A comprehensive approach, not siloed proofs of concept, will allow organizations to serve customers better and improve their economics.
Many organizations have become more ambitious with applying generative artificial intelligence (AI) to internal operations and customer interactions.
Over 80% of organizations are planning to actively reshape their data architectures soon, anticipating the transformative potential of AI.
But scaling up GenAI requires shaping a strong strategy, developing the right sequence of use cases, expanding data analytics talent, engineering the appropriate operating model, and adding the most suitable technology architecture.
CIOs will have to adeptly manage cost, resources, and the organization’s risk profile, all while promoting innovation that will create value for customers and the company.
However, as with any new or evolved technology, success is not a given, and generative AI will be most effective within the larger context of business strategy and broader technology capabilities.
Currently, some innovative marketing teams in many industries generate personalized content at high speed, producing over a hundred ads in minutes. Coding assistants promise to raise productivity for certain tasks in IT, such as code documentation, by up to 50%. In banking, initial estimates indicate process efficiencies of up to 40%.
As use of the technology has spread, many companies have realized they need a more comprehensive posture than the current siloed proofs of concept. At the same time, the swelling wave of rollouts demands a sharper focus on managing the company’s cost, resources, and risk profile, without crimping innovation that creates value for customers and the organization.
Prioritizing use cases should account for value, effort, and risk
To be sure, generative AI will not solve all challenges by itself. The technology works best when orchestrated with other machine learning processes and systems, and it raises its own organizational, technological, regulatory, and ethical challenges. That’s why it is critical to draw up a use-case roadmap, including the capabilities shared across use cases and the bottlenecks that might emerge during development and rollout.
Some organizations that began their generative AI journeys by integrating the technology into lower-risk initiatives that still involve humans are now becoming more ambitious, implementing solutions with greater scale.
An efficient yet scalable approach to delivering use cases typically relies on five steps.
- Design solution & assess risk factions & impact
- Assess data, model & integrations while designing functional & non-functional tests
- Develop & test solution for performance & security
- Review control plan & tools and execute automated deployment
- Monitor performance & risk factors while continuing to improve based on learnings
Rapid ROI is now central to strategic investments: reducing operational costs through process improvement and automation.
Smooth digital customer processes are essential for client satisfaction, engagement, and loyalty. IMSS is successfully driving end-to-end transformation around the globe in all major industries. Together with our clients, we streamline operations by improving processes and automating manual tasks. The result: reduced error rates, faster customer response times and less cost for our clients.
Over recent years, our clients have understood the need to scale initiatives to handle complex, end-to-end processes. Generative AI driven approaches become increasingly relevant to steer these initiatives, providing objective insights from data, replacing manual intensive process discovery or monitoring moving to autonomous operations.