Artificial Intelligence (AI), particularly Generative AI (Gen AI), has captured the imagination of businesses worldwide.
Recent insights from Frost & Sullivan’s 2024 Global State of AI survey indicate that 89% of IT and business decision-makers consider AI and machine learning crucial or very important for achieving business goals such as increasing operational efficiency, supporting innovation, and improving customer experience. Similarly, 89% believe that generative AI will be disruptive for enterprises.
Despite this recognition, the same study reveals that many organisations are still in the early stages of AI maturity. While AI technology adoption has increased overall, enterprise maturity remains low, with many companies lacking a comprehensive AI strategy and roadmap.
Recently, Kenny Yeo , Director and Head of Asia Pacific Cyber Security Practice at Frost & Sullivan had the opportunity to discuss this issue with Sean Duca, CTO in CX APJC for Cisco, and there follolws an outline of the recommendations resulting from that exchange.
Beyond technology: Identifying meaningful use cases
While tools like AI copilots are prevalent, their impact often falls short of expectations. The recent Cisco AI Readiness Index study revealed that only 13% of companies globally are ready to leverage AI and AI-powered technologies to their full potential.
Key considerations:
- Purpose-driven adoption: Instead of adopting AI for its novelty, organisations should ask: What specific problem are we aiming to solve?
- Common applications: Tasks like summarising meetings, refining emails, and automating routine processes are typical starting points.
- Diverse models: Different challenges may require distinct AI models; a one-size-fits-all approach is seldom effective.
Engaging employees: The heart of AI integration
To uncover impactful AI use cases, organisations must engage directly with their workforce to:
- Identify pain points: Conversations with employees can reveal tasks that are repetitive, time-consuming, or prone to error.
- Co-create solutions: Collaboratively developing AI tools ensures they address real needs and gain user buy-in.
Sean from Cisco suggests, “Engage with team members and ask the straightforward questions: What tasks do you find troublesome? What is the most monotonous task? Which tasks are the most repetitive? Having these discussions can help identify potential AI-enabled use cases.”
Managing risks: Responsible AI usage
As AI becomes more embedded in business processes, new risks emerge, necessitating proactive management:
- Usage visibility: Gain a complete understanding of AI applications across the enterprise, identifying potential vulnerabilities.
- Shadow AI detection: Monitoring unauthorised AI tool usage helps prevent data leaks and compliance breaches.
- Data loss prevention (DLP): Implementing DLP measures ensures sensitive information isn’t inadvertently exposed through AI tools.
- Access controls: Restricting AI tool access based on roles and responsibilities minimizes risk.
Frost & Sullivan emphasises the importance of ethical frameworks and rigorous testing to ensure AI’s responsible deployment, especially in sectors like healthcare and finance.
Conclusion: From hype to value
These recommendations underscore the need for organisations to move beyond the hype and develop structured approaches to AI adoption, focusing on aligning AI initiatives with business objectives, engaging employees, and proactively managing associated risks.
However, Sean Duca from Cisco, emphasises that AI transformation is not merely a one-time implementation but rather an ongoing journey for the organisation. He asserts, “Success arises from organisations that approach this as an iterative process, continually learning and improving along the way.”
Source: SECURITY WORLD MARKET