Promising AI use cases in Resource Management - Part 2.
In the second part of our blog series on AI-driven Resource Management, we focus on the cross-enterprise benefits and new opportunities that artificial intelligence offers at the project, service, product, portfolio, and strategic levels.
Find out from this blog post:
- How AI optimization works at the individual, project, service, and product levels.
- How to better align bottom-up activities and top-down goals with artificial intelligence.
- What to expect from artificial intelligence in Strategic Portfolio Management.
Flashback: Artificial Intelligence and Individual Work Optimization
In the first part of this blog series, we provided an in-depth overview of how generative AI is already helping knowledge workers perform their jobs more efficiently today and will continue to do so in the foreseeable future, and how this can be accurately documented, i.e., automatically. For the first time, generative AI will create a database that can be used for truly layered and meaningful effort estimation and scenario modeling.
How does the use of AI change the project, service and product level?
Thanks to individual, granular work recording, we also gain important insights at the project, service and product level:
- 360-degree Analytics with Impact
Generative AI helps us connect the data we collect and then present it in new contexts, flexibly and at different levels of granularity. In the future, this will no longer require complex, ad hoc data models; disruptive technology will make it possible to query data natively and across teams. The result will be faster and more effective management of enterprise resources. Generative AI will also help create and harness new types of data: An AI analysis of required employee skills based on historical project data, as well as the allocation of existing skills, can become a real game-changer for business productivity in the future.
- The end of Silo Thinking
DevOps has led the way, and now artificial intelligence could be used to transform the entire organization: Nearly every organization is missing out on significant value due to existing business silos. The people who should be driving change tend to act independently of those who need to keep the business running. In IT, the DevOps approach, which relies heavily on automation, collaboration, and shared responsibility for stability and progress, has broken down these rigid boundaries. Generative AI will lead to the classical organization as we know it today being dissolved by new, AI-optimized and, more importantly, skills-based ways of working.
- Repurpose Knowledge and Content
With the use of generative AI, all documented corporate knowledge becomes a resource. In the future, detailed activity descriptions will accelerate the implementation of similarly structured work packages by reusing content. Generative AI will also make product feedback and customer satisfaction surveys actionable for the first time by translating them into planned activities and matching them with existing resources, priorities, and skills.
Webinar Recommendation:
Status Quo and Future Outlook for Resource Management
If you want to be among the AI pioneers in your industry, we recommend this expert webinar on the Evolution of Resource Management in cooperation with ServiceNow and Projektmagazin.
AI-optimized Portfolio Management
Generative AI will enable us to improve Resource Management by using historical data not only at the role level, but also very much from the perspective of required and available skills. Currently, it is almost impossible to consider employee skills in a structured way due to the excessive administrative effort involved. With generative AI, however, it will be possible to identify similarities within planned activities across all levels and optimize them at the portfolio level. Generative AI will revolutionize the possibilities of scenario planning: Content and organizational interdependencies can be better identified and accounted for, resulting in more accurate, automatically generated schedules.
AI potential at the strategic level
As a result, the need for operational management personnel will decrease overall, as individual project workers will increasingly become their own managers thanks to AI. At the same time, the playing field for strategic management will expand. Trends can be identified earlier based on data and contribute to more differentiated investment behavior. The short- to medium-term impact of AI at the strategic level is particularly evident in communications. The intensive analysis of data makes corporate and divisional strategies more detailed, more dynamic and more tangible for operational work and their influence on the achievement of goals. Artificial intelligence will help to ensure that all organizational units are well coordinated in terms of content and communication. In the long term, it is quite conceivable that internal data will be enriched with external data that, although synthetically anonymized, will still be meaningful for benchmarking one's own company against others.
From AI hype to practical use
Generative AI has changed the technology industry and its applications at an enormous pace. However, the foundation of these developments remains enterprise data combined with powerful machine learning models. Yes, it will be possible to collect and process more data automatically in the future. But for sustainability reasons, we will need to consider where to focus our efforts. The quickest and most tangible impact of using AI in Resource Management will be at the individual work level. However, higher levels should already be watching closely and considering how their roles and responsibilities can intervene in this ongoing transformation of work.
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