The global governance, risk, and regulatory (GRC) landscape is rapidly evolving. Businesses are under scrutiny from regulators now insisting on GRC disclosures and institutional investors who have started incorporating ESG among investment criteria. With this, Boards and leadership teams have started acknowledging the need for better governance, risk, and compliance (GRC) strategies to not just counter risk, but ensure business continuity.  

The past two years' events have also highlighted the need for better rapid response capability to risk. As per OCEG, a global, non-profit think tank on GRC, 70% of organisations in a recent survey reported new GRC challenges from having employees working remotely. Also, 60% of organisations reported that increased data privacy and cybersecurity regulations drove significant changes to their approach to GRC. Business leaders have got the idea that to thrive in an accelerating digital economy, they need to change their approach to GRC to become more resilient, risk-aware, and better-governed enterprises. 

Businesses in India have mostly followed a traditional approach to managing governance, risk, and compliance. That is, by addressing them as separate silos. For example, each department may have its risk reporting structures with little contact with other departments within the organisation. However, as risks become more intertwined, the processes used to manage them often contradict each other. This leads to duplication of work, an increase in costs, and an overall increase in business risk. An Artificial Intelligence (AI)-enabled integrated approach to business GRC is the key to bringing everything together.  

How AI is transforming the GRC landscape 

With the GRC landscape rapidly evolving, the amount of data being collected by organisations is humongous. To convert this data into actionable intelligence, technical teams must first sieve through vast complex sets of structured and unstructured data, separating useful information from 'white noise' before it can be analysed further. While organisations may use automation to process risk assessments, the outcome would still need to be judged manually by risk professionals. 

This is not viable in the long run because the data will keep growing as the world becomes more connected, leading to more errors. Business decisions today span continents, jurisdictions, and customer preferences, yet must be taken all in a matter of minutes and even seconds. Therefore, business GRC models must be as agile. The technology and processes must evolve to keep up with expanding data sets without getting overwhelmed with the volume. 

As the fourth industrial revolution unfolds and AI is developing at an incredible pace, the fifth is already on the horizon. The advantages of machine learning and AI over manual processes to take in information, analyse it and then make calculated decisions are of obvious benefit to the enterprise. Even now, businesses are already looking at implementing AI systems to speed up investment decisions. Having systems that analyse and suggest GRC-related changes is a natural extension of the technology to other areas rather than a monumental leap forward. 

AI-enabled systems will be able to collect data from various data streams and channels automatically. These include regulatory and trade bodies' feeds, social media, news sites, and customer and competitor websites. It will analyse and compare this data with the organisation's existing data sets and operations. The system will also suggest any process or strategic changes. 

As the technology evolves and machine learning and predictive algorithms improve, the potential margin for error will diminish. Businesses will be able to manage the entire GRC function with minimal resources. When powered by AI, integrated GRC software will provide an overarching framework for companies to work within. From compliance to IT security, legal functions, insights, and audits, AI will create a powerful mechanism for companies to protect themselves best. These integrated programs will foster the collaboration of sharp insights, and intelligence gained both from machine learning and human observations. When utilising AI correctly, leadership teams can see the bigger picture, connecting the dots through large data sets that were previously considered near impossible to manage.  

Leveraging next-generation technologies like AI and machine learning helps to cut down on manual processes without compromising on all the data a business would need. For example, instead of waiting for a human analyst to interpret and evaluate the relationships and trends, AI-enabled GRC solutions could utilise cognitive computing to continuously analyse data points for any changes that could lead to greater risk or control failures. 

Only in the event of any threat to the business objective, the system would automatically alert human analysts for investigation. This way, the leadership team can focus more on the strategic tasks while leaving the AI to do all the heavy lifting on the workflow processes around GRC. 

Expecting the unexpected 

The GRC landscape is also impacted by unexpected events - like the fluctuating exchange rates between the Dollar and the Indian Rupee. Or the new cyber threats that emerge now and then. Businesses are finding it more challenging to plan and have no choice but to allow for wider risk margins. One can only expect that there will be far more unpredictable events in the next few years. 

As businesses face more pressure to deliver against a backdrop of evolving risks, GRC technology based on AI will evolve to provide a holistic view across the entire enterprise. The technology will enable businesses to manage uncertainty apart from standard events. Organisations will become better equipped to manage unexpected risks and will not be left vulnerable to ongoing market changes or industry trends. Organisations will be able to rely on AI to provide a single process and point of reference for GRC, ensuring that they are better prepared to face the unexpected.