This is the first blog in a three-part series on change management. Look for parts two and three in the upcoming weeks.
Change is hard. There are articles all over the internet that talk about how changing small things in your daily life — such as walking 10,000 steps, sleeping 6-8 hours or drinking at least a gallon of water — can lead to a healthier life. Many lifestyle experts say it takes a minimum of three weeks for a new behavior to start to become a habit. Sustaining change in behavior can be hard. Anyone who has joined a gym on January 1 as part of a New Year’s fitness journey resolution — only to see their membership card start to gather dust in the back of a drawer by the time February rolls around — can probably relate. Implementing and sustaining new habits requires a mature, disciplined approach.
When COVID hit, we all made a concentrated effort to adapt to a “new” world order. Industries and organizations were faced with sudden and significant market uncertainties. Early in the pandemic, there was a massive shift in demand for Pulp & Paper, Pharma, Metals and Oil and Gas industries. Operations had to be re-calibrated to hold up against these wide swings in demand. Risk of failure was likely higher as equipment was operating outside its typical range and operators lacked experience in such operating regimes. Loss of an asset wouldn’t just result in downtime but could create complications in supply chains and result in safety issues.
Faced with such uncertainties, industry is adopting artificial intelligence/machine learning (AI/ML) tools to find new ways to execute, become more efficient and squeeze more value from assets in response to market changes. While for many companies digitalization and sustainability initiatives were underway pre-COVID, the pandemic created a sense of urgency to fully embrace and adopt digital technologies. As new tools and techniques have entered work environments, mature change management strategies have become paramount for success.
Most organizations evaluating AI/ML technologies have to prepare and plan for change. Success factors must be clearly defined and be measurable.
In our recent conference, OPTIMIZE 2021, several clients spoke about deploying and sustaining AspenTech’s AI powered prescriptive maintenance tool, Aspen Mtell®. A common thread: challenges faced in adoption and how each organization managed this change in their way of working. Change management strategies which address all aspects of organizations — people, processes and tools — were described as essential to success in these uncertain times.
Here are some key questions to ask as you are considering new tools or processes for your organization:
- What is the timeline for deployment and value capture?
- Do we need to upskill our workforce?
- How do we ensure cross-functional teams are able to work together effectively?
- How can we utilize our resources efficiently?
- How do we keep our workforce engaged and ensure successful adoption?
As most successful fitness journeys show, developing a detailed plan with trainers, breaking down goals into bite sized or achievable steps; setting clear expectations for results and measuring progress against the plan is the key to success. Change is possible when the objective is clear and solutions that help you meet your objective are simple, effective and sustainable. AI/ML based tools are accelerating productivity improvements, driving operational excellence and enabling companies to meet their sustainability goals. The key is to leverage a cost-effective solution that’s reliable, scalable and will be embraced across the enterprise.
In this series on Change Management we will explore how some of AspenTech’s customers have begun to realize the full potential of AI/ML technologies and how their organizations have embraced new ways of working.
In the meantime, see how AspenTech customer LUKOIL avoids downtime with Aspen Mtell.