This article outlines why a strategic, portfolio-integrated approach to AI is essential, and previews a key principle from Planisware's webinar with thought leader Andy Jordan: how prioritizing business value is the foundation for scalable, sustainable AI success.
There aren’t many areas of organisational operations that aren’t being impacted by AI these days. In a very short space of time, it has started to influence how work gets done in virtually every department and function. And that is causing a significant number of challenges.
Adoption and use of AI are just as likely to be driven by individuals, teams, and departments as by the enterprise as a whole. That is resulting in disjointed, standalone implementations that aren’t optimising their contribution, and may be exposing the business to significant risk. It’s being made worse by the incomplete and inaccurate data that is training those AI solutions.
Policies and governance are lagging behind adoption due to the sheer speed of advancement of AI; and the disjointed approach being taken by organisations is only making things worse. Then overlay the current focus on just efficiency rather than also striving to deliver improved effectiveness, and it’s no surprise that AI investments aren’t delivering optimal value – if they are delivering any value at all.
That’s not sustainable. Especially in a world dealing with economic uncertainty, increasing AI regulation, and ever tightening financial pressures. To succeed with AI, enterprises must take a consistent, integrated, strategic approach. And they must do it immediately.
Optimising value
At Planisware, we are focused on helping organisations address these challenges. We partnered with global project and strategy thought leader Andy Jordan to present a webinar with some key actions that you can take to improve your approach to data management, AI policies, and governance. Andy also explored how businesses can focus on business value to drive scalability and growth, and on how to thrive in a constantly evolving AI environment.
In this article, we want to give you a preview of one of the key elements. That’s the ability to prioritise value. What does that mean? Let’s explore.
In the rush to avoid being left behind, many organisations are implementing AI without a clear strategy. That hurts them in many areas:
- A data infrastructure and management approach incapable of supporting effective AI use.
- Unclear or lacking policies and governance frameworks to manage AI.
- Inconsistent selection of tools and determination of use cases.
- Failure to align AI investments with other strategic priorities resulting in work that will never be able to benefit the enterprise.
This is not an approach that would be taken with any other strategic investment, and that’s the key. AI initiatives must be treated as no different from other projects.
The pressure to pursue AI projects is driven by the fear of being left behind by competitors in a fast-paced area of business. But if you aren’t going in the right direction, speed doesn’t help. Organisations must understand the work needed to prepare for AI – primarily complete and accurate data, robust policies, and meaningful governance frameworks. They must commit to those three elements to create a foundation for AI work.
They must then look for AI solutions and approaches that can be applied consistently across the enterprise and that can be scaled to provide a springboard for future growth. Most critically of all, they must integrate AI projects into the strategic portfolio to ensure alignment with all of the business-critical work that is underway and planned.
How can that be done, and what else do businesses need to do to optimise the return on their AI investments?
