A comprehensive framework for assessing and evaluating GenAI implementation

By 2026, more than 80% of enterprises are expected to use GenAI apps or models or deploy them in production. A huge jump from a mere 5% this year! (Source: Gartner)

GenAI offers clear near-term and long-term benefits from productivity boost to automation, cost savings and more. Businesses must work with experts to identify how to leverage AI artificial intelligence solutions and adopt them.

Content Generation and Conversational AI are two best suited cases for GenAI currently. For example, a business in the retail space or healthcare firms, can use GenAI to:

• Reduce repetitive manual tasks with automation (auto-fill data, analyze patterns and create holistic reports etc.)
• Generative AI Chatbots to reduce human dependency: Cut down wait time to resolve basic consumer queries

 
Adopting generative AI requires a clear roadmap for immediate benefits and long-term transformation. Decision-makers, technology leaders, and change managers must focus on understanding four key areas:

 
The tech and tools to implement it: Identify areas of improvement within the business and work with an expert to understand how to leverage a generative AI solution it. For example, GenAI can help with code automation, bug fixes, content generation, test cases, predictive analytics and more.

Define what a productivity boost or cost savings mean to the business:

• Is there a need for artificial intelligence services to eliminate errors?
• Check if there is an opportunity to cut down the efforts on repetitive tasks
• Assess your ecosystem’s ability to adapt to generative AI
• Identify the best possible option to quickly access the relevant data (Cloud/Hybrid)
• Start with what’s available – with minimal customization, modernization, and
  modularization
• Experiment with phased implementation to maximize impact

 
The talent requisites to successfully implement GenAI: It is 60-80% more difficult to hire the right AI/ML talent with the required expertise (McKinsey, 2023). The GenAI talent supply is not able to stay up to date with the demand. That’s why it is important for businesses to work with market innovators who have an expert talent pool and invest in artificial intelligence solutions that have been tried and tested. The build versus buy checklist can help understand tasks to outsource, or work with a partner and in figuring out how to upskill a business’ talent force. Work with GenAI skill-training enterprises who can offer training across all stages of adoption including ideation, design, test, implementation, innovation, and monitoring.

 
Risk aware education and training in the use of GenAI: Data security and reliability are crucial for generative AI models and platforms. To protect private information, check how your model or tech partner ensures data confidentiality and prevents leakage. Confirm that the data is reliable and that anomalies are detected and fixed. Also, consider legal implications, social impact, and business reputation when evaluating these factors. A holistic platform management process should cover all these checkpoints.

To facilitate scaled adoption with minimal risk, establish a core committee with stakeholders from leadership, engineering, IT, finance, legal, and operations. Track impact at every stage and compare theory to implementation to identify gaps and improve efficiency.

Work with established engineering partners from an AI consulting company who offer a transparent framework. Ask questions – understand where your data is fed, how they are using it and how is it impacting your business, at all stages.

 
Strategies to ensure optimal and ethical performance through GenAI: A governance plan can help ensure ethical ways of operating, as in most cases of technology adoption.

Identify the policies, processes, framework, and regulations to adhere to, set up independent review committee, assess, implement, and mitigate risks.

Before developing an assessment framework, understand the key requirements to plan GenAI governance:

• Algorithm: Ensure that the chosen GenAI algorithms minimize biases, errors, and potentially harmful outputs
• Data: Verify that the data used to train GenAI models complies with data privacy regulations
• Usage: Be sure to understand how GenAI is utilized, establish, and uphold guidelines and policies for usage, security, privacy, and compliance

To ensure optimal performance from your GenAI tech, always track periodic impact. Identify areas of improvement and be agile in implementing it. Map what your proof of concept showcased – is the solution focused on saving 30% cost of production in 6 months? Check if you are on track to achieve it.

Ensure to democratize GenAI, so that business users, the tech and governance process are in place.