Six pillars to build AI success
How to build an AI plan that addresses everything from strategy to infrastructure, data to governance, and a culture that nurtures talent.
While 95% of organizations have an AI strategy in place or under development, it is never too late to start. Every organization needs to establish a strategy that is right for their business, employees, and partners, and also supports business goals. While not all deployment strategies are the same, more than 80% of organizations are starting AI deployment by focusing on their IT infrastructure.
Form an AI committee and establish clear ownership.
Define goals and set quantifiable use cases to start.
Establish responsible AI usage guidelines.
Ensure infrastructure is scalable, sustainable, and secure.
Measure progress and outcomes and evolve to high-risk/high-reward use cases.
AI and ML deployments will absolutely lead to an increased demand for GPU resources to support network scale and AI workloads. While 95% of IT leaders agree that AI will undoubtedly increase their workloads, only 13% feel they are ready to support the computational demands required to train, support, and accommodate the change.
Understand your organization’s existing compute power usage.
Project required increase in GPUs to ensure optimized network performance and efficiency.
Ensure security capabilities are in place to secure AI workloads and protect data.
Establish support for increased power consumption and the organizational infrastructure changes required to scale.
Measure outcomes.
AI without data is like a lightbulb without electricity. It just does not work. AI depends on clean, centralized, and secure data to establish well-integrated analytics, data sources, and AI platforms. It's critical that the use cases identified align with reliable and available data.
Ensure hygiene, quality, and reliability of in-house and external data sources.
Centralize data to eliminate silos and make data available and accessible.
Align data assets with desired use cases.
Establish efficient data access protocols and procedures.
Train staff on responsible and secure AI usage.
Because AI can automatically generate insights that influence decisions and actions, clear governance for how AI is developed and deployed is critical to eliminate bias. These tips can help establish a fair and responsible approach to governing AI usage.
A global effort is needed to create equitable access to artificial intelligence.
Understand risk and impact of AI bias.
Implement mechanisms to detect bias over how AI-based solutions are built, implemented, and operated.
Ensure comprehensive and consistent checks on AI algorithms and impact to existing and future data processing.
Define data handling standards to govern responsible usage of AI.
Measure governance and life cycle management with observability.
Fear and misconceptions around AI are real and can be scary for workers who worry AI may replace their job or expose a gap in their skills. Embrace the chance to build an AI-ready workforce that can help employees expand their careers and improve their own growth.
Understand your company's current AI knowledge and skills.
Identify knowledge gaps and areas for skill building.
Invest in training and hire new talent to bridge gaps.
Establish proficiency for data handling, security, and responsible usage of AI.
Measure AI acceptance rate and percent of talent trained on new tools.
According to the Cisco AI Readiness Index, almost 80% of organizations feel pressure to embrace AI. While leaders may be eager to get started and take advantage of operational efficiency and performance benefits, not all employees necessarily share that view. These tips can help you connect with employees and create a positive conversation around the possibilities of AI at your company.
Understand employee receptiveness to AI.
Address AI concerns and internal goals.
Communicate early and often regarding AI plans, best practices, employee expectations, and goals.
Share AI wins to encourage understanding and adoption.
