Partner content: How to move beyond AI proof of concept

clock • 3 min read
Partner content: How to move beyond AI proof of concept

Across industries, organisations are looking to use AI to enhance decision-making, automate processes, and gain crucial insights. However, adoption still has a long way to go, with many only just beginning to embark on their journey to establishing a culture led by advanced data analytics.

Computing's latest report in this space, carried out in collaboration with Intel®, finds that fewer than one in ten organisations are mature enough to operationalise and scale AI. Roughly 80 per cent of respondents agree their organisation is in the early stages of adoption, suggesting they are encountering problems with moving from proof of concept to revenue-generating success.

When asked how their organisation's technology strategy will change in the next few years, Computing respondents overwhelmingly highlight AI. Plans to increase its use by automating more processes to save customer and employee time are seen as a key advantage in implementation. They see AI as an opportunity but recognise the importance of identifying current processes and how they can be improved using the technology.

Moving beyond proof of concept is dependent on a strategic rollout, comprising all elements of an organisation across data, technology, people, and governance. Careful planning punctuated with relevant measurable performance metrics, is the key to success with the least cost and risk to an organisation.

Data and technology

Examining what data is required to support specific AI initiatives as well as how existing or needed infrastructure and tools can train, deliver, and manage AI should be top of mind. AI implementations are only as effective as the data they are fed with. Most organisations utilise some form of data analytics to summarise and visualise historical data. Some will have reporting tools that enable detailed queries amidst trend and causation identification. Organisations looking to achieve their AI strategy ambitions must examine their current data set capabilities, integration, and hygiene to see how they can be used to train AI models and generate business benefits.

People and governance

In addition, having the right leadership practices and roles to establish and nurture a culture driven by AI is important. Policies and processes should be informed by how specific organisations operate. Skills shortages are pervasive in technology roles, meaning organisations must review their in-house capacity to achieve business ambitions. Does your organisation have skills gaps? How can you fill those gaps? Do you have organisational buy-in for implementing AI? How can you increase awareness for AI initiatives? These are important questions that must be answered before beginning an AI journey. For many, answers to these questions will lie in adopting technology that democratises data and AI by offering user-friendly UX and requires little development or data analytics expertise.

To learn more about Computing's research into real world AI use cases with real results, read the full report.


Sponsor insight - Intel

Organisations must harness AI to extract value from data, but challenges abound. Data pre-processing, from discovery to breaking down silos, to quality control, and managing it from edge to cloud, come first. Taking the right approach to modelling, from analytics to machine or deep learning, with the right technology and expertise comes next.

Intel provides a holistic and open path forward, addressing the full data, modelling and deployment pipeline, with the freedom to compute on whichever architecture is best, including the only x86 CPU with built-in AI acceleration.

This article is sponsored.

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