At the 2019 Gartner Program & Portfolio Summit, it was predicted that, by 2030, 80% of the workload involved with project management (PM) will be performed by artificial intelligence (AI); i.e. data collection, tracking and reporting. At the summit, it was also foreseen that this AI-heavy software will enter and revolutionize the market as early as 2023, which from the time of writing this article is only a year from now. Not bad, right?
With the prospect of such seismic changes so close on the horizon, what do we need to look out for when choosing a project management software that will work best for our own tailored needs? As was always the case, product features, data security, performance and cost are crucial factors. However, with AI now starting to take centre stage, we need to be aware of what this can mean for analytics.
Team Tool, Governance Tool or Both?
Project management software is an umbrella term that can be applied to any software which facilitates collaborative work processes. This encompasses anything from bare-bone collaboration apps to enterprise software used for managing entire project portfolios. With such a broad market variety, simply searching for and comparing products which carry the moniker of ‘project management software’ is not the best approach. Rather, your search needs to be narrowed down to products which correspond to your requirements. For instance, you might need a project governing tool which specifically needs to facilitate compliance; this would make simple task management tools unsuitable.
When it comes to searching for a product which is orientated towards offering management solutions rather than a simple platform for collaborators, the term Project Portfolio Management (PPM) might come in handy. In a market survey from 2004, Frederik Ahlemann also compiled the following terms that might prove helpful in narrowing down your search:
- Single Project Management Systems (SPMS): Only suitable for individual projects.
- Multi-Project Management Systems (MPMS): Planning-, process-, resource-, and service-orientated.
- Enterprise Project Management Systems (EPMS): Can be used company-wide.
- Project Collaboration Platforms: Offers communication solutions.
Even with these categorizations, however, you aren’t guaranteed to find the best software for your needs. You still need to identify the specific requirements the product has to fulfil.
Which Requirements Does a PM Software Have to Address?
You need to keep this question in mind. You can also go a step further and ask yourself, what do I want to achieve with this software? What value and use will it hold? Additionally, you need to define your business case as a point of departure, as well as address what elements you expect the software to improve; these could include transparency, automated work processes or complying with regulations.
Once you have identified your over-arching goals, you can start with requirements management. For this, you need a structured approach, such as the one recommended in one of our other blog entries (IREB: 6 Steps to Good Requirements). In it, we map out the following steps:
- Demarcate system context
- Analyse stakeholders
- Identify goals
- Detail use cases and processes
- Identify requirements
- Evaluate and document requirements
You also need to keep in mind that there are different categories of requirements which you need to distinguish from each other. These can include functional requirements (i.e. performative features), technical requirements (i.e. interfaces), organizational requirements (i.e. accessibility and restrictions), and general criteria such as price and data protection.
Analysing requirements is usually more of an iterative rather than a linear process. This spans from the fact that not everyone within an organization will value a requirement in the same way; requirements can have different levels of prioritization according to individual outlooks and interests. Requirements themselves can also change over time due to internal and external processes. With this in mind, coordinating and prioritizing requirements in a way that recognizes their volatile and multi-faceted nature is crucial. For this, you might find a points-based system helpful. It could look something like this:
0 = Insignificant
1 = Desirable
2 = Necessary
3 = Crucial
By compiling a total score from the affected stakeholders, you can create a prioritizing criterion. With this, you have a much clearer idea concerning the precise features a software needs to possess in order to address these requirements.
In the Cloud or On-Premise?
Cloud-based software solutions are often referred to as Software-as-a-Service (SaaS) platforms. This entails that the software provider is responsible for providing, hosting and maintaining the software, with user access gained online. SaaS platforms are usually provided through monthly subscriptions, unlike on-premise software whose one-off prices can often discourage uncertain potential customers. In this regard, the flexibility of SaaS platforms can help save you a lot of money if you only end up using them in the short term. However, if you need a product for the long term, the one-off cost of on-premise software usually works out cheaper than a SaaS after a few years, even with the maintenance and hosting costs you still have to pay for on-premise software. On-premise software also differs from cloud-based solutions when it comes to where your data is stored, with the former affording you the extra peace of mind that everything is stored behind your own firewall and installed on your own servers.
With a look to the future, on-premise software is also more desirable for two reasons. Firstly, purchased software lends itself more to customization; with the AI revolution drawing closer, this can’t be understated. Secondly, if you want to optimally calculate process flows based on your own data, the security of that data will play an important role.
Features of PM Software
What does PM actually entail and how can software facilitate it? In a nutshell, PM is a methodological approach to overseeing the planning, implementation and evaluation of individual project phases. PM methodology can vary, however, this principle remains constant. In all phases of work, with teams not always being in the same locations, it’s helpful for them to have access to a central database. This way, updates, changes and progress can be seen by project members in real-time. Regarding how PM software assists these work processes, the following activities can be performed by a software solution:
- Time planning (Gantt charts, calendars, to-do lists, milestones, control flows, critical paths, roadmaps, network analysis, etc.)
- Activity management (task/Kanban boards, backlogs)
- Collaboration (discussion boards, chats, whiteboards, video conferencing, role/ right concepts, contact management, notifications)
- Document management (file sharing, versioning)
- Resource management (employee productivity, personnel planning, time recording)
- Cost management/ budget planning (comparison of plan and outcome)
- Project controlling (dashboards with KPI, earned value analyses, cumulative flow diagrams, burn up/ down charts, velocity charts)
- Reporting (import/ export from MS Office)
- Process management (standardization of project management method, project templates, workflows, automation)
- Requirements management (forms, backlogs, modelling)
- Issue management
- Risk management
- Quality management
- Knowledge management (Wikis, glossaries)
- Test management
Not every project needs software to perform and manage all of the above-listed activities. Nevertheless, some features will be more important for some projects than for others. For instance, a lot of projects are reliant on time, cost and resource management, whilst agile projects usually require metrics for analysing progress. Meanwhile, project managers working in highly regulated industries will need to generate audit-proof documentation and department heads will need to prove the value that a project is providing for their companies. In other words, it’s highly dependent on who exactly is using the software and which conclusions they are trying to draw from the available data.
Nevertheless, PM software should ideally always provide support for building continuously improving work processes. One way of doing this is by creating templates from previous successful projects. This way you improve the chances of a quick and easy start to your project, as well as employing tried-and-tested work processes which can be refined even further through efficient collaboration. In this sense, good PM software can be analogous to a project management office (PMO) when it comes to establishing better standards of management and work processes.
PM and AI
AI will certainly be a significant component in PM software of the future, with voice assistants, Natural Language Processing and Machine Learning set to play major roles. According to a report from Pulse, AI technology will increase both project management productivity and overall work quality:
AI technology and increase in productivity (estimate given as a percentage)
- Robotics and process automation (74%)
- Reinforcement learning (64%)
- Machine learning (61%)
AI technology and increase in quality (estimate given as a percentage)
- Anti-bias solutions (68%)
- Expert systems (61%)
- Knowledge-based systems (59%)
In the same study, data analysis was predicted to be the most affected area by AI. Additionally, 47% of project managers questioned about their experience with AI answered that it had helped them with time-saving.
One big question that arises is how will AI affect the way project managers analyse data in the future? Whilst AI is reliant on big data to learn and evolve for the better, it is also capable of producing high-quality data in ever-greater volume. To use this data to its full potential, however, one requires absolute control.
The world of PM software has a huge spectrum. Naturally, this complicates things when it comes to comparing different platforms. However, you can simplify this by recognizing the value that a prospective software will bring to your own specific context. Integral to this is doing due diligence with your requirements management. You also need to keep the potential of future technologies (i.e. AI) in mind. As argued in a 2020 Splunk study, data analysis creates 67% more product and service quality, 62% more workforce efficiency, and 60% quicker market launches; what seems certain is that centralizing, protecting and analysing your project’s data are becoming ever-more significant factors for success.
 Ahlemann, Frederik: Comparative Market Analysis of Project Management Systems, EiS Universität Osnabrück, 2004.