Generative AI adoption is the buzzword for businesses currently. While from a technology perspective, it is redefining the way we operate, enterprises are slowly navigating through the challenges and complexities in its adoption.
Gen AI would be ineffective without roadmap and usability testing. All decision makers, technology leaders and change managers, must focus on understanding four key areas in generative AI offerings:
Evaluating the use case of Gen AI solutions: Identifying and prioritizing areas that incur maximum costs or poses a need for improved efficiency/automation. Map this to gen AI relevance and business impact. Collect data – this will serve as the base for intelligent solution mapping. All and any decision regarding evaluating the need for gen AI, is mostly data driven.
Define what kind of productivity boost you are seeking from generative AI offerings. Is it mere automation to reduce manual, and repetitive tasks? Or does your team need to reduce errors in data management? Data optimization is a broad spectrum. Would you benefit from predictive analytics or personalized data? Work with artificial intelligence experts in the field to define it.
BFSI businesses have benefited from predictive analytics in mitigating investment risks. The telecom industry has benefited from chatbots with personalized query management with issue resolution suggestions.
Assessing the resources at hand in deploying Gen AI for use cases: Only 1% of the global workforce has the skills needed to develop and deploy AI. (McKinsey, 2023) Working on adopting gen AI without assessing internal abilities or external partnerships might require significant talent overhaul.
Check your existing pool of talent for a wide range of skillsets including AI and ML specialists with expertise in algorithms, models, and AI techniques. Assess training and upskilling requirements for potential workforce well-versed in generative AI development. Work with gen AI skill-training enterprises who can offer training across all stages of adoption including ideation, design, test, implementation, innovation, and monitoring.
Resources in hand also include the tool and technology requirement assessment. Plan for a comprehensive toolkit model development, invest in the right platform and partners. Would you require an upgraded cloud platform? Trained APIs? Or next gen chips? Discuss, evaluate, and then invest!
Evaluating the technical and business impact of leveraging Gen AI: Check its ability to generate content or solution for technical impact (improved accuracy, relevance, and consistency), business impact: (improved CX, operational efficiency, and cost savings)
Ensuring the readiness of data through its planned collection, curation, and governance: Not all data are useable, not all data are readable (AI/ML model-wise). All gen AI solutions are powered by a data foundation. It is primary to train and improve the ML models.
Now, how do you “prepare data,” that can be used? Simple – focus on identifying the data requirements for specific use cases – the kind of data sources to be assessed: text, image, or audio? Then curate it. This includes data mapping, fixing anomalies, and ensuring that quality data is fed in the system. Once you’ve curated it, label and deploy it. And always ensure manual and automated quality checks to assess live performance.
Privacy, security, and confidentiality – three prime areas in gen AI data management. Develop a gen AI risk assessment framework within the organization that offers clear view in:
- Preventing data leaks
- Ensuring data reliability
- Define ownership
- Assess and fix biased data output or unethical responses
Plan change management with a governance team and always track periodic impact. Identify areas of improvement in generative AI development and be agile in implementing it.
Gen AI is an evolving technology currently. Successful early adoption can offer several benefits – but there are caveats to going all in without a plan.
