By Manish Godha, Founder and CEO, Advaiya
AI’s transformative potential continues to grow, influencing industries, economies, and societies. However, the technological revolution brings with it a profound responsibility. For an organisation, balancing innovation with continuity is the cornerstone of a sustainable AI-driven future.
AI has consistently demonstrated its ability to redefine possibilities. From optimising healthcare diagnostics and enhancing agricultural efficiency to powering personalised consumer experiences, the potential applications of AI are boundless. These innovations are reshaping industries, making them smarter, faster, and more responsive to evolving demands.
For instance, in sectors like logistics and supply chain management, AI has enabled predictive analytics, reduced waste and improving efficiency. Similarly, autonomous vehicles, supported by machine learning algorithms, are poised to revolutionise transportation, enhancing safety and reducing environmental impact.
Rapidly progressing technologies and competitive adoption are going to disrupt many business models that have been a source of continued viability so far. Businesses must recognise that they would need business model transformation, but at the same time, they cannot damage themselves while doing so. In many ways, AI adoption can be akin to playing with fire. Taming AI in the specific context of a business, its market, and its constituents is a tough but key challenge.
Having a vision while being responsive
The pursuit of AI advancement can sometimes be marked by haste, with companies rushing to outpace competitors. Such impetuous innovation often overlooks critical considerations, such as system reliability, long-term impact, and ethical implications. This “race to innovate” mentality risks making costly investments that are powerful but poorly understood, increasing the likelihood of mishaps and, more commonly, poor, if not non-existent, return on investments.
Consider a wholesale, organisation-wide adoption of AI agents for, say, customer service or quality inspections. Not just costly, it can cause major disruptions to the business’s fabric—risking loss of market share and reputation if these systems do not adequately consider and incorporate the product, market, or people nuances that have come to be implicit standards within that business.
This highlights the need for a more nuanced approach to innovation, one that promotes rapid experimentation and testing, gradual adoption, and continuity without disruption, allowing continuity without disruption. The adoption of AI should be gradual, with adequate safeguards in place to ensure systems perform as intended.
Building a framework for responsible AI
Beyond technical risks, the ethical dimensions of AI demand the fullest attention. AI systems learn from data, and if that data is biased, the outputs can perpetuate and even exacerbate inequalities. Responsible AI development must address issues such as data fairness, algorithmic transparency, and accountability.
This framework emphasises transparency and explainability in AI operations, ethical oversight through independent review boards, resilience and continuity by prioritising stability, inclusive development by involving diverse perspectives, and regulatory alignment to balance innovation with public safeguards.
The Peripheral Automation approach
Identifying initiatives that have an impact requires a careful decomposition of organisational systems. Recognising the data, automation, and intelligence layers as well as organisational domains give an inventory of organisational system components. This allows businesses to prioritise right digital initiatives with appropriate focus on innovation and change. Once efficacy and robustness are achieved—the same may spread across systems and processes.
Peripheral Automation powers AI by speeding up the implementation of specific systems and foster quicker innovation cycles. By creating a flexible layer of core databases, Peripheral Automation approach ensures AI can access a wide range of data from various business functions, such as financial and operational data. This improves AI training and insights, supporting more informed decision-making across the organisation. With PA, businesses can swiftly deploy AI solutions. For instance, financial data can power AI-driven automation that integrates seamlessly into lead qualification processes. This enables companies to experiment, refine, and scale new ideas swiftly, driving operational efficiency and delivering improved customer experiences.
A shared responsibility
AI’s evolution is a shared journey, requiring collaboration between technologists, businesses, governments, and society at large. Business leaders must champion responsible practices that prioritise long-term benefits over short-term gains. By fostering a culture of accountability, we can unlock AI’s potential while mitigating risks.
