By PJ Mithun
As a seasoned Oracle Fusion implementation consultant with a decade and a half under my belt, I've witnessed the evolution of enterprise applications and its implementation across multiple industries and sectors. We have all read of snippets and possibilities about how Gen AI can add capabilities to ERP landscape increasing the overall productivity of organization.
In this article, I want to run my imaginations wild on how Generative AI promises a new era in Oracle Fusion (or any ERP) implementation’s speed and efficiency, and its undeniable potential to streamline the Oracle Fusion Cloud journey. Let's explore how Generative AI could potentially change some of the traditional approaches across various phases of a typical Hybrid Agile Oracle Cloud implementation lifecycle.
Assessment & Project Planning
Generative AI can now help turbocharge the initial assessment phase.
By analysing vast amounts of historical implementations, AI models can today identify common pitfalls, recommend best practices, and generate tailored risk assessments.
AI can provide multiple phased timelines and implementation options based on the budget and organization’s strategic priorities.
This empowers the project managers to select highly optimized project plans, minimizing roadblocks and ensuring realistic timelines.
Optimizing Requirements Gathering Phase
Tired of those endless requirements workshops and deep dive sessions? Generative AI can be a powerful ally.
AI-powered tools can analyse current state process documents and system configuration to generate standard requirements applicable to the organization.
It can review key user meetings and interview transcripts to extract key requirements, significantly reducing manual effort. This also ensures that none of the user requirements stated by key users are missed.
Moreover, by drawing on industry benchmarks and successful implementations, Gen AI can also suggest additional requirements specific to the industry.
Once trained on the Oracle Cloud documentation and support data, AI tools can perform the fit gap for the specific requirements which can reduce human judgement errors to a great extent. For those requirements marked as gaps, AI can suggest solutions using the RICEW framework and industry recommendations.
System Configuration and Prototyping
Configurations will be one of the faster adoptions where a large portion of today’s effort can be handed over to a Gen AI model.
Gen AI can help ease through the multiple prototyping phases planned in the project (P0, P1, P2…) based on the requirements.
AI can suggest Oracle Fusion configurations based on the requirements. This can also be applied for a global design scenario where a specific tweaked country configuration (US for example) can be extrapolated by AI to all the applicable countries with prior knowledge of localization requirements.
Human intervention will eventually be limited to reviews, validations and sign offs.
Build Phase
There are certain use cases today where Generative AI can truly shine during the development phase.
By providing the list of requirements to be addressed, AI can generate standard functional design documentations and mapping spreadsheets which can then be reviewed/tweaked by consultants as needed.
By understanding the business logic and requirements expressed in natural language, the AI can generate tailored codes or snippets of code for specific functionalities for any of the RICEW objects identified as a part of approved fit-gap analysis.
This will greatly reduce the implementation timeline as development of complex objects always tends to be one of the bottlenecks.
AI can help automate unit test execution to identify any loose ends in the code simulating multiple technical edge cases.
Validation Phase
Validation phase, consisting of one or more System Integration Testing cycles (SIT) and User Acceptance Testing (UAT), has a number of tasks that can leverage Gen-AI.
Generative AI models can be trained on project plan scope, business requirements and system documentation to automatically generate comprehensive test cases and test data for SIT and UAT.
These models can also be used to cover all the edge cases execution to identify any probable defects.
The models can generate comprehensive test reports, summarizing testing activities andresults, making it easier for stakeholders to understand the overall testing progress and status.
The one certain candidate of complete AI automation is performance testing in which an AI model can test and validate the system for processing large volumes of data as per the business needs.
Change Management
Say goodbye to manual training document/video generation.
With the help of AI task managers, generative AI models can be used to generate comprehensive documentation, user guides, and training materials from the GOLD instance for the new Oracle Fusion Cloud applications. This can help employees quickly understand the new systems, processes, and best practices, facilitating a smoother transition.
AI models can assist in drafting clear and concise communications to stakeholders, including emails, presentations, and announcements, ensuring that important information about the change is effectively disseminated throughout the organization.
In the training process, a common AI chat bot can help answer training queries reducing a lot of training consultant and superuser overhead.
Cutover and Go-Live
Time is running out for the historical activities related to cutover planning and task execution management.
Generative AI models can be trained on project plan, project documentation and cutover tasks to generate comprehensive plans tailored to the Oracle Fusion Cloud implementations.
Gen AI task managers can perform automated migration and validation of custom code to the production environment. Models can validate data from source to target and match the data with the defined migration criteria.
Where are we heading?
We, at Strata, are still skimming the surface in terms of opportunities and are investing our time in making solutions for the above use-cases a reality. As we start embracing the power of Generative AI in implementations, the opportunities are going to be limitless. Once these come to fruition, organizations will be able to harness the full potential of Generative AI to drive faster, more efficient, and more successful Oracle Fusion Cloud implementations. With the worldwide Gen AI adoption, I foresee the implementation timelines for large and medium organizations reducing by a factor of 40-50% in the next 1-2 years. The next few months will help reimagine the world of tech consulting and I am super excited to be on the forefront in exploring what shape AI-powered implementations take!
Be on the lookout for our next update on the progress made on these use cases!