By Balakrishna D.R.
Artificial Intelligence (AI) is appreciated for its potential to solve some of the biggest challenges that mankind is faced with -- from drug discovery to climate change to poverty reduction and beyond. While it has made its way into many daily consumer uses, the lack of widespread enterprise applications is often cited by critics as a reality check over the hype cycle.
What is certain in the coming year and beyond is that AI will continue to push the boundaries of what is possible in both consumer and business. Companies are already automating mind-numbing repetitive workplace tasks, empowering employees to focus on higher-value, creative problem-solving. A McKinsey survey showed that enterprise adoption was up 6 per cent from the previous year to 56 per cent in 2021.
Going forward, businesses are likely to embrace a more fit-for-purpose AI that is deeply embedded into organisational culture and operating models. AI's future in 2022 and the years to come can be looked at through the lenses of discovery, democratisation, and de-risking.
Discovering new frontiers
The past 18 months challenged businesses on what can be accomplished almost overnight with the aid of technology. Digitally native firms, with AI embedded at the core of everything from architecture to operations, showed how to solve the most relevant problems whether it was to fix supply chain disruptions or support remote operations.
Traditional businesses will learn from this, adopting an AI-first approach to solve those challenges that are relevant and feasible to them. To identify the most suitable use cases, organisations will need to be creative, curious, and collaborative. Finding the right problems to solve will define the success of AI adoption.
To scale up AI successfully, organisations must utilise a two-pronged strategy. Harness AI for quick wins in the short term and embraces an enterprise-wide, AI-embedded culture that transforms how employees work alongside machines to boost their own cognitive abilities.
Additionally, organisation-wide data literacy will transform problem solving and value capture, which will cut costs, generate new revenues streams and make businesses more competitive. Recent advances in Quantum Computing will lead to the development of the next generation of algorithms and applications. This convergence with AI shows immense potential for the acceleration of Machine Learning (ML) and deep learning, resulting in new discoveries and use cases.
By the people, of the people, for the people
In 2022, AI's journey will not only be towards being more common, but also being more strategic. The bolt-on approach that has been common in the infancy stages, will be replaced by a deeper and wider adoption, becoming essential to the entire technology stack.
AI's power will be leveraged to rethink and rebuild services, products, business models, and entire approaches, as enterprises move from an experimentative to a bolder implementation mode. Access to AI/ML models will also trickle down beyond the deep pockets of Big Tech and large enterprises to midsize businesses, courtesy the availability of more off-the-shelf and reusable assets in AI marketplaces that will include reskilled talent.
Some of this will be made possible by a revolution in how code is written, developed and released. AI-centric software, automated by MLOps, will be empowered to become responsive and make changes to itself.
Simultaneously, this will free up software developers to more strategic and creative tasks and applications, which will again be augmented by AI. 2022 will also be the year when AI moves from beyond the confines of AI teams in organisations, to a wider stakeholder base within the business, and even beyond to an ecosystem play. Enterprises are starting to engage with citizen data scientists, who can help with reducing costs and risks, while bringing diversity of thought and approach.
From unknown devil to responsible angel
As AI increasingly permeates into human territory, the more scrutiny there will be around its decision making. Biased data that feeds AI systems is an almost universal ailment, further fuelling the mistrust of AI.
The call for transparency and ethics by design, is leading to the rise of AI that is at once explainable, ethical, auditable and even humble (when it knows that it is not sure about the right answer) -- essentially AI that is responsible. The need to ensure fairness and transparency in AI, taking care of safety, privacy and society at large has led to the recent release of IEEE 7000, the first standard towards achieving Responsible AI.
While AI has largely been under the purview of academia, businesses and governments, cybercriminals have also been quick to leverage its abilities to launch targeted attacks. Fortunately, building in AI-powered deterrents and responses into cybersecurity toolkits is also gaining momentum and can be expected to be implemented widely.
IDC expects 85 per cent of enterprises to combine human expertise with AI to augment employee effectiveness and productivity across the organization, by 2026. The coming year will see AI technologies picking up the pace towards and beyond this goal.
(The writer is a Senior Vice President at Infosys and leads their AI and Automation practice globally)
Source: IANS
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