Data and AI witnessed a major boom in the last few years. Every business function that has started initiatives around AI has been able to appreciate the value and its impact. Understanding data at speed to make faster decisions has become a differentiator between winners and others. One major shift in AI-led initiatives is that focus is now on business value rather than AI techniques which speak about the maturity and confidence AI has gained over the last few years.
In the last 7 years, we have seen drastic cloud adoption rates, where on average % of data over the cloud increased from 30% to 60%.
AI also has seen tremendous growth where the AI software market grew from 10B USD in 2018 to ~35B USD in 2021.
A few years back we saw AI coming to fold with Narrow AI use cases around PoC/MVP aiding operational efficiencies. This momentum picked no of initiatives increasing along with the adoption of cloud. In the last 2-3 years, the industry started focusing on how AI can help with top of line growth.
Today, AI models are made to be sustainable, trackable, and continuously trainable where both implementation and operationalization are given equal importance by adopting CI/CD, MLOps, and ModelOps. COVID has only accelerated the focus and investments around AI. It is an established fact that most companies do well while adopting AI during the initial stages of Pilots, and very few can adopt it to scale.
However, it’s also proven that the RoI for POC is very limited and much higher when adopted at scale.
Business agility without scaling the AI
Today AI is required for both businesses’ sustainability and new growth. Business leaders are taking lead with scaling AI and data platform. With data platforms in place and ability to forecast events with help of AI they can take lead over others.
Gartner says, “By 2025, the 10% of enterprises that establish AI engineering best practices will generate at least three times more value from their AI efforts than the 90% of enterprises that do not.”
In today’s connected and agile world, failure in one function can have a domino impact on another and the whole business line, e.g., marketing can impact supply chains, finance, operations, etc. Multiple events like competitor new product launch, social media complaints, change in macro/micro-trends can impact all functions like marketing, supply-chain, finance, etc. Organizations that do not assess the data coming from various internal and external sources; do not predict predictable events, can have an impact on their sustainable growth. It is paramount:
- To assess (what is happening out there) with AI
- To adapt (taking intervention in various business functions) to sustain and grow top of line and optimize bottom line.
Organization must scale Data and AI across functions beyond Pilots
Enterprises can follow the principles of design thinking to understand pain points and re-envisage new models with AI for internal and external customers. Enterprises can find gaps that AI can fulfill, run pilots, create MVP, and scale AI within the organizations. The leaders should ensure that initiatives don’t limit to one function but are scalable across functions.
For example, demand forecasting should not be limited to the supply chain but has an impact on finance, marketing, and operations. For better ROI, AI strategy must scale across functions, and AI must penetrate deeper to the foundation of the business stack rather than be standalone AI applications. Getting real value from AI is not possible if data & AI is not at foundation, and AI applications are limited to POC or standalone.
On the Technical front, the organization must develop the ability to manage large-scale data with technologies, and host scalable AI applications with orchestration tools. AI models should be manageable, monitor-able, re-trainable for the data drifts using MLOps and ModelOps tools. To develop trust organizations can adhere to best AI practice where Data and AI governance can instill trust. We created an AI Foundation framework that has been extensively used in our experiences, such as in cognitive customer analytics, Next Gen Enterprise and Asset Analytics. This framework has benefited many of our customers for adopting AI at Scale.
The framework can be utilized across various use cases covering scaled model inference, training with multiple GPUs, distributed training and inference, enabling retraining mechanism, data drift, etc.
How AI at scale can unlock value
AI can transform various touch points, and functions. Whether it is looking internally or externally, at bottom-line, or topline growth. Stronger AI foundation can help in understanding customers, competitors, company-internals, collaborators, and business climate. It can enable businesses to be agile to adopt the change. AI is already mainstream for leaders; and many more organizations are adopting it. Leaders are taking actions to build business on strong foundation of AI, AI is not going to be limited to pilots but would be scaled across functions to drive more business value.
Leaders need to continuously assess the business models and move to AI-first approach to stay ahead.
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