AGI: Cognizant on Technological Challenges & Opportunities
Major technology players such as OpenAI, Google and Anthropic are progressing towards artificial general intelligence (AGI), compelling businesses to confront the technological realities of deploying AI at scale.
This momentum, however, raises significant questions concerning AGI's timeline and potential impact.
Experts forecast the emergence of AGI within the next decade, a development that could profoundly impact industries from financial services to manufacturing.
Against this backdrop, Amir Banifatemi, Chief Responsible AI Officer at Cognizant, shares insights into the implications AGI holds for business leaders navigating the digital landscape.
Demystifying AGI as a buzzword
“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.
- Transferring knowledge across different domains
- Reasoning about cause-and-effect relationships
- Navigating social contexts
- Generating creative solutions
- Making decisions with incomplete information
“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
Gen AI poses notable challenges in its implementation journey, as Amir points out.
Highlighting Gartner's classification of Gen AI in the “trough of disillusionment” within its technology Hype Cycle, he remarks that this technology, despite its burgeoning popularity in 2022 and 2023, is undergoing necessary recalibrations.
“Gen AI, which surged in popularity during 2022 and 2023, is undergoing a necessary recalibration,” he says.
Many enterprises are realising that issues such as AI hallucinations — erroneous information generation, integration costs and regulatory variables are more significant than initially expected.
“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 suggests a deconstruction of AGI into its functional components to understand its benefits and integrate it effectively into business strategies.
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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.”
Significant changes are expected in the labour market, with research indicating that 90% of jobs might undergo disruption from Gen AI technologies.
“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.”
Performance evaluation in business should prioritise real-world outcomes over theoretical standards, Amir asserts.
“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
Across various industries, the implementation of AI provides both opportunities and challenges.
In healthcare, AI improves diagnostics and efficiency, as seen with Google DeepMind's AlphaFold aiding drug discovery.
Likewise, AI-driven safety systems in construction correlate with reduced on-site incidents.
Conversely, deepfake technologies in the financial and commercial sectors create new vulnerabilities and undermine trust, as evidenced by a survey revealing that 26% of executives acknowledged at least one deepfake incident impacting financial data.
The risks of AGI
AGI-related concerns, often perceived as future risks, are already apparent in existing systems.
“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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