Big Data

Enterprise Readiness For Generative AI Adoption


AI technology is changing the way the world does business. Generative Artificial Intelligence (Generative AI) refers to the use of AI to create new content, like text, images, music, audio, and videos. It can produce a variety of content covering images, video, music, speech, text, software code and product designs. Generative AI helps in faster product development, enhanced customer experience and improved employee productivity.

According to Gartner, “Any organization in any industry, especially those with very large amounts of data, can use AI for business value.” Generative AI is primed to make an increasingly strong impact on enterprises over the next five years.

Recent CEO surveys show almost 80% of CEOs believe AI is likely to significantly enhance business efficiencies in their organization – Forbes

Today, an estimated 60% of IT leaders are looking to implement Generative AI – CIO.com.

44% of the worker’s core skills are expected to change in the next five years. Training employees to be able to leverage Generative AI is going to be critical – World Economic Forum.

Business Case for Generative AI

Many CXO’s see the IT budget as an area of overspending and are continuously looking for ways to reduce costs and effort.

Driving business outcomes with Generative AI requires strategy and collaboration from enterprise teams. The following strategy level questions help to understand about the enterprise readiness for the Generative AI adoption.

  • Is there a CXO mandate for Generative AI
  • Is there a top management-level charter for Generative AI tied to one or more of the drivers.
  • How does the Generative AI helps in enhancing existing processes and enterprise strategy
  • Who will make the final decision on the Generative AI initiative (business only, business and IT, IT only)
  • Is there an internal business case built? If so, at what level
  • Is existing MLOps-tech stack and platform licenses fuel Generative AI, or are third-party services required
  • Does the workforce possess the skills to use Generative AI, and what are the implications for talent acquisition and upskilling
  • What risks emerge when deploying Generative AI and how do these risks impact Generative AI value
  • What current and expected laws and regulations concern the use of Generative AI
  • When would the Generative AI initiative start (next 3 months, next 6 months, 1 year+)

Generative AI Adoption Steps

The following are the steps to follow to perform Generative AI adoption across the enterprise.

Figure 1: Generative AI Adoption Steps

Critical Success Factors of Usage of Generative AI

In most cases, the IT department of enterprises initiates Generative AI adoption in response to business pressure to reduce the cost. They start the initiative with a lot of enthusiasm, and over a period, it dies down on its own. This could be because of a lack of commitment from top management, shifting the focus to some other new initiative, poor planning and unrealistic expectations.

The following are the critical success factors to be addressed by Generative AI initiatives across the enterprise.

The CXO need to focus on,

  • Strategize and lead in governance
    • Establish a Generative AI governance council to help guide enterprise decisions
    • Ensure that Generative AI strategy to align with business strategy
    • Clear communication of objectives of Generative AI to respective stakeholders
  • Obtain peer buy-in
    • Articulate the benefits of executing the Generative AI, as well as the costs and risks to the business
  • Define metrics
    • Access to and active participation of all the stakeholders
    • Bring in the business
    • Establish a culture of responsible AI
  • Maintain momentum
    • Monitor the Generative AI initiatives through regularly scheduled reviews
    • Demand regular updates on modernization projects
    • Encourage employee interest in generative AI

IT leaders to focus on,

  • Conduct regular Generative AI adoption reviews
    • Deploy experienced team of consultants with right mix of skills
    • Identify the applications that quality for the adoption of Generative AI in terms of meeting business needs in a cost-effective and reliable manner
  • Determine a recommended course of action
    • Create a Generative AI adoption framework
    • Streamline data sources, talent, and technology
  • Build a business case
    • Articulate the costs and risks of each potential Generative AI project
    • Prevent employees from launching untested and unregulated AI projects
    • Allow employees to experiment without the ability to operationalize the use of generative AI
  • Establish Centre of Excellence
    • Upskill employees in Generative AI
    • Building use cases and minimum viable products
    • Prompt definition and fine tuning them

Generative AI Team to focus on

  • Collect relevant and meaningful data
    • Availability and time commitment from IT stakeholders and key SMEs/resources for information sharing, workshops, interviews, surveys, validation of findings, and related activities as per schedule
    • Ask right set of questions very specific to enterprise pain areas leading Generative AI exercise
  • Identify Dynamic data
    • Check for existing data and use it appropriately
    • Prepare dynamic data. Dynamic data includes tables, images, videos, text, code etc
  • Prompt identification
    • Identification of Prompts & LLMs
    • Adjust the prompts AI uses in the initial stages
    • Fine-tune the prompts to address inaccurate and biased outputs
  • Build Target Architecture
    • Create target reference architectures
    • Create a Generative AI adoption Roadmap

Summary

Understand Generative AI fundamentals to identify business use cases and develop a strategy for data and AI across the enterprise. Identify the highest value of use cases requiring LLMs.

Train the people to promote Generative AI-driven initiatives. Consider reskilling and upskilling employees to work with Generative AI effectively. Address and stay informed about emerging ethical guidelines and regulations related to AI.

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