Project Business Automation: The Foundation to Successful AI Implementation
This article is part of a series of blogs on Artificial Intelligence in Project Business. See the rest of the articles in the series at the end of this blog.
For many project-based companies, or Project Businesses, realizing the potential of artificial intelligence as a driver of growth, productivity and efficiency can seem daunting and almost impossible. Not surprising seeing as it would be a major shift from living in the past to predicting the future.
While traditional industries such as retail and manufacturing have had many successful AI implementations, Project Businesses continue to struggle to establish a data infrastructure to reap the benefits of AI.
Data is Key to AI, But Not Just Any Data
According to a Forbes article, “Data may be the foundation of all AI initiatives, but it also presents huge challenges, especially at organizations not deeply experienced in investing in advanced or disruptive technologies.”
The article continues to dive into three core data challenges organizations face when trying to adopt AI: not enough data, too much data, and/or bad data.
As mentioned in our previous blog, data isn’t lacking in Project Business. The problem is this data is not stored and managed in a standardized way. By using a host of disparate applications and tools to manage their core business processes (e.g. project management, resource management, asset management, project accounting, time & expense, budgeting/costing, etc.), there’s little to no data integration, except through manual consolidation. In this fragmented environment, Project Businesses are left with a large amount of unstructured data that is difficult to consolidate and organize into any meaningful format that AI can utilize.
To make meaningful use of AI, Project Businesses need to produce real-time project financial and operational KPIs that are recorded over time. And to do so, they need to integrate all their processes and data into one comprehensive business system.
Project Business Automation (PBA) stands uniquely as a systematic approach with the capabilities to provide the foundation for AI use in Project Business.
PBA provides three key elements to structure your processes and data for optimal AI adoption.
Standardization
Unfortunately, unlike traditional industries such as retail and manufacturing, Project Businesses do not have standardized processes. Project Businesses need to standardize their processes to produce standardized data and metrics. PBA accomplishes this prerequisite by systematically structuring processes around best practices. As a result, the enterprise turns normally chaotic operations and unstructured processes and data into a well-controlled environment that produces highly structured performance data and metrics that can be used for an effective AI implementation.
Integration
With PBA, Project Businesses can eliminate the need for multiple point solutions because all processes and data are managed in the same system. From project inception, budgeting, and costing to scheduling, issue and risk management, to month-end processes, project close, financial analysis and more, everything is integrated to provide seamless workflows and data management across the enterprise.
PBA takes the financial and operational aspects of projects that are normally managed in separate systems and now evaluates and considers them simultaneously. With PBA, the project manager and the project controller can see the financial impact of a schedule change immediately, while executives understand the health and risk of their projects in real time.
This allows the entire enterprise to operate more fluidly and from one source of truth, making a strong foundation for AI.
Automation
By systemizing and integrating all processes and data, PBA can produce the necessary real-time financial and operational metrics that AI can use to make valuable predictions. Automating workflows and the production of this data is the key to this achievement. The ability to produce standardized financial and operational metrics in real-time and then storing that data in a time-series format is key to making AI effective in Project Business. PBA automates the collection and presentation of this data.
This automation means the entire organization is brought into real time and all stakeholders are up to date on the state of their projects. As a result, they can spot issues earlier and take action to mitigate risks before they become significant problems.
In addition, by automating your workflows and data, it’s already structured in a way that AI can ingest and use to make accurate predictions as to the final budget and schedule of a project.
Take the Right Approach
While AI is achievable for Project Businesses, it’s important to establish foundational project intelligence data that can be fed into an AI. If AI is only as good as the data you put into it, it’s important to not just dump loads of outdated or bad data into it. Predictive AI needs structured data stored in a time-phased manner to “learn” and make accurate predictions.
CEO of ADEACA - Daniel founded ADEACA in 2007 to address the needs of project-driven organizations who were lacking a holistic solution for project and financial management. As one of the principal architects behind Microsoft Dynamics AX, Daniel was in a unique position to recognize the best way to fulfill this need was inside the ERP. ADEACA was born.
Prior to founding ADEACA, Daniel was a principal architect and product director of Axapta at Damgaard Data, which was acquired by Microsoft in 2002 and later became Dynamics AX and now called Microsoft Dynamics 365 Operations. His experience at Damgaard equipped him with the skills to create user-centric software, an art form not practiced by the large, generic ERP companies.
