Key AI Concepts

Even with fairly simple applications (think video conferencing apps or office entry systems, for instance), software vendors often describe their offerings as “powered by AI”. It sounds impressive. But what exactly does it mean?

AI is not a technology in itself. It’s basically an umbrella term, described by McKinsey as, “the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, and even exercising creativity”.

A wide range of technologies and principles underpin AI. These are some of the most important ones to be aware of:


The terms, AI and automation are sometimes used interchangeably. It’s certainly the case that a lot of AI-based applications are designed to automate manual tasks. However, not all automation relies on AI.

Take a corporate accounting package, for example. Let’s say you can programme it to follow certain predetermined instructions – e.g. to extract a narrow range of data points to make certain calculations and populate a report. This is automation – but it isn’t necessarily AI.

Contrast this with more advanced office of finance functionality. With this, the software has the ability to make predictions and decisions based on the data it is presented with. The solution isn’t just following narrow, pre-defined rules; it’s making correlations and uncovering insights that are often impossible to pick up on through human intervention alone.

Machine Learning

Machine learning is the key concept that makes the difference between automation and actual AI. With this, the machine isn’t just reliant on explicit programming instruction. Rather, it relies on algorithms to make predictions and recommendations by processing data. The more data the machine processes, the more accurate it becomes.

Particularly relevant in areas such as performance management and sales forecasting, machine learning takes you beyond descriptive analytics (describing what just happened), and opens up the possibility of predictive and prescriptive analysis: anticipating what will happen next, and providing recommendations on what to do.

Deep Learning

One step up from machine learning, deep learning gives you the potential to process a much wider range of data sources for more complex use cases. It is based on multiple, interconnected layers of calculators known as neurons, forming a neural network. Vast amounts of data can be fed into the machine, and processed through those layers. The network can then make a determination about the data, learn if this determination is correct, and then use this knowledge to inform future determinations. Use cases include image classification (e.g. visual diagnostics), facial and voice recognition.

Big Data

Think about all of the data generated across your sales and customer service channels, operations, finance, and other key areas of your business. Much of this falls into the category of Big Data: i.e. large, diverse sets of data generated through the usage of devices, software, and networks.

With AI, the more data you feed into an application, the more accurate the solution becomes. As such, the success of any AI initiative is always going to be intrinsically linked to your ability to harness and process the right data, at the right time. Millennium Consulting’s Big Data White Paper explores this in more detail.

Generative AI

Generative AI – i.e. the ability of a machine to generate new output based on training data – isn’t new. However, when ChatGPT’s latest model, GPT-4 arrived last year, it was instantly recognised as a big deal. Previous generative AI models could produce serviceable – if not always very impressive – output. GPT-4 stepped things up several levels.

Like its immediate predecessors, the solution is trained through a neural network utilising massive amounts of data, combined with powerful natural language processing (NLP) capabilities. However, the latest version is much more effective at completing tasks that demand advanced reasoning or creativity, including the ability to understand and respond to nuances of conversation. It can also comprehend user intention more accurately, it allows users to adjust tone and style depending on needs, and is also a lot better at avoiding ‘hallucinations’ and nonsensical output.

In terms of practical application, generative AI is no longer confined to providing basic responses to simple requests. It can troubleshoot, advise, diagnose, solve complex problems – and even come up with new ideas: potentially disrupting a vast range of not just routine activities, but also senior and specialist job roles.

Use Cases

How can we inject extra intelligence into business processes to boost efficiency and effectiveness? What is the potential of AI for aligning our offerings more closely to customer needs and expectations?

Whether and to what extent you adopt a particular technology (including AI-based technologies) should always be dependent on what it is your business is trying to achieve. In general terms, however, possible use cases to explore include the following:

Customer experience

  • Personalised offerings. Use AI tools to analyse customer data, identify individual preferences to deliver tailored recommendations and offers. Combine this with generative AI for the rapid creation of highly personalised marketing material.
  • Proactive service. 73% percent of customers report a more positive perception of brands if they can anticipate minor issues before they become more significant. AI-based tools that can monitor software or device usage makes it much easier to identify errors/service issues and resolve them in a timely manner.
  • Quality assurance. Human QA analysts can only listen, on average, to less than one percent of all customer service interactions. AI-based systems have the potential to appraise 100% of all interactions across all channels. Alongside this, ‘virtual assistants’ offer a much quicker and cost-effective way to resolve customer queries.
  • Data collection. 68% of businesses say that the customer experience data generated within their organisations is not currently being utilised effectively. Look carefully at your data management and processing to consider how this might be optimised.


  • AI-powered solutions give you the ability to drill into report lines, spot trends, make predictions, and deliver insight to the wider business.
  • Performance monitoring. AI analytics solutions typically have a strong self-service element. This makes it possible for finance team members and business users to put financial and operational data to work, ask questions and generate scenarios without the need for specialist technical input.
  • Routine disclosures and internal reports can often take weeks of preparation. Generative AI solutions could potentially shave large amounts of time from the drafting, verification and approvals process.

Operations management

  • Predictive maintenance. These solutions can analyse performance at machine-level, anticipate failure/maintenance requirements before they arise, thereby minimising downtime and extending equipment lifespan.
  • Inventory management. Predictive analysis delivers a better understanding of purchasing patterns and future demand. Most likely, this analysis is owned by your finance and sales teams. However, if you can feed these insights through to operations, it ensures that stock levels and distribution strategies are more closely aligned to actual demand.
  • Supply chain management. Generative AI can give you the ability to process very large data sets, enabling you to identify performance weak points and risks across a complex supply chain ecosystem.

Potential pitfalls of AI

While AI certainly has the potential to create extra value, there are also risks to consider; particularly around the areas of public perception, employee anxiety, and data security.

90% of customers rate ‘instant customer service’ as important. And more than half of consumers say that slow response times would be enough to cause them to switch brands. In an ideal world, customers should be able to access precisely the support or information they require, right at the point of need. Advanced chatbots and other AI-based initiatives can go a long way in answering these needs.

However, there is a potential flipside to AI when interacting with customers. According to a recent survey from XM Institute, about half of consumers express a general concern about the ‘lack of a human being to connect to’ with automated interactions.

Dig a little further into this, and it’s clear that levels of concern vary depending on the complexity of the issue in hand. For instance, most customers are fine with AI for simple queries (e.g. making a routine travel booking, checking order status or minor support issues). If it’s advice on, for instance, a medical problem or resolving a billing issue, levels of comfort reduce considerably.

The most recent Chat-GPT release prompted reports that 300 million jobs could be affected by the latest wave of AI. And unlike previous waves of automation, generative AI places mid and even senior-level white collar under the spotlight.

More than three quarters of people say they are very or somewhat worried about human job loss linked to artificial intelligence. Significant AI initiatives are bound to raise employee concerns. As such, success depends on ‘selling’ the vision, on inviting input, and on highlighting the fact that AI-driven automation provides greater opportunity for individuals to focus on value-added work – thereby potentially enhancing rather than eliminating their role. Our finance transformation guide considers some of these themes in more detail.

On one level, AI is good news for anyone tasked with protecting a business from cyber attacks. Not least, it offers greater potential for scanning vast amounts of traffic for hidden threats. And by utilising deep learning techniques, businesses can enhance their ability to identify deviations from the norm that may indicate a breach.

But hackers have caught onto the potential of AI, too. For instance, we’ve seen recent reports of how threat actors are using generative AI to compose highly-specific and very convincing phishing campaigns. You can ask generative AI solutions to write code for you. So if threat actors are able to train models based on high-quality exploit data, there’s a very real potential for them to generate malware capable of evading detection by current security filters.

What next?

Across the last three decades, Millennium Consulting has established a reputation for enabling companies to solve their most pressing business problems with a range of expertise that spans next-gen data analytics, data architecture, cybersecurity, automation, AI and more.

For ‘joined up’ advice on your next big AI initiative, speak to us today.

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