Navigating the AI Seas: the six pillars for AI readiness

Over the past decade, Artificial Intelligence (AI) has swiftly advanced from a, relatively, niche research area into a transformative force reshaping industries worldwide. According to the US International Trade Administration, the UK AI market was valued at over $21 billion last year and is projected to reach $1 trillion by 2035. Analysis by Sopra Steria Next, based on available research, predicts that global AI spending will reach $1.27 trillion in 2028.

Today, this growing market projection clearly demonstrated. It’s almost impossible to browse LinkedIn without encountering a plethora of opinion pieces on the subject. (Here’s to one more!) Noise which is backed up by statistics provided by Google Trends that shows an increase in “Interest over time” for “AI” search terms from 14 in July 2014 to 98 in July 2024.

As organisations strive to remain competitive and drive efficiencies, the question is no longer whether to adopt AI, but how to best prepare for and leverage it effectively. To do this, it’s essential to first understand the current state of AI. Then, its potential applications, what an organisation must get right to adopt it safely, and how to industrialise the process of iteratively delivering value led use cases.

Understanding the Current State of AI

First, what do we mean by AI? For the purposes of this piece, we will refer to the definition adopted by the European Parliament, which states:

‘AI system’ means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments.

Currently, when most people think about AI they’re usually thinking about Generative AI, a broad category of AI systems that generate new content including text, images, music, videos and more. More specifically, and in the case of juggernauts such as Chat GPT, BERT and Claude, they are thinking of Large Language Models (LLMs), a specialised type of AI model and subset of Generative AI. They make use of Natural Language Processing (NLP) to generate new humanlike text-based content in response to prompts provided to them.

To help make sense of AI and the vast array of systems in existence today, these systems are usually categorised in two primary ways: by their capability (what the system can do) and by their functionality (how the system achieves its purpose), type -1 and type -2 respectively. Given the nature of this classification, an AI system and/or technology will be found in multiple categories.

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This diagram depicts the categorisation of Artificial Intelligence (AI) into two primary categories, based on capability and functionality.

Type 1- based on capabilities

Artificial Narrow Intelligence (ANI)

Systems designed and trained for specific tasks. They cannot autonomously perform taks outside of this narrow domaian. Otherwise known as Weak AI, this is the only type of AI that exists today.

Artificial General Intelligence (AGI)

Otherwise known as Strong AI, these AI systems are hypothetical and would possess human-like intelligence, enabling them to tackle new and unfamiliar tasks autonomously.

Artificial Super Intelligence (ASI)

A theorised future state where machines surpass human intelligence, excelling and outperforming across all domains and tasks – ranging from creatibity to problem solving, and everythig in between.

Type 2- based on Functionalities

Reactive machines

Systems operating on immediate inputs and predefined rules, lacking memory, or learning capabilities- they react to current stimuli.

Limited Memory

These systems learn from past experiences and the data they store to inform and improve future decision-making. Most AI systems of today fall within this category.

Theory of Mind

These systems will be able to better understand the things they are interacting with through an ability to discern the mental states of others, including beliefs, desires, and emotions.

Self-Awareness

A hypothetical AI possessing consciousness, subject experiences, and emotions- it understands its current state.

No AI system that exists today has managed to surpass Artificial Narrow Intelligence (ANI), where AI systems are designed for specific tasks. As such, the Generative AI tooling of today is not able to apply human-like critical thinking and, going further, is prone to errors – for example citing non-existent sources. While advancements continue with breakthroughs almost weekly, true Artificial General Intelligence (AGI) and beyond remain future milestones and the target of research institutes, big tech players, and governments.

Organisations must therefore focus on optimising their use of current AI technologies such as Machine Learning (ML), NLP and Deep Learning to name but a few, while positioning themselves best to take advantage of any future developments as they are released. Developments which are almost certain to materialise if the likes of Raymond Kurzweil (American computer scientist and author of ‘The Singularity Is Near’ and ‘The Singularity is Nearer’), Sam Altman (CEO of OpenAI), and Shane Legg (cofounder of DeepMind Technologies, now Google DeepMind) are to be believed.

If this all seems overwhelming, we’re here to help!

Preparing for AI: The Six Pillars of Readiness

How then do organisations prepare themselves?

At Sopra Steria Next, our AI Readiness Assessment evaluates an organisation's preparedness across six critical pillars: Strategy, Governance & Ethics, Infrastructure, Data, Expertise, and Culture.

Read more on the six pillars and find out how to get your organisation ready for AI here.