Major technology companies are moving beyond AI experimentation to thoughtful, scaled implementation with robust guardrails, according to executives speaking at Mobile World Congress Las Vegas. Qualcomm’s Durga Malladi declared that generative AI has become “a necessity” rather than a luxury for enterprises, signaling a fundamental shift in how businesses view AI adoption.
Verizon’s approach exemplifies the industry’s maturation, with Executive Vice President Shankar Arumugavelu explaining that 2024 marks a transition from “aggressive experimentation” to “thoughtful implementation at scale.” The telecommunications giant has deployed AI across network operations management, workforce planning, and customer service, while establishing an AI Council to oversee responsible adoption, development, and deployment. This governance structure reflects growing recognition that AI implementation requires executive-level oversight to mitigate risks while maximizing benefits.
Salesforce’s Chief Ethical and Humane Use Officer Paula Goldman shared a compelling use case where AI actually helps identify and address human bias. She described a venture capital firm using AI during investment meetings to listen, take notes, and identify patterns—including potential biases that human participants might miss or feel uncomfortable raising. This demonstrates AI’s potential to enhance rather than replace human decision-making.
The consensus among speakers emphasized that AI should complement, not replace, human workers. However, executives also struck cautionary notes about implementation challenges. Anthropic’s Julian Williams distinguished between internal-facing AI applications with lower risk profiles and external-facing use cases requiring extensive red-teaming and evaluation frameworks. He noted that companies show more hesitation with customer-facing applications that could impact millions of users.
Sand Technologies’ Chief Product Officer Piers Sanders warned against technology-first approaches, criticizing companies that “jump from technology and then try to find a use case for it.” He emphasized the importance of understanding customer problems before implementing AI solutions. The overarching message from MWC Las Vegas was clear: successful AI implementation requires balancing innovation with responsibility, ensuring technology serves both business objectives and human needs.
Key Quotes
We’ve reached a point where, for enterprises, generative AI is not a luxury, not an option to be considered, but it’s a necessity
Durga Malladi, Senior Vice President at Qualcomm, emphasized the fundamental shift in how businesses must view AI adoption, signaling that competitive pressure has made AI implementation mandatory rather than optional for enterprises.
With generative AI at Verizon, last year was all about aggressive experimentation. This year is about thoughtful implementation at scale
Shankar Arumugavelu, Executive Vice President at Verizon, described the company’s evolution in AI strategy, highlighting the industry-wide maturation from testing to responsible deployment with proper oversight mechanisms.
AI is not afraid to raise the issue, and as a result, they have a much, much richer conversation and they have better decision-making
Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer, illustrated how AI can identify bias that humans might miss or hesitate to address, demonstrating AI’s potential to enhance rather than replace human judgment.
To make AI work for our businesses, we have to first make sure it works for the people our businesses serve and the people our businesses employ
Goldman concluded her keynote by emphasizing the human-centric approach necessary for successful AI implementation, arguing that technology must serve people rather than the reverse to unlock AI’s full potential.
Our Take
The convergence of views from Salesforce, Qualcomm, Verizon, and Anthropic reveals an emerging consensus on enterprise AI strategy. What’s particularly striking is the shift from AI evangelism to AI pragmatism—executives are now focused on governance, risk mitigation, and human-centric design rather than simply promoting adoption. The establishment of formal oversight bodies like Verizon’s AI Council suggests that AI governance is becoming as important as AI capability. The distinction between internal and external AI applications provides a practical risk framework that acknowledges AI’s limitations, particularly around hallucinations and customer-facing scenarios. Most telling is the emphasis on complementing rather than replacing human workers, which may reflect both ethical considerations and practical recognition that AI works best in augmentation roles. This measured approach contrasts sharply with earlier AI hype cycles and suggests the industry is maturing toward sustainable, responsible implementation.
Why This Matters
This article captures a pivotal moment in enterprise AI adoption, as major corporations transition from experimentation to scaled implementation. The establishment of governance structures like Verizon’s AI Council signals that AI oversight is becoming standard practice, not an afterthought. This matters because it sets precedents for how businesses across industries will approach AI deployment.
The emphasis on responsible AI and human-centric design reflects growing awareness of AI’s risks, including hallucinations and bias. Companies are recognizing that rushing to implement AI without proper guardrails can damage customer relationships and employee trust. The distinction between internal and external AI applications provides a practical framework for risk management that other organizations can adopt.
Most significantly, the shift from viewing AI as optional to necessary indicates that competitive pressure is driving adoption. Companies that fail to implement AI thoughtfully may fall behind, while those that rush without proper oversight face reputational and operational risks. This creates urgency for developing robust AI governance frameworks across all industries.
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