we may never see the day machines replace humans completely, performance monitoring is an area of the product management process where machines are making impact.

Product Development Process

(Source: Capterra’s I, Project Manager: The Rise of Artificial Intelligence in the Workplace)

These are recipes for major success in the new product development world.


Technology will forever define and redefine how we do things.

In the age of artificial intelligence, it’s not people versus machines. It’s people and machines.

Businesses that successfully bring the two together are leading the pack.

Those who choose to ignore – whether due to fear of the unknown or ignorance – fall further and further behind.

Early adopters are innovating rapidly, with more precision and in most cases, cutting timelines and costs down significantly.

The ways artificial intelligence reshapes the product development process is truly exciting.

More accurate planning, faster.

Better resource allocation.

Transparent performance management.

These are just three areas of the product development process being impacted positively by artificial intelligence in 2019.

Have you experienced any of these things for yourself? If so, share in the comments below.