Juniper Networks on the increase in enterprise AI adoption
Tell me about Juniper, your role and your responsibilities.
Juniper Networks provides secure, AI-driven industry-leading solutions to simplify network operations and deliver optimum experiences that transform how people connect, work and live. As CIO, I lead the ongoing enhancement of the company’s IT infrastructure and applications architectures to support the growth objectives of the company. My team and I are also responsible for showcasing Juniper’s use of its technologies to the world.
Can you tell me about your company's recent research into working with AI?
Juniper Networks partnered with Wakefield Research for the most recent report, in order to assess AI adoption, growth and integration of AI at enterprises in North America, APAC and EMEA. This research surveyed 700 people across different titles and industries to gauge sentiment and adoption levels of AI in their organisations. The overarching outcome of the research was finding that a large increase in enterprise AI adoption over the last 12 months is yielding tangible benefits to organisations, with 63% of company leaders saying they are at least “most of the way” to their planned AI adoption goals. Critically, the survey revealed that enterprises are increasingly moving beyond proofs-of-concept and limited trials of AI and are now implementing the technology cross-organisation. For example, 29% of European respondents described their organisation as ‘very reliant’ on AI.
What shocked you about the findings?
There’s a lot of energy around AI, so the fact that adoption is rising rapidly is not shocking. However, given Europe, in particular, is at the forefront of the regulatory environment, a more surprising takeaway is just how wide a gap there is between the implementation of governance and the rate of adoption. While leaders recognise the importance of governance in place to manage AI, only 9% of AI/ML leaders surveyed consider their AI governance and policies – including establishing a company-wide AI leader or responsible AI standards and processes – to be “fully mature.” And almost half of the surveyed leaders (48%) think more needs to be done to effectively govern AI.
I believe this, in part, stems from how overwhelming it can be to think about all the requirements surrounding AI. My advice is to first understand what you need to use AI for and why – and evaluate the relative risks of that usage of technology. Then, it’s important to consider how strong your cybersecurity is with respect to those applications, the data and the tools used to build them. Consider that AI is cross-functional and impacts all aspects of your organisation, get buy-in from all stakeholders at the top, be transparent about how you’re deploying the AI, and, finally, be inclusive in developing your AI systems.
Why is there such an AI skills gap? What can be done to address that?
The skills gap for AI adoption is distinctly prominent in Europe, with 59% of respondents saying that their workforce is capable and prepared to work with AI, in comparison to 81% in North America. This is partly due to limited inclusion of AI skills (usage, management and deployment) in employee learning and development frameworks. However, over half of European respondents have indicated that they intend to rectify this in the next 12 months. Furthermore, businesses can increase their AI talent pipeline with internships and partnerships with universities to upskill and expand the workforce to enable AI growth. This is a solution which 33% of European organisations are undertaking currently, according to the research.
What do you think businesses prioritise when it comes to leveraging AI?
European businesses are at the forefront of ensuring governance is embedded into their AI solutions, with many prioritising responsible standards and processes for the technology. Whilst only 10% of European workers feel that AI governance is mature in their company, nearly half (42%) did report that governance is growing. Another crucial priority for companies globally is ensuring robust cybersecurity when maintaining and securing enterprise AI. 95% of AI/ML leaders report cybersecurity as a critical component, with 29% reporting it is the singular most important area, versus 14% in 2021.
