Recession and AI investments
When I first wrote this note in October 2019, the threat of a recession was a possible scenario, but not a given at all. Now, with COVID-19 economic slow-down is a given and a recession plausible. So cost containment preparation should be something every leader is working on currently.
PETER SONDERGAARD | CHAIRMAN OF THE BOARD, 2021.AI | THE SONDERGAARD GROUP | MARCH | 2020
Its ten years since the last recession, which will mean most of us have forgotten how to manage costs during a period of cost containment. Furthermore, a new cadre of first-time managers will have no experience, at all, leading through such a period. Lastly, Artificial Intelligence and Machine Learning were not as evolved in 2008/09 and, therefore, were not considered in the planning organizations did. AI is new, which means hard measures of benefit may still be challenging to produce because we have minimal experience with it.
In a recessionary environment, artificial intelligence (AI) and machine learning (ML) can actively support your organization’s focus on cost optimization. Here are a few things to consider in reviewing your AI projects and your AI strategy:
- Get realistic about implementation timelines: Most AI projects take much longer to implement than initially assumed. Now is the time to review your expected implementation timeline and to categorize you AI projects based on when you will commence realizing the benefits of the implementation.
- Get specific about business benefits: Most AI project plans are not very specific about the business benefits, the outcomes of the solution, or the KPIs of the project. Categorize your AI projects based on business benefit understanding, which drives revenue, cost optimization, and which AI projects mitigate business risk.
- Get educated about the hidden costs of preparing processes, data, people, and the organization. AI projects have a large number of hidden costs and challenges that impede the deployment of AI solutions. The lack of defined process flows can slow down the implementation of any software solution, including an AI platform. Lack of data architecture and preparedness is one of the most frequent challenges organizations face but in initial deployment and scaling of AI solutions. The people in the organization affected by the AI solution may not be ready to absorb the changes caused by the solution.
Furthermore, managers in the organization may lack leadership skills to deal with artificial intelligence in their organization (see the blog post “The Seven New Leadership Skills“). And lastly, deploying an artificial intelligence (AI) software solution may require organizational changes that the organization is not prepared to address. All these challenges may impact the project timeline and business benefit. While addressing these issues is always important during a period of economic downturn, it is particularly important.
- Get focused on data science and AI/ML skills: During an economic downturn, skills may become available from organizations that are making them redundant. But, equally, be prepared for scarce talent moving to other mission-critical projects. Understand how dependent you are on your data science and AI/ML skills and what impact it has on a project losing some of them. Equally, consider how you want to market your AI/ML and Data Science center of expertise to make it attractive to join.
- Get realistic about “blue sky” projects: Some organizations have several explorative AI/ML projects with the purpose of both gaining experiences but, in some instances, also projects that are too ambitious. In preparation for a potential economic downturn, organizations should be realistic about projects that are less specific or speculative and remap resources to AI projects that deliver certain business benefits.
- Get detailed about integration: All AI projects involve a level of integration with other parts of the organization’s technology architecture, beyond the apparent data integration. Do a cost optimization risk assessment covering all the integration points between the AI project and the entire software architecture. Key elements that the AI projects integrate with may be delayed or dropped due to cost optimization — understanding these risks sooner rather than later is essential.
- Get close to your third party AI solution providers and software vendors: In all recessionary environments, your partners in the form of integrators, consultancies, and software providers will also be under pressure. Evaluate them now based on their economic position and even their current customer base. A solid customer base will be a good indicator of how a provider can get through an economic downturn.
Over the next 3-4 years, artificial intelligence will transform business processes, customer interactions, supply chains, and ecosystems. However, should an economic downturn happen in this period, it is advisable to prepare now to secure the most critical artificial intelligence projects. As organizations continue their digital transformation, review all projects. However, given the future importance of artificial intelligence, CEOs should be specifically focused here.
Source: The Sondergaard Group
Chairman of the Board, 2021.AI
Peter Sondergaard is currently Chairman of the Board at 2021.AI and Owner of his Executive Advisory company, the Sondergaard Group. Before this, Peter worked as the Executive VP and member of Gartner’s operating committee for 15 years. Peter is a well known and sought out speaker covering many topics within IT, AI & ML.
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