As businesses engage in a competitive digital arms race focused on AI adoption, Cisco is committed to being a leader and partner in this field. AI has undoubtably been a dominating topic of discussion, influencing both daily lives and transforming the business landscape. We are at the forefront of the AI revolution utilizing our expertise in networking and cybersecurity to develop cutting-edge innovative solutions.
Building an AI-mature organization requires defining a clear strategy, frequent communication, and setting measurable outcomes to optimize results and avoid common errors. A recent Gartner report emphasizes the importance of a measured approach to AI adoption for businesses. Their AI adoption framework helps organizations avoid major pitfalls and maximize the chances of successful AI implementation. The framework encourages the use of the AI adoption curve to identify and achieve goals that increase AI value creation by solving business problems more effectively, faster, at a lower cost and with greater convenience.
Advertisement
The Gartner phased approach to AI adoption
AI can help classify and understand complex sets of data, automate decisions without human intervention, and generate anything from content to code by utilizing large repositories of data. However, underestimating the importance of prioritization can lead to delays and frustration.
Cisco’s key takeaways from Gartner’s AI phased approach include:
Phase 1. Planning: Start small, similar to building running endurance with short runs. Identify and recruit an internal champion to socialize efforts and gain support from key stakeholders. Establish three to six use cases with measurable outcomes that benefit the line of business.
Phase 2. Experimentation: Invest in the people, processes, and technology to ease transitions between phases, such as funding a Center of Excellence (COE) and teaching cloud AI APIs. Build executive awareness with realistic goals and be flexible to pivot as necessary.
Phase 3. Stabilization: At this stage, a basic AI governance model should be established, with initial AI use cases in production. The implementation team should have working policies to mitigate risks and ensure compliance. This st “pivotal point” is essential for expanding to more complex use cases. With strategic objectives defined, budgets in place, AI experts on hand, and technology at the ready, organizations can finalize the organizational structure and complete processes for the development and deployment of AI.
Phase 4. Expansion High costs are common at this stage of AI adoption as initial use cases demonstrate value and build momentum, leading to hiring more staff, upskilling employees, and incurring infrastructure costs. As the organization integrates AI in daily operations, it is vital to track spending, demonstrate progress against goals, and share outcomes with stakeholders to maintain transparency.
Phase 5. Leadership: AI success depends on fostering transparency, training, and shared usage across business units. Establish an “AI first” culture from the top down, where all workers understand AI’s strengths and weaknesses to be productive and innovate securely.
AI adoption varies across organizations
To avoid common mistakes, focus on creating a responsible use of AI that reduces technology risks and aligns with the resources currently available. Cisco’s key recommendations include:
- Choose the first project carefully, as most AI projects fail to deploy as projected.
- Do not underestimate the time required for deployment.
- Ensure your team has the necessary skills, capacity, and experience to take advantage of AI trends.
According to Cisco’s AI Readiness Index in the UAE, half (50%) of respondents ranked improving the efficiency of systems, processes and operations among the top outcomes that companies are looking to drive through adoption of AI. This was followed by growing revenue and market share (48%) and improving ability to innovate at (47%). However, with only 25% of respondents in the UAE saying AI deployment has been given the highest priority for budget allocation and incremental budget funding, organizations need to think about how they plan to fund AI deployments over the long run.
