How Agile will expose your decision-making effectiveness
FYI, this story is more than a year old
Article by Certus3 managing partners Simo Popovac and Michael Devlin.
A key goal for many organisations that go down the Agile path is to move faster.
The language of Agile supports this, packaging work into sprints and delivering new features and products on a regular cadence. So does the live experience: velocity is listed as the third-highest measure of success for individual Agile projects in the latest State of Agile report.
How fast you can go depends on a range of factors.
First, as Atlassian notes, velocity varies between teams. Each team estimates the amount of work it can complete in an iteration differently, and therefore works to a different pace. However, one would expect that pace to increase over time "as the team optimises relationships and the work process", Atlassian says. There is a direct relationship between Team Performance and velocity.
Second, the higher the velocity, the better an organisation has to be at decision-making but making the right decisions.
In our experience, this is an area where many organisations still find they need some help. Artificial intelligence shows tremendous promise in this field because it is able to monitor across a vast array of complex scenarios thrown up in Agile projects and surface timely information and insights that help the business leaders overseeing these projects to adapt on-the-fly, make the right decisions at speed and keep to time.
Bad decisions still abound
Decision-making in Agile organisations is hard.
A survey by McKinsey in April found only 48 per cent of respondents agreed that "their organisations make decisions quickly". Decisions taken at speed were not necessarily good; "just 37 per cent of respondents say their organisations' decisions are both high in quality and velocity," McKinsey found.
As if to highlight that, an earlier study, also by McKinsey, found 72 per cent of senior executives "thought bad strategic decisions either were about as frequent as good ones or were the prevailing norm in their organisation".
That earlier study recognised the role that Agile organisational models could play in getting decision making "into the right hands" and being able to react to or anticipate shifts in the business environment faster.
Yet, adopting Agile by itself is not a guarantee that decision-making speed and processes will improve.
"In the digital age, good decision making entails taking more shots on goal and shortening iteration cycles. However, few decision makers are rewarded for such an approach," a March survey says.
The success of a decision is still measured on the outcome it produces. How you arrive at that decision can be augmented and innovated on, and there is clearly room for that to occur.
A Swedish study on data-driven decision-making presented at the International Conference on Agile Software Development in late May shows the enormous promise of AI in this space.
While 79 per cent of respondents said data was “highly valued in today’s decision-making, a majority of the respondents agreed or strongly agreed that data should play an important role (71 per cent) and be highly valued (87 per cent) when making decisions in the future.
Bringing in artificial intelligence
In an Agile environment, governance is required to understand the metrics that indicate success in overall project terms and what actions need to be taken and when to get there. In that respect, information is power - the power to be successful.
Senior executives responsible for governing and assuring the success of Agile-driven transformation projects are rethinking how they get access to the right information at speed to help make good decisions.
Artificial intelligence (AI) is emerging as a key enabler. AI can assist people to access information that was previously inaccessible in a timeframe and format that enables sound and timely decision-making.
By making use of machine learning algorithms and expert systems, organisations can gather data from across a project and model it in new ways.
AI-based systems can also protect against internal bias and other factors which might weigh on the direction of decisions and results. Within Agile, you depend heavily on teams to accurately estimate how much work they can get through, and on people to provide assurance that things look correct. This is very prone to being influenced by organisational culture, politics and biases.
What is clear to date is that without AI, organisations and executives are far more limited in being able to measure and use the information for fast and accurate decision-making. Data-driven decision-making is the key to unlocking Agile success.