Shifting from plus-AI to AI-plus: Transforming business with holistic AI integration

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There is no doubt that the democratisation of artificial intelligence is well underway, radically transforming the nature of work and how businesses operate. Its adoption is also increasing per our Global AI Adoption Index poll of 2022 that found that 57 percent of Indian companies are using AI in their business, while an additional 27 percent are exploring the technology. This democratisation is owed to advancements in solving the critical challenges of how to scale and operationalise AI using the power of foundation models. Given AI’s propensity to positively impact businesses, there is immense pressure on business leaders to act now or risk falling behind competitors.

In today’s AI for business era, business leaders must move away from just adding AI to already existing processes (providing short-term incremental benefit) to a holistic approach where processes are designed with AI at the core—i.e., move away from a plus-AI model and adopt an AI-plus approach.

The journey from +AI to AI+

The key to success in this new AI era lies in integrating technology into every essential activity and capability that is fundamental to the organisation and the customers they serve. Whether based on machine learning (ML) or foundation models, the more customised AI models are to those priorities, the better they will be able to serve customers and deliver real business value. Furthermore, AI is only as good as the data that fuels it, and it is critical to identify the right data sets from the beginning. Finally, all this must be done underpinned by the fact that AI must be explainable and protect consumers’ privacy and data rights to engender trust.

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As business leaders think about scaling and accelerating the impact of AI, they need a completely reimagined AI and data platform. Broadly speaking, this platform needs to be designed to tackle four critical elements of a sound AI strategy:

1) Empower developers with the next-generation studio for enterprise AI

Businesses need to equip their AI developers with tools to train, validate, tune, and deploy both ML and new generative AI capabilities powered by foundation models. These models should be designed to work on multicloud architectures and with a rigorous focus on data acquisition, provenance, and quality to serve enterprise needs.

2) Create an open, hybrid, and governed data store

To fuel advanced AI models, organisations must harness the explosion of data being generated throughout their enterprise. They need a fit-for-purpose data store optimised for AI workloads, supported by querying, governance, and open data formats to access and share data.

3) Use a powerful toolkit for AI governance

There is no point in all this advancement unless people trust the results and insights generated by AI. Hence, it is important to create responsible, transparent, and explainable AI workflows by providing AI and data governance capabilities throughout the AI lifecycle.

4) Infrastructure fit for AI

Infrastructure is an essential consideration of any AI strategy. A hybrid cloud architecture provides an agile and secure foundation for extending AI deep into every business. Using a hybrid cloud platform that is optimised for AI workloads, makes it possible to take AI models to where the data resides rather than the other way around.

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Many AI use cases that impress us today will soon be overtaken by far more disruptive advances yet to come. The solutions we are only beginning to imagine will certainly become commonplace, and new tools and processes powered by AI will lead to entirely new types of work. However, to fully realise its potential and to be prepared for that future, AI must be built on a foundation of trust encompassing three principles—being true to the purpose of using AI to augment human intelligence, ensuring that the data and insights always belong to their creator, and the output from any technology must always be transparent and explainable.

The writer is the managing director, of IBM India and South Asia.

The thoughts and opinions shared here are of the author.