Data Cloud Investments bearing fruits of AI

Artificial Intelligence (AI) is increasingly playing an integral role in determining our day-to-day experiences. The applications of AI are inherently expanding beyond search and recommendation systems to encompass high-stakes domains such as Digital e modernization, efficient operations, hiring, lending & mortgage management, retail, healthcare, and education. The potential impact of AI on individuals, businesses, and society is significant, and it is essential that AI models are designed to be accurate, reliable, and ethical.
 
A tree does not bear fruits overnight, but needs strategic planning and knowledgeable modern farming to bear fruits on time. Even if it were to be grown expediently, it needs careful care from seed selection to fruit delivery and proper knowledge of purpose, climate, location, conditions, breed, and time to bear the fruit that is seasoned optimally. These elements are to be a thought-through decision with clarity rather than an afterthought as far as potential impact.
 
Keeping this farming analogy in mind, to produce reliable, accurate and ethical AI models, organizations first need to define their Data cloud strategy to embark on the journey of AI. The success and accuracy would depend on the foundation that is available to be used by the AI. Using the same analogy, there are 7 top foundations that organizations should focus on to bear the fruits of AI.
 
This 7 steps guide helps assess the measurement of the outcome of the AI journey accelerated by the Data Cloud:
 
1.       Identify the potential and Value that's on the offer

Where does your organization currently stand in terms of the AI journey? Is FOMO the key driving factor or have we identified the right business problem to address with potential AI use cases?  The first step is identifying and deciding what business problems AI can solve. To achieve this, familiarize yourself with various Cloud Data, Analytics, ML- Classification and AI concepts and illustrative use cases of Generative AI, Adaptive AI, Responsible AI, and ML- Classification use case approaches. In parallel, identify the best possible functional areas to apply the above solutions based on the organization's data and technology maturity. For example, potential opportunities in Modernization of the Application Platforms through AI, Modernize Business functions like HR, Sales & Marketing, or Improve operational efficiency like Contact Center Modernization etc.
 
2.       Leadership buy-in for memorization with tangible value
 

The second important foundational dimension is to get commitment from the leadership for the purpose of Cloud Data-led AI. This needs significant involvement, commitment, and investment to create the right focus automation and optimization-related use cases. The best approach is to start small and fail first based on the organization's Data & Cloud Platform maturity, data availability and current position on its AI journey. The roadmap for the above commitment is critical for visibility to sponsors to outline tangible and measurable success criteria. This business case should provide implication and course correction indicators if it requires bringing in confidence.
 
3.       Identify the use cases and prioritize

The third foundational dimension is the imperative to identify the use cases that are most impactful and valuable for the organization's vision and leadership's success. Some of the examples of use cases that are foundational with the Digital enterprise value chain are Application and Technology landscape, Sales and Marketing, HR – Hiring and Operational Efficiency.
 
4.       Identify the Existing Platform, Technology Foundations, and capabilities

The fourth foundational dimension is determining the feasibility of identified use cases and leveraging existing technology platforms and solutions; as well as assessing the maturity of the landscape with respect to the capability and capacity that can support Build vs. Buy for AI. A robust Data Cloud foundation is a MUST as the anatomy of work, and requirements for augmenting capabilities, change daily. Another aspect to consider is the agility to partner with other technology providers to achieve quick wins.
 
5.       Data Foundations to fuel the AI journey -
 

The fifth and most critical element is Data, which acts as the seed for the entire crop that would bear fruits. The key foundations to focus on are:

a)       Cloud Data management capabilities:  Cloud Data Engineering Capabilities for Data integration and management for various forms of data like structured, unstructured, and semi-structured data.
b)      Data Identification, Understanding and Quality: Trustworthiness of the Data with respect to definition, cataloguing and traceability of all of them or the identified potential use cases.
c)       Data Anatomization: Availability of sensitive data management across the organization and access to them.
d)      Experimentation Data: Availability of the experimentation/synthetic data that mimic real-life scenarios.
e)      Enterprise AI Operating model that seamlessly integrates the Enterprise scheme of things on Data & AI
 
6.       Talent & Competency & Culture & Partnerships 

To implement a pilot or scale to be an achiever, identifying and earmarking the right talent pool for the selected technology stacks is the primer for the outcome. There needs to be a plan for upskilling and cross-skilling the existing pool of talents with complementary skillsets. Along with this, an AI innovation culture with dedicated AI competency could also accelerate the cause of AI. It will benefit the organization to assign the implementation ownership to the Chief Data Officer (CDO) or de-facto owner, as the CDO has enterprise Data visibility.
 
7.       Track and Course Correction

Only strategy will not provide outcomes; it must be continually monitored, augmented and maintained across both controllable and uncontrollable scenarios. Hence, it's critical to prepare and publish the Data Led AI Strategy roadmap, critical focus areas and checkpoints .Why do we think this is the minimalist Data and AI strategy, which would bear the fruit of tangible value? Recently, one of the largest and most reputed conglomerates across the globe re-strategized the connected vehicle platform to be the foundational platform servicing AI to technology, customers and production lines locally and eventually globally. This conglomerate wanted to move rapidly along its AI journey within a span of 6 months. Its top -3 challenges were to capture the unconventional information, fuel and facilitate the connectivity among the existing unknown to known data with speed for AI experimentation, without disrupting the 1500 approx. security, regulations, and compliance controls.
 
Leveraging this 7-step approach, they were able to structurally identify the challenges by forming the Business case for centralization of the Data & AI Technologies platform, while focusing on the existing data as well as the future data roadmap, and prioritizing the use cases to achieve the short-term goals of assistive assembly platform in line with the long term roadmap of the global launch.
 
To achieve AI outcomes, it’s important for organizations to adopt a structured approach through defined strategies, rather than adopting an incremental approach or treating it like an afterthought. This 7-step guide for the Cloud Data Led AI journey holistically addresses overall dimensions, from conceptualization to value realization, throughout the AI journey.

Amaresh Mahapatra

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Amaresh Mahapatra is Senior Architect at Happiest Minds Analytics CoE.