McKinsey in its report categorically stated that generative AI could add the equivalent of US $ 2.6 trillion to US $ 4.4 trillion annually across the 63 use cases they analysed. This impact is massive and organisations cannot overlook Gen AI if they want to stay relevant.

In the past, analytics and technology have majorly played the roles of enabler, accelerator, optimiser and it is only now that they’re hitting at the core business processes. Organisations which have integrated AI into their workflows are at a distinctive advantage of realising the value from Gen AI at a faster rate.

Gen AI is adding value in creative fields, traditionally outside the purview of technology. Business leaders across the industry are welcoming of this. However, most business leaders struggle to arrive at the quantum of investment that needs to go into Gen AI and the justification of this investment in terms of ROI.

Fashion industry is still fresh with the bad after-taste left from the investments into Web3.0-metaverse and NFTs. AR-VR continues to be another area with active investments but little returns.

In this context, leaders often wonder how much of this is beyond the technology enthusiasm and an ambitious sales pitch. How much of this can really impact business? There is no easy answer to this question. Before we address this question, let us look at how Gen AI effects different organisational departments.

Gen AI’s transformative impact across functions

Gen AI is democratising creativity. It’s widening the gap between high-end creativity vs. the regular creatives. Organisational functions like Design and Marketing are using Gen AI to augment their creativity, whilst functions like supply chain, finance, legal, HR and sales are looking at automating routine stuff.

Gen AI, especially stable diffusion model-based solutions, are like a shot in the arm of Designers, opening new inspirations for them. They also greatly help reduce the design time by tremendously fastening up the process steps. In one such implementation, we saw a 70 per cent reduction in the design time. A Designer now is expected to have the skills to leverage AI, by the way of right prompting.

Marketing is another area where Gen AI shows tremendous promise. From taking over content creation to generation of visual content for marketing and even personalised messages for campaigns, this is one area where Gen AI-based enterprise solution can free up time for the Marketing executives to let them focus on the decision-making process. A marketing executive now is expected to tap into AI to develop, for example, a much finer customised creative for the customer.

In e-commerce, category description enrichment is an area fashion retailers struggle with, keeping in view the growing encyclopaedia of style elements and the arduous job of tagging the product with the right attributes.  Gen AI solutions combined with computer vision techniques are showing tremendous promise in this area. An initiative we ran helped save Rs.1 crore in terms of category enrichment cost and saw a double-digit increase in the SEO metrics. E-com heads are racing towards using AI to improve their website metrics.

Virtual Photoshoots is another area which is heavily dependent on human models and software involved in draping. This is time consuming, expensive and less flexible. You cannot afford multiple models for the same product. Businesses often end up settling for the best model fit.  Gen AI, in this case, gives flexibility to choose the model body type, location of the shoot and even the mood of the shoot.

Proof of Scale

Gen AI solutions are currently in the early stages of maturity when compared to enterprise products that can be a seamless fit. This is expected to continue for a few more years till the development in AI attains a more stable growth. This does not imply that businesses can wait out on this. This is a technology which benefits from long-term investments and internal capability building.

Organisations need to move beyond ‘Proof of Value’ to ‘Proof of Scale’. It is no more enough that solutions can deliver value; they need to be able to deliver value at scale. This often means that they need to be integrated into the existing technology landscape, governance mechanisms and provide relevance to the value drivers of the organisation.

We have moved beyond the Proof of Value experiments and have established how AI can transform fashion. Now is the time to figure out the best way to scale these solutions. This will be an individual journey and businesses should invest in their experimentation muscle.

Historically most businesses aren’t used to the level of experimentation that a Gen AI solution demands. It’s important now more than ever that these experiments be run by a group of P & L owners and such AI experiments are conducted with a mandate to train and scale AI solutions for their business.

The way forward

Most executives wonder what it takes to get started on the Gen AI journey and how do you define the roadmap. At Aditya Birla Group, we have worked with multiple businesses which are at different stages of AI maturity.

First, assess if Gen AI can add value to your business. Then, integrate AI and Gen AI into your long-term strategy. Establishing a Centre of Excellence (CoE) with a clear budget is key to successful implementation, along with a solid plan for infrastructure, talent and experimentation, driven by close collaboration between business and technology leaders.

The future belongs to those who can scale up these solutions in their enterprises. As for solution providers, the accuracy and the ability to productise an AI solution would be the defining point for any business.

At the Group Data and Analytics Cell of Aditya Birla group, we work with internal business on various levels of analytics maturity with an established Gen AI Centre of Excellence. We look at the holistic development and deployment of Gen AI solutions which includes a focus on not just model development but also a focus on ways of working and ways of thinking. We bring in a long-term view for talent, culture, funding, partnerships and organisational structure to drive project adoption amongst the end users.