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The rise of AI applications in transformation strategies

Matthew Jones

Director of Operations

The long and the short of the matter is, transformation is evolving rapidly. The boom of Artificial Intelligence (AI) started in late 2022 and has changed the game when it comes to digital transformation. So much so that many companies are racing to implement the right AI applications in the right way to:

And that’s just to name a few of the many applications of AI.

Machine learning has been on the transformation agenda for some time, but has recently taken a back seat to AI applications. Arguably, the speed of which companies are trying to implement generative AI within their processes far outstrips the progress made with AI in the last decade.

Amongst the long list of technologies available to transform a business, including the cloud, the Internet of Things, machine learning, and virtual, augmented and mixed reality, AI adoption has leaped to the top of most company’s transformation plans. The sophistication of large language models and their potentially positive impact on customer support and productivity are too tempting to resist.

Due to the popularity and possibility offered by AI and natural language processing (NLP), transformation now means something very different than the pre-AI boom definition. Every company may have access to the same AI applications, yet the business outcomes can vary wildly.

We recently commissioned IDC to produce the follow up research to last year’s Enterprise Horizons report. 650 technology leaders were surveyed to explore where they see value in AI applications for transforming a business today and produce the IDC InfoBrief, Enterprise Horizons 2024: Technology Leaders’ Priorities on their Digital Business Journey.

For context, the top three technology investments sited by the 650 global technology leaders in the IDC InfoBrief, Enterprise Horizons 2024: Technology Leaders’ Priorities on their Digital Business Journey were:

The results are very telling about the flexibility and the potential that AI offers as it has shot to the number-one priority for technology leaders. In last year’s Enterprise Horizons report, AI systems and ML were the number three priority, superseded by security and 5G investments.

What’s more, according to the IDC InfoBrief, Enterprise Horizons 2024: Technology Leaders’ Priorities on their Digital Business Journey, 31% of those surveyed this year stated that AI/ML will be critical to fulfilling their business priorities, and 60% site that it’s important if not critical to fulfilling business priorities. However, only 15% of those surveyed stated they had their AI initiatives operational at scale. So it’s clear everyone is proceeding with caution, unsure of how to correctly implement AI transformation.

The IDC InfoBrief, Enterprise Horizons 2024: Technology Leaders’ Priorities on their Digital Business Journey found that the top three AI initiatives technology leaders were prioritizing were:

It’s interesting that cybersecurity has topped the poles as one of the most popular applications of AI. AI for some time was a serious cybersecurity concern. Prompt injection and data poisoning attacks were cited early as risks for using generative AI and LLMs, but here we are looking to use AI to help prevent threats. AI can be used to safeguard businesses by detecting anomalies in cybersecurity threats, often identifying attacks before they can inflict damage. The ability to respond quickly and proactively is a huge advantage in today's landscape, where cyberattacks are increasingly sophisticated and frequent.

However, its application to Human Resources (employee services) and products and services is an extremely promising area for many companies looking for high-quality communications and personalized responses that boost the customer experience.

The challenges that most technology leaders face when implementing AI, however, are significant. According to the IDC InfoBrief, Enterprise Horizons 2024: Technology Leaders’ Priorities on their Digital Business Journey, the top six were:

As AI has long been the topic of iconic science fiction explorations, ethical considerations and concerns are no surprise!

We should be thinking very carefully about how we allow AI to collect our data. What we allow it to do with it, and how we keep biases out of the process. This is a huge challenge to overcome as very little precedent exists to guide enterprises with their AI implementations. Particularly when the reputational and liability risks are massive if it goes wrong.

What is of particular interest to me is how many tech leaders struggled to implement AI models due to legacy infrastructure and networks. Network agility and scalability are key to take on the large amounts of data required for AI applications.

With the famous (yet ominous) McKinsey stat hanging over everyone’s head, that 70% of all transformation projects are doomed to failure, we all need to proceed with precision, yet caution when it comes to the race to implement generative AI. Which is not easy.

The network implications of AI are extremely important and should be a leading component in your AI transformation.

So how do you ensure your network is ready to support a successful AI implementation program for your organization?

When it comes to implementing AI applications, it can be tempting to take an everything everywhere approach. But like most major transformation programs, it’s a good idea to take it small and specific at first, starting with a specific use case and expanding from there.

Conduct a thorough assessment of your current network infrastructure to understand its strengths and weaknesses. Evaluate elements like bandwidth, latency, and overall network performance.

See where you may need to deploy additional connectivity solutions. For example, AI often demands high-speed data processing and real-time connectivity which you’ll need to adapt your network to.

Implement advanced connectivity solutions, like SD-WAN, to optimize traffic flow and ensure reliable performance across multiple sites and cloud environments.

You need to look at how you can enhance network security protocols to protect data integrity and privacy with built in monitoring and threat response processes.

I would recommend you deploy network security solutions like firewalls, intrusion detection systems, and secure access controls to safeguard against cyber threats and implement real-time monitoring tools to detect and respond to potential security breaches swiftly.

There are cloud solutions that have specialized infrastructure optimized for AI workloads, like scalable computing power and storage. However, if you’re using the cloud, then you need to ensure reliable connectivity to your cloud applications.

If you’re using a hybrid network approach that combines on-premises infrastructure with cloud services, you’ll enable your AI applications to operate efficiently across different environments.

By assessing and upgrading your network infrastructure, ensuring robust security, optimizing for AI workloads with cloud and hybrid solutions, you can create a network environment that supports AI transformation.

AI is a huge topic right now but there are so many factors to consider. Check out the Expereo commissioned IDC InfoBrief, Enterprise Horizons 2024: Technology Leaders’ Priorities on their Digital Business Journey today to see insights about attitudes towards growth, digital technology investment priorities, challenges and limitations, the talent market, and the changing role of the technology leader.

The long-term impact of AI on digital transformation is clear: those who embrace it early and implement it wisely will be better equipped to face future challenges and capitalize on emerging opportunities. As the AI landscape evolves, companies must stay agile and innovative, ensuring they leverage AI not just as a trend but as a core driver of their transformation strategy.

Expereo is no stranger to the process of integrating AI solutions. Our understanding of the ins and outs of what a network needs to support large-scale AI transformation programs means you get a network that is custom built to support your transformation goals.

Get in touch today to discuss how we can design, build and run a network that can support your AI needs.