How To Ensure Your Next AI Project Is A Success – Not A Statistic

It’s estimated that a massive 90% of artificial intelligence (AI) pilots and proofs of concept (POCs) end up being scrapped. Now, while you wouldn’t expect every AI POC to be productionised, this figure is alarmingly high.

So let’s dig a little deeper – why do so many AI projects fail to make it to production? And more importantly, what can you do to give your next AI initiative the best chance of being a success, rather than a statistic?

Why do so many AI projects fail?

There are multiple contributing factors to the failure of AI POC, but they tend to revolve around five core reasons.

#1  - No AI strategy or delivery plan

While 92% of companies intend to increase their AI investments, only around a quarter have an AI strategy. Organisations without a strategic framework to guide and govern their use of AI are opening themselves up to increased cost and risk, while massively hampering their chances of turning promising pilots into fully fledged production capabilities.

A delivery plan, aligned to the strategy, is also essential, but often absent.

#2  - Lack of expertise

Of course, identifying where AI could be of value and what you should deliver is just the start. You then need to turn these plans into reality, which is where the second major challenge typically arises: skills. 40% of respondents to a recent study cited employee skills gaps as a major barrier to AI adoption.

Generative AI, in particular, is still a relatively new technology – few had even heard of it before ChatGPT burst onto the scene. Consequently, many organisations don’t yet have the expertise in-house to be able to design, build, deploy, scale, and maintain production-grade AI capabilities, particularly when they involve generative models.

#3 - Cost concerns

Worries about budget also hold back many organisations’ AI successes. Will AI development require a big upfront investment in infrastructure? What will it cost to maintain the platforms? What will the total cost of ownership be if we productionise this pilot? Could any nasty surprises be lurking ahead?

#4 - Data: Compliance and ethics headaches

To achieve its full potential, AI needs access to data. This opens up all manner of complexities, be they technical, compliance-related, or ethical. The age-old challenge of bringing together data from siloed systems hasn’t gone away. Ditto issues around data quality: is the information complete, and error-free? And importantly, in the context of AI, does it contain bias?

Recent geopolitical developments have also brought two other data-related issues into sharper focus: data residency and digital sovereignty. Where is my data, and who has ultimate control over my platforms?

#5 Lack of suitable compute resource

Pilot and POC AI workloads demand substantial compute power. Productionising then takes things to a whole new level, often far beyond what organisations’ existing infrastructure is capable of.

Your three-stage route to AI success

While none of these challenges is trivial to solve, there are steps you can take to help ensure your next AI initiative doesn’t become yet another unwanted failure statistic.

Here at CDW, we’ve developed a unique and holistic three-stage AI delivery approach, combining CDW’s AI strategy, platform design, integration, deployment, and data governance expertise. With our technology partners Hewlett Packard Enterprise (HPE) and NVIDIA, we can implement a Private Cloud AI platform that is designed to give teams the freedom to experiment nimbly, then confidently scale successful AI pilots to production.

Step 1: Lay the strategic groundwork

Not every problem needs an AI solution. That’s why our first step will always be to help you identify where you should be using AI, and what to do to prepare.

Together, we’ll map out any current AI capabilities you have, identify your desired outcomes, and set out a workable plan covering skills, processes, data management, and technology to get you there.

Step 2: Governance to ensure responsible use of AI

Robust, appropriate governance is essential if you’re to use AI effectively and responsibly. And let’s not sugar-coat this: it’s an incredibly complex area. That’s why we have teams specialising in this specific space. They’ll consider the unique legislative, regulatory, security, and ethical requirements you need to meet, and help you put in place the necessary governance and guardrails.

Step 3: Architect and deliver the right AI-ready platforms

Lastly, you need suitable technology to support your AI initiatives. HPE’s Private Cloud AI solutions combine flexibility, scalability, and access to state-of-the-art software, with the ability to deploy in locations of your choice.

This means you can innovate at pace, using tools such as NVIDIA AI Enterprise and NIM inferencing microservices. Then scale successful POCs out to production, all while maintaining digital sovereignty, and meeting data residency requirements. Flexible and transparent OpEx or CapEx cost models help budget holders plan effectively and avoid surprises.

See HPE Private Cloud AI in action at our UK showcase

This three-stage programme has been proven in organisations large and small across a range of sectors, meaning you can proceed confidently, knowing you have the foundations your teams need to try out AI in the right places, and robustly productionise the most promising applications.

To get started, why not book a demo of CDW’s HPE Private Cloud AI UK showcase and experience it for yourself, guided by our specialist team? Contact us today to arrange your session.