Data & AI Operating Model —
Powering Transformation
Are you struggling to scale AI use cases?
Building a strong Operating Model for scale
In the dynamic digital era, organizations often grapple with the application and scaling of Artificial Intelligence (AI). Despite successful pilot AI initiatives, scaling these across the business landscape poses a significant challenge. Complications arise due to inconsistent AI governance, unclear role definitions, and lack of cross-functional collaboration. Furthermore, misalignment between AI initiatives and broader business strategies can lead to disjointed efforts, hindering growth potential. Unaddressed ethical considerations and regulatory compliance in the AI domain also expose organizations to potential risks. Navigating these complexities calls for a comprehensive and expertly planned approach.
At HYGHT, we leverage our team's hands-on experience, including former Chief Data Officers and Heads of Data, to navigate the complexities of AI scaling. Our pragmatic approach maps high-value, quick-to-implement use cases with a long-term capability and use case roadmap. This dual focus delivers immediate results and sets the stage for future AI growth. By aligning AI initiatives with broader business strategies, we ensure cohesive progress, guiding organizations through their AI journey to drive meaningful, sustainable change.
"76% organizations acknowledge they struggle when it comes to scaling it across the business. What’s more, three out of four C-suite executives believe that if they don’t scale AI in the next five years, they risk going out of business entirely."
Accenture
"AI Built to Scale"
"AI Built to Scale"
Our 5-step methodology ensures the link
between vision, as-is and to-be capabilities
Setting the Course for Data & AI
01
Vision Design
- We begin by collaboratively defining an data & AI vision that aligns with the organization’s broader business goals and strategic objectives
- This vision serves as the guiding light for all data & AI initiatives, providing a clear direction and shared understanding across the organization
- The defined vision sets the stage for subsequent steps, ensuring a structured approach to the data & AI transformation journey
02
Use Case Definition & Prioritization
- We identify potential AI use cases that align with the defined vision and offer tangible value for the organization
- These use cases are then prioritised based on their potential impact, feasibility, and alignment with the overarching business strategy
- The prioritisation process ensures resources and efforts are focused on the most valuable and achievable use cases in the short and long term
03
To-be capability definition
- Based on the defined vision and identified use cases, we outline the desired future state of AI capabilities within the organization
- This definition includes technological needs, skills, processes, governance, and culture changes required to effectively implement AI
- The to-be capability definition guides the transformation efforts, ensuring a comprehensive understanding of what is required for successful AI deployment
Bridging the Gap and Charting the Path
01
Fit-gap analysis
- We conduct a fit-gap analysis to identify the gaps between the current data & AI capabilities and the defined future state
- This analysis helps pinpoint the areas that need significant enhancement or transformation for successful AI deployment
- The fit-gap analysis informs the prioritization of capability enhancements and provides insights into the roadmap design
02
Roadmap design
- Utilizing the insights from previous steps, we design a detailed roadmap outlining the journey from the current state to the desired future state of data & AI capabilities
- The roadmap includes specific milestones, timelines, and responsibilities, providing a clear implementation plan for the AI transformation
- The roadmap acts as a strategic guide, ensuring a structured, phased, and measurable approach to scaling AI initiatives within the organization
An AI Operating Model provides
a structured framework for scaling AI
01
What many companies do
- Initiate AI projects on an ad-hoc basis without a clear, overarching strategy or operating model.
- Struggle with scaling AI initiatives from pilot stage to full implementation across the business.
- Face challenges in aligning AI initiatives with broader business strategies, leading to disjointed efforts.
- Overlook the importance of cross-functional collaboration, clear governance, and defined roles in successful AI deployment.
- Neglect the ethical considerations and regulatory compliance related to AI, increasing potential risks.
02
What we do differently
- Leverage our team’s hands-on experience to provide a structured framework for scaling AI initiatives.
- Focus on high-value, quick-to-implement use cases while simultaneously building a roadmap for long-term AI capabilities.
- Ensure that AI initiatives align with broader business strategies, fostering cohesive and strategic progress.
- Emphasize the importance of cross-functional collaboration, clear governance, and defined roles in AI deployment.
- Help navigate the ethical considerations and regulatory compliance related to AI, mitigating potential risks.
