measuring performance

Harnessing the Power of AI in Project Governance: Driving Better Decisions, Reducing Risks, and Achieving Goals

Posted on by holnor

Introduction

In today’s fast-paced and complex project management landscape, effective governance is the key to ensuring projects are delivered on time, within budget, and according to established objectives. As the scale of projects grows and variables multiply, project governance teams are increasingly turning to artificial intelligence (AI) to gain a competitive edge. By leveraging AI-driven tools and analytics, organisations can transform traditional governance processes, redefining how risks are managed, resources are allocated, and progress is monitored.

Enhancing Risk Management with AI: Predicting Challenges Before They Arise

Managing risk in large-scale projects can resemble navigating through a dense forest with limited visibility. AI serves as an advanced compass, leveraging historical data, trends, and patterns to predict potential obstacles. AI tools analyse past project outcomes, market trends, and a variety of external factors to anticipate risks and flag potential issues early on. Whether it’s identifying supply chain vulnerabilities, potential resource shortages, or compliance risks, AI empowers governance teams with actionable insights, enabling mitigation strategies to be implemented before risks escalate into project roadblocks.

Predictive Analytics: Transforming Forecasting Into Precision Science

AI’s predictive analytics capabilities are revolutionising project governance by enabling more accurate forecasting of critical variables such as costs, timelines, and resource utilisation. By analysing historical project data in conjunction with current inputs, AI algorithms generate highly reliable forecasts, helping project managers make data-driven decisions. The days of relying on gut instincts and broad estimations are giving way to a new era of precision management, where data ensures that plans and execution are closely aligned and surprises are minimised.

Real-Time Dashboards: The Evolution of Project Monitoring

One of the primary responsibilities of project governance is to track performance against goals and ensure compliance with organisational standards. AI-powered real-time dashboards are not just tools to visualise data but are transformative platforms that offer deep insights into key performance indicators (KPIs). These dashboards can integrate data streams from multiple sources, simplify complex scenarios, and provide stakeholders with instant updates on project health, resource allocation, budget adherence, and risks. Moreover, AI-driven features such as anomaly detection can highlight deviations and foster proactive corrections—keeping projects on track with minimal manual oversight.

Challenges Along the Way: Adapting AI to Legacy Project Governance Systems

As promising as AI might be, integrating it into existing project governance frameworks comes with its share of challenges. Legacy systems, many of which are rigid and outdated, can make it difficult for organisations to reap the full benefits of AI tools. Data silos, resistance to change, and the need for upskilling teams to understand and apply AI insights are common hurdles. Organisations will need to invest not only in technology but also in change management programs to ensure successful adoption and alignment with their governance practices.

Success Stories: Real-World Applications of AI in Project Governance

Despite these challenges, several industry leaders have already paved the way, showcasing what’s possible when project governance aligns with AI-powered solutions. For instance, in the construction industry, predictive analytics tools are helping firms forecast project costs and timelines with unprecedented accuracy. Similarly, AI-enabled risk assessment software is aiding financial institutions in managing compliance risks in complex projects, while manufacturing companies are using real-time monitoring systems to track supply chains and resource availability.

The Future of AI in Project Governance: A Shift from Reactive to Proactive Management

As organisations around the world embrace digital transformation, AI presents an unparalleled opportunity to redefine project governance. By offering predictive insights, real-time monitoring, and data-driven decision support, AI helps project managers and governance boards focus more on strategy and value creation. However, the journey to successful integration will require carefully navigating implementation challenges, fostering a culture of openness to innovation, and ensuring the right mix of technology and talent.

Ultimately, the integration of AI into project governance is poised to be a game-changer, helping organisations achieve their strategic objectives more effectively while embracing a culture of informed and responsive decision-making. Organisations that embrace this transformation now stand to lead the way in shaping the future of project management.

Is the Cost of Innovation Worth it?

Posted on by holnor

A great article explaining why focusing solely on a businesses current core strengths is not always a good thing. At least not in the long term.

https://hbr.org/2014/02/the-strategic-mistake-almost-everybody-makes/

I find it fascinating that many intelligent people fall into the trap of thinking that because their company has a competitive advantage in an area (whether  a market segment or a specific product) it means that they do not have to keep innovating. Often it’s born out of a fear of cannibalising the sales of existing products or what the market will think of their margins or a fear of competition. But look at three of the most successful companies going, Apple, Amazon and Microsoft.

First Apple is in no way shape or form afraid of cannibalising their own sales. If they were they would not have introduced the iPad Mini, the iPod touch or the iPhone 6 plus.

Second Amazon, Bezos has steadfastly ignored the markets desire to see Amazon’s profit margins increase. Instead Amazon continues to stick with margins that would barely allow others to continue operating. The strategy… grow market share. Amazon wants a share of every $ spent on line. This is the thinking that underpinned the Amazon Market place. Rather than competing with every online seller as well as with eBay, Amazon created a platform whereby everyone is a partner and pays them for the privilege. The pay off is starting to be seen as Amazon is now the first place most head when searching for products online.

Microsoft may not be first to the party but with pockets as deep as theirs they are able to play a long game. Don’t believe it? How about the Xbox it started out as the bit player in the console gaming market dominated by Sony and Nintendo. Now Nintendo are an after thought despite being the first to introduce motion sensors into gaming. Also the introduction of Office 365 subscriptions that include the right to install versions of office on multiple devices at no extra cost. Great for the consumer but not so good for the bottom line as they are foregoing revenue  on those sales in order to keep market share away from Google Docs (also Microsoft are still in the search game. Not nearly on the scale of Google but still enough for it to be worthwhile).

Speaking of Google it has been shown relying on advertising revenue to support a suite of free apps is not necessarily the best business model.

So next time you hear someone bemoaning the amount of money being invested into new product R&D tell them to stop being so short sighted and start thinking in terms of the long game!

The Trouble with Metrics

Posted on by holnor

Over the years I have worked on a number of back office system projects. Typically they projects are aimed at delivering either cost savings or performance improvements (or optimistically both). Naturally management are eager to monitor and calculate how much of each is achieved. To that end they look to set up a number of measurements that will show the level of uptake, throughput, cost per unit etc.

When the system is designed to make it easier for someone to do their job you would expect that their productivity would increase… Makes sense right? But it’s not alway the case. For example implementing a system that makes it easier for a sales rep to capture the client details, record conversations and track follow up actions will only increase the productivity of the rep if their job doesn’t require them to meet the client face to face. It would be pointless therefore trying to measure the benefit of the new system and the performance of the rep on the basis of the number of phone calls the rep makes each day. If you do then the result of that will be to encourage sales reps to sit at a desk and make phone calls. Not a good result when what you really want from them is to be generating new sales and building relationships with clients so that they become repeat customers.

Therein likes the problem with metrics… Get them right and they will be able to tell you a story of how your business is performing, identify areas for improvement and highlight those activities that are contributing to good performance. Get them wrong and they have the unintended consequence of influencing peoples behaviour toward irrelevant tasks that should not be their focus.

A classic example of this is the UK government target that no patient should wait more than 2 days to see a doctor. The result… When you ring up to make an appointment many doctors won’t allow you to make an appointment that is more than 2 days in the future as it will distort their statistics. Additionally if there are no appointments available you have to ring back the next day in order to make an appointment for 2 days in the future (rinse and repeat until you get in early enough to beat the rush). They do this as the government is not interested in how many people are unable to make an appointment on their first, second, third (or more) attempt. But I’m almost certain this was not the behaviour that the government was trying to encourage. It’s just the unintended consequence of a poorly thought out and defined metric.

So when determining what metrics should be used to measure performance, you must determine (either for yourself or your client/customer):

  1. is the metric relevant (to the job and/or system)
  2. what behaviour is use of the metric likely to encourage (and is that what you want to happen)
  3. is the information the metric conveys actionable
  4. what are you going to do with the information

If the answer to any of the above is negative then it’s probably not the right metric to be using. On the bright side though, being able to identify this will make you look good and help avoid some of the post implementation pitfalls that beset many projects.