June 14, 2024 | 5 min read
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The rapid rise of AI applications in transformation

Matthew Jones

Director of Operations

The long and the short of the matter is, transformation is evolving rapidly. The boom of Artificial Intelligence (AI) from late 2022 onwards 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:

  • Improve customer service.
  • Safeguard the business.
  • Personalize communications.
  • Boost efficiency.

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.

How does AI compare against other transformative technologies?

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 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, 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:

  1. 1. AI & Machine Learning (ML) (42%)
  2. 2. Security and cybersecurity (37%)
  3. 3. Cloud or multi-cloud networking connectivity (35%)

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, AL 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.

Where are the opportunities and pitfalls of AI?

Which is the most popular AI integration for enterprise?

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:

  1. 1. Customer service applications (chatbots, online Q&A pages, etc.) (36%)
  2. 2. Cybersecurity initiatives (45%)
  3. 3. Employee applications (skills/talent/training/productivity) (45%)

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.

What challenges do enterprise technology leaders face when implementing AI?

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:

  1. 1. Concerns over possible issues relating to AI governance or ethics (36%)
  2. 2. Regional variations in ability to implement (28%)
  3. 3. Pace of change (28%)
  4. 4. Legacy infrastructure (26%)
  5. 5. Concern over changing or reducing IT team headcount (25%)
  6. 6. Our networks or connectivity are not ready to support large data/AI projects (22%)

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 due to legacy infrastructure and networks. Network agility and scalability are key to take on the large amounts of data required for AI applications.

So how do you ensure your network is ready to support AI transformation?

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 impact on an enterprise’s network is 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?

Step 1: Define your use case and choose your AI application

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.

Step 2: Assess and upgrade your network infrastructure

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.

Step 3: Implement robust security measures

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.

Step 4: Integrate AI with cloud and hybrid solutions

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.

Aside from AI, what else keeps technology leaders up at night?

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.

Go faster to the future with an AI-ready network from Expereo

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.

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