As CPO, Sander Barens manages Expereo‘s service portfolio, driving world-class connectivity solutions for global enterprises.
Enterprises across the globe are currently grappling with how to deliver on the potential benefits the recent proliferation in AI tools offers. However, global organizations are finding it challenging to attract or retain essential skills in AI, data, automation and networking.
The Skills Shortage Dilemma
The talent gap is not just slowing organizations down but also putting ambitious AI plans at risk. A shortage of skilled professionals is a significant barrier to growth and innovation, making it difficult for enterprises to stay competitive in a rapidly evolving technological and business landscape.
This talent shortage is not just about numbers; it’s about the quality and expertise of the available workforce. AI, data, automation and networking are complex fields that require a deep understanding of both the relevant technology and the specific applications concerned. Without the right talent in place, organizations struggle to develop and implement effective AI strategies, leading to missed opportunities and wasted investments.
While not the most critical skills that most regularly come to mind, a deficiency of required networking talent has its own complications for large organizations. Our Enterprise Horizons 2024 report underlined networking talent as the second hardest area to recruit for, with 36% of enterprises identifying it as a key challenge. The struggle to onboard talent extends beyond AI to the core infrastructure skills necessary for successful AI deployment.
The Need For Effective Network Infrastructure
To ensure the successful implementation of the latest technologies such as generative AI and machine learning tools, all network infrastructure needs to be able to support the addition of these new tools into an organization’s ecosystem. Without this, organizations won’t be able to have the bandwidth to make the most of new tools. As a result, securing strong returns on investments in new applications will prove to be a considerable challenge.
For far too long, network performance and flexibility have been among the key factors preventing companies from implementing new tools and initiatives at scale and at pace. Specifically, our report highlighted nearly 40% of global technology leaders citing network performance issues such as application responsiveness and latency as a limitation of their network, while a similar number pointed to their networks’ inability to scale flexibly on demand.
These challenges highlight the importance of robust and adaptable network infrastructure in supporting the successful development and deployment of new AI initiatives.
Existing network performance issues can significantly hinder the deployment of AI applications. AI tools and systems often require real-time data processing and analysis, which demands high network performance and low latency. If the network cannot meet these requirements, the AI applications will not function effectively, leading to poor performance and user dissatisfaction. Additionally, the inability to scale the network flexibly on demand can limit an organization’s ability to expand its AI initiatives and adapt to changing business needs.
Properly Partnering With A Networking Provider
Where enterprises have recurring network performance issues or can’t get the right networking talent in place, they should consider beginning the journey toward transitioning to a partnership with a networking provider.
Internally, enterprises should begin this journey by assessing their current capabilities as well as evaluating their network performance, personnel skills and infrastructure to identify gaps. Clear objectives and expectations for a potential partnership should be established, including performance metrics and desired outcomes. In addition, updating processes and policies is crucial to facilitate smooth collaboration with the external partner once a partnership is formed.
The engagement of key stakeholders is also vital. This will not only ensure alignment and buy-in for the transition but also allow for a detailed transition plan outlining steps, timelines and responsibilities to be put in place effectively.
At the same time, to ensure effective AI training and development, enterprises need to identify skill gaps through a comprehensive assessment. Organizations should develop a training roadmap that includes both foundational and advanced AI and networking topics tailored to different roles while also seeking to make use of online platforms and certification programs.
Perhaps most critical is that businesses foster a culture of continuous learning and development by looking into collaborations with universities and other external providers for specialized AI courses, further enhancing capabilities.
When researching potential network partners to work alongside, enterprises should look for firms with proven expertise that use the most advanced technologies with robust security measures. Checking the partner’s reputation through references and reviews helps gauge reliability, while organizations should seek to avoid partners that lack transparency, have poor communication or show inflexibility to their specific needs.
Challenges may arise during any transition—including resistance to change among some teams and integration issues more widely. However, by engaging in change management practices, planning for a phased integration and maintaining open communication, businesses can mitigate these challenges significantly. Crucially, overreliance on the partner can be risky, so maintaining some internal expertise and having contingency plans in place is advisable.
Conclusion
To ensure the successful development and deployment of AI technologies, the critical shortage of both AI and networking skills needs to be taken on and addressed as one. This is critical for organizations to achieve their growth ambitions and stay ahead of competitors.
By adopting a strategic approach that includes undertaking the necessary preparatory work for a successful partnership with a networking provider, enterprises can build strong foundations to implement AI initiatives successfully. The challenges may be considerable, and the journey may seem overwhelming, but the rewards and end results will likely prove well worth the effort.
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