• Sachin Arora, Partner |
7 min read

Key takeaways


  • AI is not just a technology to manage data but a sharp tool to achieve business goals, such as sales growth, operational efficiency and accentuated customer experiences
  • Data shows AI use cases perform better by a large margin in comparison to where it’s not adopted
  • AI use cases are becoming a reality across the business spectrum of growth, and cost and functional optimisation
  • Business leaders are increasingly looking at AI to augment their key business decisions, helping them through critical and high impact moments of truth.

By Sachin Arora, Partner and Head – KPMG Lighthouse the Global Center of Excellence for Data, AI & Emerging Technologies, KPMG in India; Anuj Kumar, Director - Digital Lighthouse, KPMG in India and Vidyashankar Kumar, Technical Director – Digital Lighthouse, KPMG in India

Introduction

Artificial Intelligence (AI) has arrived. It is no longer the subject of heated discussion among technology SMEs but an integral part of an organisation’s business strategy and hence, a board room topic. For business leaders, the question today is no longer whether AI will fit into their business, but rather how can they realign their organisation to fully capitalise on AI’s transformational value and, in doing so, become an AI-first enterprise.

Let’s take a step back and put the use of AI into context. Today, the capabilities of artificial intelligence are widely being classified under three broad segments:

  • Narrow AI – performs a dedicated task with intelligence
  • General AI – learns, perceives, understands and functions completely like a human being
  • Super AI – surpasses human intelligence and performs any task better by application of cognitive properties
     

powered-systems

Narrow AI represents all kinds of existing AI-powered systems, including the most complicated and capable versions created till date, and that is the main kind of AI, which is driving innovation in businesses. Narrow AI is key to automating, accelerating and enhancing key business areas to scale transformation and drive value through capabilities, such as pattern recognition, deep learning, voice/image recognition, machine learning (ML) and natural language processing (NLP).

The impact of AI-backed data for enterprises

The four dimensions of business opportunities generated through AI-backed data are explored in this article.

Revenue growth powered by AI
AI augmentation creates a significant uptick in revenue growth by improving the efficacy of the marketing and sales functions, thus increasing customer loyalty and advocacy. A well-rounded marketing strategy with hyper-personalised targeting is a precursor to any organisation’s strong sales pipeline. AI insights help marketing managers generate personalised content based on triaging information from multi-variate sources, including CRMs, ad platforms, clickstreams and other data collection platforms. This further helps in automating sales pursuits.

AI-driven sales reduce the cost of new customer acquisition by refining the sales funnel whilst significantly driving up the quality of leads by sifting through consumer behavioural patterns.

With AI’s ability to surface hyper-personalisation, organisations can drive higher customer loyalty, repurchase and advocacy.

Reimagining your customer journey powered through AI
Customer experience and journey strategy is at the core of every business decision of any successful organisation. With hyper global connectivity and a tough competitor landscape, the cost of switching for a customer is increasingly lower. To tackle this highly fickle landscape, it is critical for a customer experience leader to adopt AI-powered customer journeys.

Besides, the world has moved well beyond linearly programmed BOTs and rather have AI-powered personal valet guiding, anticipating and helping customers through their transactional journeys and points of interaction.

When these AI insights are combined with an emotional quotient, it amplifies the power of every moment of interaction with intelligently intuitive recommended next steps. This changes an interaction with a machine into an empathetic and human experience, while the organisation still maintains relatively lower cost.

Evaluating bottom-line growth and revenue productivity per employee through operational efficiencies
While customer experience powered by AI becomes more intuitive, it can only come to fruition if the same capability is extended to an organisation’s operational processes and how well these processes are adapted to customer journeys.

Running a lean and efficient business operation was set in motion from the days of production line manufacturing and became an integral part of innovation, especially in the automotive industry. Today, AI-backed data has opened the window to Industry 4.0 where operational efficiency means augmenting current operations with the power of AI. For example, Industrial manufacturers in India are using machine learning models to improve throughput, optimize resource utilization, reduce cost, and improve asset productivity across various stages of production to drive efficiency as well as EBITDA improvement.

AI-powered accelerators like process mining, digital twins and smart automation are augmenting human efforts in continuously adapting and improving the operational processes to ensure the cost of delivering service remains optimised.

Using AI-powered Big Data frameworks like process mining help organisations auto discover and visualise every scenario of their operational processes bringing 100 per cent transparency. ML augmentation predicts outcomes like delayed orders, late payments, supply chain failures, etc. These help set intelligent signals and triggers to intuitively error-proof their processes, thereby ensuring high accuracy at optimised cost.

AI-powered, data-backed smart automation has consistently helped in drastically reducing cost per transaction by reducing failures and process waste while increasing end-to-end single-touch or no-touch transactions.

AI-driven controls, risks and compliance
The cost of controls or compliance failure is often the most overlooked by organisations. In today’s complex and interconnected world, risk is accepted as an inherent part of business and risk aversion is not an option any longer.

Any comprehensive risk management strategy today has AI at its heart. Accordingly, a Chief Compliance Officer’s role has become increasingly complex, and hence, AI-backed data is seeing greater adoption via the following methods:

  • Strategic data acquisition and processing: To remediate the overlap between existing and new rules, regulations and policies, compliance teams are triaging information from multiple sources like litigations, parliamentary bills and unstructured data formats. AI is being used to process this information at a very high speed and accuracy.
  • Data unification: The processed data is then semantically related to, classified and clustered into regulations, policies, processes and controls. They are also auto-labelled and key phrases attached with common ontology, further integrating the data with the overall organisation’s data strategy.
  • Automation of manual labor: Manual efforts are replaced by smart bots consuming AI-backed data, thus reducing the cost of compliance.
  • Guardrail management and first line of defense: AI-powered data frameworks, such as process mining are quickly being adopted as the guardrail management and first line of defense for regulatory audit, thus reducing the dependency on human hunches, improving sampling efficiencies and transparency.

Looking ahead

AI is expected to have an equally transformative impact on the emerging future as well. As metaverse takes over the real world and businesses jump in to captivate their consumers’ imagination and be omni-present for their customers, AI can play an important role in winning in this new realm. In today’s physical world, we are already producing and collecting huge amounts of data through IoT devices in machines, factories, stores, or through sensors in our cars, watches, phones, etc.

As we transit to the virtual world, this is expected to become even more critical as each pixel is equivalent to an IoT device or sensor, producing huge amounts of data in terms of personal preferences, ownership and brand alignment, thus paving the way for hyper personalisation.

Conclusion

KPMG in India’s experience of working with organisations that are looking at developing and implementing their own AI strategies shows that the following steps should be considered to achieve the most value from your AI investments.

  • Strong data foundation: Invest in comprehensive data and robust data infrastructure
  • Onboarding right talent: Hire AI and data science talent, either internally while improving AI literacy or through partnerships outside the organisation
  • All invested in AI success: Creating an organisation-wide culture invested in AI's success. Start by educating and upskilling employees
  • Aligned to business value: Devise AI strategy, driven by business needs, ensuring use across the entire organisation
  • Ethics and governance: Develop an AI ethics policy with clear guidelines on how AI will be deployed. Commit to ethical and unbiased use of AI.

Transforming into an AI-first enterprise requires organisations to think strategically. It means thinking bigger and embedding data-driven technology throughout the enterprise. Organisations need to be agile enough to adapt to the cultural changes that AI brings, as well as create an ethical framework to manage its responsible implementation.