Cognizant: What Leaders Must do for AGI’s Impact on Business

Major technology companies including OpenAI, Google and Anthropic continue advancing towards artificial general intelligence (AGI) while businesses grapple with the practical realities of deploying current AI technologies at scale.

Yet questions about AGI’s timeline and implications are intensifying.

As technology leaders predict AGI capabilities could emerge within the next decade – potentially transforming industries from financial services to manufacturing – Amir Banifatemi, Chief Responsible AI Officer at Cognizant, discusses AGI with AI Magazine.

Demystifying AGI as a buzzword

AGI presents immediate challenges for businesses rather than distant theoretical concerns, according to Amir.

“AGI has become one of the most talked-about topics in technology circles, evoking excitement, speculation and concern,” Amir says.

“Yet for leaders navigating today’s evolving AI and digital landscape, the more relevant questions are not about when AGI might arrive, but what it means, why it matters and how to prepare.

“As we move through 2025, the AGI conversation must shift from abstract futurism to concrete implications for markets, operations and society.”

The problem with Gen AI 

Amir’s warning comes as Gen AI faces implementation challenges across enterprises, with Gartner placing Gen AI in its “trough of disillusionment” category within its technology Hype Cycle framework.

“Gen AI, which surged in popularity during 2022 and 2023, is undergoing a necessary recalibration,” he says.

For example, many companies are discovering that deployment obstacles including AI hallucinations – when systems generate false information – integration costs and regulatory uncertainty exceed initial expectations. 

“After widespread experimentation, many enterprises are discovering that implementation hurdles – ranging from hallucination risks to integration costs and regulatory uncertainty – are often greater than anticipated,” Amir says, reflecting on the gap between enterprise expectations and current AI system capabilities.

“This moment of pause presents an opportunity for business leaders to reframe the AGI discussion away from timelines and hype and toward more meaningful strategic considerations.”

Cognizant’s roadmap to making AGI work for businesses

Amir believes that rather than viewing AGI as a single omniscient software system, businesses should examine its component capabilities.

These include: 

• Transferring knowledge across different domains
• Reasoning about cause-and-effect relationships
• Navigating social contexts
• Generating creative solutions
• Making decisions with incomplete information

“Each of these functions presents distinct technical challenges and offers different kinds of value and risk,” he says.

“Rather than treating AGI as a monolith, businesses should assess which cognitive functions are most relevant to their industries and operational needs – those capable of best delivering shareholder value, contributing to society and strengthening long-term strategic advantage.”

Transformation in the labour market is already underway. Cognizant’s New work, new world research indicates that 90% of jobs could face disruption from Gen AI technology.

“This change will surely be exacerbated by AGI,” Amir warns.

“For business leaders, this is not merely a workforce planning issue but a strategic opportunity: to invest in reskilling, shape future-of-work policies and support talent transitions that keep their organisations resilient and competitive.”

Amir argues that business performance measurement should prioritise practical outcomes over theoretical benchmarks.

“Accuracy in narrow tasks is not equivalent to intelligence,” he says. 

“A model that excels at answering trivia questions may fail catastrophically in unfamiliar or ambiguous scenarios.”

The challenges and opportunities of AI across sectors 

Current AI applications demonstrate both opportunities and risks across industries.

In healthcare, diagnostic tools improve access and efficiency – and Google DeepMind's AlphaFold system, which predicts protein structures, accelerates drug discovery processes. DeepMind is Alphabet’s AI research laboratory.

Meanwhile, construction companies deploy AI-based safety monitoring systems that correlate with reductions in workplace incidents.

However, financial services and commerce sectors face new threats from deepfake technology, which creates realistic but fabricated audio, video or images.

A 2024 survey found that 26% of executives reported their organisation experienced at least one “deepfake incident targeting financial and accounting data” within 12 months.

These incidents create cybersecurity vulnerabilities and damage trust relationships.

The risks of AGI

Current AGI-related risks including misalignment with human values and concentration of technological power are manifesting in existing systems rather than future developments. 

Addressing these challenges immediately mitigates near-term harm while establishing foundations for managing advanced capabilities.

“All these developments suggest that the most urgent AGI-related challenges are not in the distant future – they are here now,” Amir says.

“The AGI risks that often animate discussions, such as misalignment with human values or concentration of power, are already playing out in today’s systems.

“Addressing these challenges now not only mitigates near-term harm but also lays the foundation for responsibly navigating more advanced capabilities down the line.”

Amir says that companies must reframe their approach to AI development and deployment.

“Ultimately, AGI should not be seen as a finish line, but as part of a broader continuum of increasingly capable AI systems,” he concludes.

“The question may not be when machines might match human intelligence, but what kind of intelligent systems we are choosing to develop today – and whether they align with long-term goals of trust, accountability and economic inclusion.”

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