One factor I hear constantly from enterprise leaders is that this: We consider within the promise of AI, however we’re nonetheless determining tips on how to flip it into actual enterprise development.
At Cisco, that is the journey we’re on. Over the previous 18 months, we’ve invested in AI instruments and studying experiences designed to assist individuals improve their work and ship measurable enterprise outcomes.
To grasp whether or not these investments are making a distinction, the Folks & Communities group stepped again and requested an even bigger query: When AI turns into integral to how our individuals work, how does it form engagement, efficiency, and development throughout Cisco—and what does that imply for the enterprise?
Over the previous yr, Cisco’s Folks Intelligence group examined how workers have interaction with AI instruments, drawing on surveys, interviews, focus teams, and knowledge evaluation. The findings ship a transparent sign: our strategy is working—and when paired with a tradition that encourages studying, experimentation, and belief, the chances for our individuals and our enterprise are limitless.
Key Findings:
1. AI Powers a Higher Worker Expertise
AI is greater than a device—its use positively impacts particular person engagement, retention, efficiency, and development.
- AI boosts particular person engagement: We’ve seen a robust, mutually reinforcing cycle emerge: engaged workers actively use AI, and AI use deepens worker engagement. AI customers who have been interviewed report better enthusiasm for Cisco’s mission, stronger confidence in our future, and really feel extra challenged and empowered to develop in comparison with their friends who don’t use AI. In addition they report having extra alternatives to make use of their strengths day by day.
- AI strengthens retention: Opposite to claims that AI customers usually tend to go away, AI customers at Cisco keep longer—and use AI twice as typically every month as workers who exit the corporate.
- AI enhances productiveness and efficiency: Over 70% of workers surveyed report that AI helps them save time, enhance productiveness, and deal with routine work extra effectively. This enhanced productiveness seems to be contributing to efficiency, as workers who use AI instruments extra regularly are inclined to obtain barely greater Particular person Efficiency Issue (IPF) scores.
- AI accelerates profession development: AI customers usually tend to be promoted quicker, spend much less time in the identical grade, and are 40% extra prone to be designated Crucial to Retain. These really helpful for promotion use AI 50% extra typically than those that aren’t. These patterns counsel that Cisco is changing into a spot the place AI abilities should not solely developed however rewarded.
2. Driving AI Adoption Throughout Our Workforce
Understanding what drives and hinders adoption helps us create the suitable atmosphere for studying and innovation.
- Leaders who use AI amplify adoption: Staff whose direct leaders use AI are twice as doubtless to make use of it themselves. High-down modeling actually issues. Even small actions like mentioning AI instruments in group conferences or 1:1s create alternatives to introduce sensible options, construct consolation, and normalize AI utilization.
- Flexible work environments help AI utilization: Hybrid work and worker autonomy might help extra AI utilization. Curiously, workers who select to come back into the workplace three or extra days per week are extra doubtless to make use of AI instruments than their friends.
3. Designing Efficient AI Skilling Methods
How workers be taught AI makes all of the distinction. Our findings reveal what works finest to maintain our workforce on the forefront of AI innovation.
- Most workers are studying by doing: 87% of workers surveyed report studying AI by curiosity-driven, role-relevant experimentation with AI instruments. Entry to supporting alternatives and assets is essential to sustained confidence and adoption.
- Leaders want tailor-made help: Director-level leaders surveyed report barely decrease confidence in utilizing our inside AI device than mid-level workers, in addition to decrease general satisfaction with AI instruments. These findings counsel that senior leaders might profit from tailor-made studying alternatives and focused help to assist construct their confidence and satisfaction with AI, to allow them to extra successfully champion AI adoption throughout the group.
- Mid-level workers are searching for extra specialised AI abilities: The AI Options on Cisco Infrastructure Necessities Studying Path (a role-specific coaching for mid-level IT professionals supplied by Cisco U.’s Ladder Up program) noticed thrice the enrollment of earlier choices. This surge displays a powerful demand amongst mid-level IT professionals to maneuver past foundational AI ideas and acquire extremely sensible, role-specific abilities, corresponding to deploying, managing, and optimizing AI programs in real-world environments.
4. Constructing Pleasure Round AI
Rising AI adoption at Cisco is grounded in optimism and a shared perception that expertise ought to elevate human work.
- AI is sparking pleasure: Whereas analysis corresponding to Pew Analysis Middle’s 2025 research on AI within the office finds that many staff are extra anxious than hopeful about AI’s affect on their jobs, Cisco workers who have been interviewed described feeling captivated with its potential.
- AI adoption is rising throughout Cisco: Each technical and non-technical teams present progress towards extra frequent AI utilization.
- Company guardrails are making a distinction: Cisco’s Accountable AI Framework, together with clear and constant messaging from management, is resonating. Staff who have been interviewed perceive that AI is best with human oversight and see verifying accuracy and making use of vital considering as important elements of utilizing AI effectively.
Closing Ideas
AI is already making a significant distinction for Cisco’s workforce, and its affect is rising.
Every worker’s journey with AI is completely different, and everybody at Cisco has a job to play. As this transformation continues, we stay dedicated to equipping our individuals with the talents, instruments, and tradition they should thrive in an AI-powered future. By embracing findings like these, we’re evolving collectively, constructing on what works, and shaping what comes subsequent.
Methodology
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Scope: Complete evaluation (August 2024 – October 2025) of AI device adoption, utilization, expertise, and affect inside Cisco, specializing in CIRCUIT (Cisco’s inside AI assistant), GitHub Copilot, and Ask Cody.
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Knowledge Sources: Anonymized and aggregated knowledge from AI device utilization, AI studying, worker expertise surveys (Actual Deal, Engagement Pulse, IT@Cisco, AI@Cisco), worker demographics, collaboration knowledge (Webex, occasion/workplace attendance), efficiency/rewards, abilities, and hiring/termination knowledge.
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Analytical Strategies: Hybrid strategy combining quantitative and qualitative strategies, together with descriptive statistics, statistical modeling (e.g., XG Increase, OLS regression), worker interviews, and worker focus teams.
Acknowledgments
This analysis was made potential by the devoted efforts of the Folks Intelligence group and IT companions:
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Sponsors: Roxanne Bisby Davis, Matt Starr, Madison Beard, John Lagonigro
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Leads: Hanqi Zhu, Might Liew
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Researchers & Knowledge Scientists: Mary Williams, Peiman Amoukhteh, Madi Brumbaugh, Erik Wangerin, Delia Zhou, Casey Bianco, Ty Busbice, Rachith KS, Sara Hardesty, Joshua Rickard
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Assist Crew: Kensleigh Gayek, Kate Pydyn, Grace Jain, Charlie Manning, Samantha Everett, Lauren Grimaldo, Elle Sawa, Shavonda Locke, John Misenheimer
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IT Companions: Tammy Fitzwater, Jennan Tracy, Arebah Ajni, Dick Loveless
