Organizations can better harness the power of ecosystem innovation in their Oracle ERP transformation journeys, Anindya (Andy) Chaudhuri, senior partner at IBM Consulting UKI, shares.
Generative AI continues to be the topic of conversation in the news and the boardroom. Research from the IBM Institute for Business Value reports that 64 percent of CEOs say they face significant pressure from investors, creditors and lenders to accelerate adoption of generative AI. With pressures from external stakeholders to innovate and various economic and geopolitical pressures impacting business, organizations must balance the value generative AI can create against the investment it demands and the risks it introduces.
To help tackle that, consultancies are leveraging the power of generative AI to help clients in their Oracle ERP transformation journeys. For example, by embedding generative AI into IBM’s Oracle transformation programs, it is possible to unlock value early, improve user experience and enhance productivity.
From our experience, customers tend to have two key requirements in relation to their ERP transformations: the need to modernize enterprise processes to meet the evolving needs of the business and the fact that boards are pushing these transformations to be made more quickly and deliver ROI much faster than in the past.
For those embarking on a journey with Oracle SaaS, we’ve found leveraging a preconfigured Cognitive Enterprise platform for Oracle, which includes industry-specific automation and a comprehensive suite of generative AI capabilities, can help meet these needs.
One way to approach the embedding of generative AI into Oracle transformation programs is by starting with four key tenets as a base, including driving early value, embedding generative AI into core business transactions, expediting program delivery timelines and redefining the target operating model.
Driving early value
The typical back-office transformation journey across finance, procurement and HR processes for a global commercial organization could take anywhere between 18 to 24 months. However, business leaders looking to begin their digital transformation journey should consider deploying generative AI platforms as early as three months into the program.
One example of a generative AI solution that can help drive early value is a Conversational Assistant that can be accessed via the client’s collaboration platform – think direct messaging and group messaging tools – enabling employees across the organization to query information such as policy, process and procedures across departments. The Assistant would be able to answer questions by ingesting large volumes of policy and process documents and through training be tuned to deliver the necessary results.
By deploying a generative AI platform early into a multi-year enterprise program, an organization can begin to realize benefits such as establishing a common and intuitive user interface to act as a convergence point across users and departments.
Furthermore, the creation of a unified digital experience layer makes changes to back-end systems less obvious to the users who are already leveraging the platform.
Embedding generative AI into core business transactions
Business leaders across the C-suite are realizing that while they can’t afford to spread funding equally across departments, they can develop a holistic approach to generative AI that touches many processes across their organization.
Today, generative AI platforms can be embedded into Finance, HR and Procurement processes in alignment with the design of an organization’s operating model. As an example, IBM leverages its own library of use cases, by function and industry, to augment processes and create efficiencies.
Some examples of commonly referenced use cases include improving talent acquisition to reduce time to hire, enhancing skill development processes to improve employee engagement, quick decision support for finance by crafting narrative reporting capabilities, intelligent invoice management to improve collections and driving sustainability objectives by enabling responsible sourcing.
Using generative AI to expedite program timelines
Generative AI frameworks are now also being designed to accelerate the development lifecycle, ensuring that companies can implement their Oracle ERP systems quickly and effectively.
With that function in mind, the use of generative AI could potentially include areas such as persona creation, drafting functional and technical specifications and augmenting test cycles.
Redefining the target operating model
One of the ways to reimagine the future of service delivery is by creating a unified, intuitive and personalized experience for clients’ users designed to unlock efficiencies.
Therein, the fourth and crucial part of the generative AI solution will be to extend the use of a Conversational Assistant to drive the future services management paradigm by delivering a simple, consistent, intuitive, personalized, secure and unified set of service management processes.
The goal here would be to direct all client users across departments to use the Assistant as an entry point for any service queries. The Assistant would then prompt the users and try to resolve their queries through an intelligent conversational dialogue.
If the Assistant is unable to resolve the query, it will automatically create a service ticket on behalf of the user and route it to the appropriate service team or will guide users to an appropriate channel or application to raise the query.
This proposed solution aims to exponentially improve services for all key stakeholders and unlock value across the service value chain.
As we have seen internally at IBM, using a generative AI-infused service model like this could resolve upwards of 60 percent of typical user queries within the Assistant without the need for additional resolver groups.
Generative AI has the potential to transform the way organizations approach ERP transformation. By combining the power of AI with the capabilities of Oracle ERP systems, clients can achieve greater efficiency, productivity and value from their technology investments.
