By Sunil Gopinath, CEO, Rakuten India
AI can often present a perplexing business proposition. While many organizations boast mature techniques
and data, the cost and complexity associated with AI deployment poses significant hurdles. Consider an airline
contemplating facial recognition and biometric scanning to streamline boarding processes. While these
technologies enhance operational efficiency, concerns regarding glitches and data privacy could potentially
outweigh the benefits. Conversely, certain companies grapple with unpredictable scenarios with novel use-
cases, untested AI tools or under developed end-markets.
At Rakuten, when we first started our AI journey, the formulation of an AI roadmap proved to be a pivotal
milestone. It helped us strategically identify sector-wide opportunities, IT and OT architectures and mission-
critical resources for transformation. But what truly empowered us was extending beyond the confines of
immediate goals. For AI implementation to succeed, companies can benefit from holistically identifying
specific value propositions in past-fix scenarios and future use-cases alike to align business evolution with
stakeholder needs.
The core of this metamorphosis brings a crucial decision for executives: delineating their AI identity through
one of three strategic archetypes: the ‘taker’, ‘the shaper’ and ‘the maker’. The ‘taker’ embodies a prudent
approach, leveraging existing AI tools for immediate gains. While this facilitates swift entry into the AI arena, it risks fostering reliance on external providers and constrains customization to address specific requisites.
Conversely, the ‘shaper’ reflect a proactive stance, customizing existing AI tools to align with distinct
requirements through collaboration and adaptation. This archetype is particularly advantageous for
burgeoning enterprises intent on fostering innovation and bolstering competitiveness. Lastly, ‘the maker’
epitomizes a pioneering ethos, allocating substantial resources toward developing proprietary AI solutions.
Although the archetype offers unparalleled avenues for differentiation and long-term strategic supremacy, it
demands an appetite for experimentation. In an increasingly volatile business landscape, success hinges not on rigid adherence to a singular archetype but on adeptly balancing choices in context.
For enterprises, the bedrock of effective AI integration lies in data quality, governance, and ethical
development. Targeted investments in data infrastructure and architecture are imperative, particularly in
harnessing insights from unstructured data reservoirs. Additionally, as the demand for AI applications
burgeons, so does the requisite computational resources.
Understanding the nuances of compute demand across various phases of AI development—from training to inference—is key for optimizing resource allocation and enhancing efficiency. One of the most pragmatic and impactful avenues for automating enterprise operations is through the integration of AI and ML in IT operations (ITops). Cognitive AI-powered self-healing systems possess the remarkable capability to autonomously detect, diagnose, and resolve issues before they impede operations.
By leveraging machine learning algorithms, these systems analyze vast datasets in real-time, identify root causes of anomalies, and adapt to rectify errors. Cognitive AI not only minimizes downtime and disruption but also optimizes performance cost-effectively. While AI is often associated with productivity and efficiency gains in business operations, its potential is far more than one-dimensional. The most significant advancements are happening in B2C and customer-facing use cases. For instance, in e-commerce, customers typically search for specific items like a white jacket.
But with AI capable of processing unique information across catalogues, user behaviours and external sources- contextual and dynamic search capabilities are made possible. Customers can now ask for recommendations such as “make me look cool for a New Year’s Eve party in New York” or “dress me for the Oscars.” This approach extends to product bundles and upsells that adapt to individual preferences, resulting in hyper-personalized and intelligent recommendations that enhance customer satisfaction and engagement. AI-powered chatbots and virtual assistants are also changing the way customers interact and derive value from brands.
These sophisticated AI systems handle a wide range of inquiries, deliver immediate support, and
continuously refine their responses through machine learning. With the ability to discern and react to
customer emotions, AI efficiently mimics empathy to customize and expedite query management. Chatbots
also enable round-the-clock customer service with improved responsiveness, significantly boosting customer
trust.
As we look ahead, AI is undoubtedly empowering businesses to meet customers exactly where they are,
offering a level of personalization previously considered unthinkable. The efficacy of AI in driving
organizational growth will largely depend on strategic foresight and a balanced approach that considers both
short-term gains and long-term investments in innovation. By leveraging AI systems that are reliable and aligned with both customer needs and business outcomes, companies can forge more meaningful stakeholder relationships, which are essential for business growth and ,ultimately, viability.
