Getting to Grips with AI!

July 22, 2025

By

Charles

X

min read

Understanding the myriad of terms used to talk about AI can be challenging, but we're here to help!

Artificial intelligence (AI) is transforming the tech landscape, with companies worldwide integrating AI to drive innovation and efficiency. However, the field is rife with jargon and complex concepts, making it challenging to grasp for those not deeply embedded in tech.

At Yopla, our mission is to make business better by aligning people and technology. To help you navigate the AI landscape, we’ve compiled a comprehensive guide to some of the most common AI terms and concepts, explaining what they mean and why they matter.

What Exactly is AI?

Artificial intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence. This includes learning, reasoning, problem-solving, and understanding natural language.

AI is a broad field encompassing various technologies and methodologies, often used interchangeably with machine learning, deep learning, and neural networks.

“Artificial intelligence will have a more profound impact on humanity than fire, electricity and the internet.” - Sundar Pichai, CEO of Google

Key Terms in AI

Machine Learning

Machine learning (ML) is a subset of AI where algorithms are trained on data to make predictions or decisions without being explicitly programmed for the task. ML systems improve over time as they are exposed to more data.

Artificial General Intelligence (AGI)

AGI refers to a type of AI that possesses the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to a human. While current AI systems are specialised, AGI aims to be versatile and adaptable.

Companies like OpenAI are investing heavily in AGI, which holds great promise but also raises ethical and safety concerns.

Generative AI

Generative AI is a type of AI that can create new content, such as text, images, music, and code. Examples include OpenAI's GPT models and Google's Gemini.

These systems are trained on large datasets and can generate outputs based on the patterns they have learned.

Hallucinations

In AI, hallucinations refer to instances where generative models produce confident but incorrect or nonsensical answers. This happens because the models generate responses based on their training data, which might not cover every possible scenario accurately.

Bias

Bias in AI occurs when the training data or algorithms lead to unfair or discriminatory outcomes. For example, facial recognition systems have been shown to have higher error rates for certain demographic groups. Addressing bias is crucial to ensure AI systems are fair and equitable.

Understanding AI Models

AI Model

An AI model is a mathematical framework designed to solve specific problems or perform tasks by learning from data. Models can range from simple linear regressions to complex neural networks.

Large Language Models (LLMs)

LLMs, such as OpenAI’s GPT-4 and Anthropic’s Claude, are a type of AI model trained on extensive text data to understand and generate human language. They can perform tasks such as translation, summarisation, and question-answering.

Diffusion Models

Diffusion models are used to generate images from text prompts. They work by adding noise to images and then learning to reverse this process to create clear images. This technology is also applied to audio and video generation.

Foundation Models

Foundation models are large-scale AI models trained on diverse datasets, making them versatile for various applications. Examples include OpenAI’s GPT, Google’s Gemini, Meta’s Llama, and Anthropic’s Claude. These models can handle multiple data types and tasks without requiring task-specific training.

Frontier Models

Frontier models are the next generation of AI models under development. These models promise to be more powerful and capable than current models, potentially transforming industries and posing new challenges.

Training AI Models

“We need to be careful about the data we use to train AI systems. If the data is biased, the AI will be biased.” - Joy Buolamwini, Founder of the Algorithmic Justice League

Training AI models involves teaching them to recognise patterns and make predictions by processing large datasets. This requires significant computational resources and advanced hardware such as GPUs.

The process includes the following components:

Training Data

The data used to train AI models, which can include text, images, audio, and video. The quality and diversity of training data are crucial for the model's performance.

Parameters

Parameters are the variables within an AI model that are adjusted during training to improve accuracy. The number of parameters can indicate the model's complexity and capacity.

Natural Language Processing (NLP)

NLP is a field of AI focused on enabling machines to understand and generate human language. Applications include chatbots, voice assistants, and language translation tools.

Inference

Inference is the process of using a trained AI model to make predictions or generate outputs. This is what happens when you interact with AI applications like chatbots or image generators.

Tokens

Tokens are units of text (words, subwords, or characters) that AI models process. The model's ability to handle more tokens can improve its performance in understanding and generating text.

Neural Networks

Neural networks are a type of AI architecture inspired by the human brain, consisting of interconnected nodes (neurons) that process data. They are fundamental to many AI systems, especially in deep learning.

Transformer

Transformers are a neural network architecture that has revolutionised NLP by enabling models to handle long-range dependencies in text. They use an attention mechanism to process sequences of data efficiently.

Retrieval-Augmented Generation (RAG)

RAG combines the generation capabilities of AI models with external data retrieval to improve accuracy. It allows models to access information beyond their training data, reducing hallucinations and enhancing reliability.

AI Hardware

AI systems require robust hardware to process large datasets and perform complex computations. Key components include:

Nvidia’s H100 Chip

A leading GPU for AI training, known for its efficiency and performance in handling AI workloads.

Neural Processing Units (NPUs)

Specialised processors designed for AI tasks, providing faster and more efficient performance than general-purpose CPUs or GPUs.

TOPS (Trillion Operations Per Second)

A measure of a chip's capability in executing AI operations, often used to highlight the performance of AI hardware.

Leading AI Companies and Tools

Several companies are at the forefront of AI development, each contributing unique tools and innovations:

OpenAI / ChatGPT

Known for its popular AI chatbot, ChatGPT, which has brought generative AI into the mainstream.

Microsoft / Copilot

Microsoft integrates AI into its products through Copilot, which is in some applications an extension of OpenAI's GPT foundations.

Google / Gemini

Google’s AI models power various products, from search enhancements to smart assistants.

Meta / Llama

Meta’s open-source AI model, Llama, aims to democratise AI research and development.

Apple / Apple Intelligence

Apple incorporates AI features into its ecosystem under Apple Intelligence, enhancing user interactions with devices.

Anthropic / Claude

Founded by former OpenAI employees, Anthropic focuses on creating AI models with a strong emphasis on safety and ethics.

xAI / Grok

Elon Musk’s AI venture, Grok, aims to push the boundaries of AI capabilities.

Hugging Face

A platform that provides a directory of AI models and datasets, fostering collaboration in the AI community.

How Yopla Can Help

Understanding the multitude of terms and concepts in the field of AI can feel overwhelming. The rapid pace of technological advancement means new terminologies and methodologies are always emerging, making it hard for anyone not deeply involved in tech to keep up. From machine learning and neural networks to generative AI and large language models, the sheer volume of information can be daunting! Plus, the subtle differences between similar concepts and the details of how these technologies are used in real-world scenarios can add to the confusion.

At Yopla, we get it! That's why we're here to help you make sense of it all.

Our mission is to make AI accessible and understandable for businesses of all sizes. We're dedicated to providing clear, practical insights that help you navigate the AI landscape with confidence. We’ll work closely with you to find the AI solutions that best meet your organisation's unique needs, making sure you can leverage AI to drive innovation and efficiency.

With Yopla by your side, you'll have a trusted partner to simplify the complexity of AI and deliver real benefits for your business.

For more information, contact us today on team@yopla.co.uk, or book a meeting now.

That Gut Feeling? It’s Probably Right. Let’s Talk.

Still thinking about what you just read? That’s usually a sign.

So don’t sit on it. Book a quick chat - no pressure.

We’ll help you make sense of the friction, share something genuinely useful, and maybe even turn that spark into real momentum.

No jargon. No pitch. Just clarity - and the next right move.

Related Posts

Capability

X

Min read

Mystery Steps and Email Misfires: How Hidden Workflow Chaos Eats Growth

Expose hidden workflow missteps, duplicate tasks and email ping pong draining productivity, how Yopla map processes to turn chaos into faster lasting UK growth.

Productivity

Insights

Mystery Steps and Email Misfires: Why Your Workflows Aren’t What You Think

Every business leader likes to believe they know exactly how work gets done in their organisation. You might assume all teams are following the same script, from sales and marketing to customer service. Spoiler alert: the reality is often quite different. The simple workflows you started with may have taken on a life of their own. Let’s explore why clearly defined workflows and consistency matter, and why the way you think work gets done might be a far cry from what’s actually happening.

The Great Workflow Assumption

On paper, you probably have processes for everything. Maybe there’s a standard onboarding checklist for new hires, a documented procedure for handling customer queries, or a flowchart for how a sale moves from lead to invoice. It’s comforting to assume everyone follows these processes to the letter. This is the Great Workflow Assumption … the belief that work in your organisation happens exactly as it’s supposed to.

In reality, people are people. Teams under pressure find shortcuts. New employees learn from Sandra down the hall that “actually, we do it this way.” Over time, what’s written in the handbook and what happens on Monday morning can drift apart. It’s nobody’s evil plan, it just happens. Everyone assumes the process is clear, but each person might have their own version of how it’s done. It’s like a company-wide game of whispers: the message (or process) changes a bit with each handoff.

Now, at a small scale, these differences might not be obvious. In a tight-knit 10-person team, you can possibly get away with informal understanding. But when you grow to 25, 50, 100, or more, those little deviations add up. The CEO still thinks “We have a smooth process for X,” while on the ground floor, X is being done five different ways (only one of which matches that neat diagram in the SOP binder). This isn’t about blame, it’s about visibility. You can’t fix what you don’t see, and assumptions can act like blinders.

When Workflows Go Rogue

So what does it look like when the ideal process in your head doesn’t match the messy reality? Here are some relatable examples of workflows going rogue in the wild:

  • Duplicate Efforts: Two departments unknowingly enter the same data into two separate systems. Finance painstakingly updates a spreadsheet of sales numbers, not realising Sales has been updating the CRM with the exact same data. Double the work, zero extra value.
  • Email Ping-Pong: A customer inquiry email gets forwarded to everyone and their dog because no one’s sure who owns the next step. The thread bounces around for days. By the time someone responds, the customer has given up, or worse, you have two people responding and contradicting each other.
  • Mystery Steps: There’s an extra step in a process that everyone follows even though no one remembers why. (“We always wait for the Tuesday report before approving this, it’s just how it’s done.”) If you ask who produces that report and what it’s for, you get blank stares. It might as well be magic.
  • Lost in Translation: Marketing hands off a task to Operations, assuming it’ll be done in a day. Operations schedules it for next week because, unbeknownst to Marketing, there’s an approval queue. Both teams are assuming the other knows this, and both are puzzled when nothing happens.
  • The Silo Special: Each department has its own version of the process. The sales team thinks Legal is handling the contract after a deal closes. Legal thinks Sales is. Meanwhile, the customer is left waiting because of a classic miscommunication. Oops.

Sound familiar? These little workflow mishaps happen everywhere. Individually, they might just cause a laugh or a minor irritation (“Oh, Alex already did that? Whoops!”). But collectively, they point to a deeper issue: fragmented, inconsistent processes. In other words, what you think is a well-oiled machine might actually be a bunch of mismatched cogs spinning in different directions.

When workflows go off-script like this, each extra handoff or unclear step is a chance for something to go wrong, an error, a delay, or just wasted effort. It’s like a relay race where runners keep dropping the baton because no one quite knows where the next hand-off point is. Entertaining to watch, perhaps, but not great if you’re trying to win the race (or run a successful business!).

The Real Cost of Chaos

It’s easy to shrug off a bit of process chaos as the price of doing business. However, those duplicate tasks, email misfires and mystery steps have real consequences. Small inefficiencies multiply over time, slowing your teams down and creating confusion that can spread across the organisation.

Think about the minutes (or hours) lost each day to chasing information, clarifying misunderstandings, or doing something twice. It all adds up. In fact, knowledge workers admit to wasting over 5 hours a week just waiting for colleagues to provide info, or recreating work because they couldn’t get it. One estimate even pegs 20–30% of a business’s revenue is lost to these kinds of inefficient processes. That’s right … potentially a third of your organisation’s output effectively vanishes due to workflow hiccups. Ouch.

Beyond the hard numbers, there’s the human cost. Consistently messy processes drive employees up the wall. Talented people don’t enjoy wading through unnecessary admin or firefighting basic communication mix-ups. Morale can take a hit when every day is an obstacle course of avoidable hassles. New hires get confused because the “official” process they learned doesn’t match what people actually do. Teams start to get a bit cynical: “This is just how things are around here … chaotic.” It’s not exactly the culture you dreamed of, is it?

And then there’s scaling. Trying to scale a business on top of inconsistent processes is like trying to build a tower on jelly. If your way of working is ad-hoc or dependent on who remembers what, adding more people or more customers can turn cracks into chasms. You might manage with 50 employees muddling through, but at 150, that approach could buckle. Inconsistent service delivery leads to customer complaints. Inability to delegate (because processes only live in one veteran employee’s brain) means you hit a growth ceiling. In short, operational inconsistency is the enemy of scaling confidently.

Mapping: A Reality Check for Your Workflows

So, how do you go from assuming to actually knowing how work gets done in your business? The answer is to shine a light on those hidden, fragmented processes. This is where mapping comes in. Mapping means taking an outside, objective look at your operations, following the trail of tasks and handoffs across teams to see the real picture.

Think of it as a workflow reality check. An outside party (like our team at Yopla) comes in and essentially acts like a business process detective. We interview team members, observe how tasks move from one person to the next, and dig up those “unwritten rules” and workarounds everyone’s been relying on. The goal is to visualise what’s actually happening day to day. That might mean drawing a literal map (diagram) of a process: from the moment a customer raises a hand, to the point they get what they need, who touches the work and when, what tools are used, where information flows (or doesn’t).

The findings can be enlightening. Often, leadership expects to see a nice, straight line of steps A → B → C. Instead, the map comes back looking more like spaghetti: A → B → X → C → B again → D → ??? → Z. But here’s the thing, this isn’t about embarrassing anyone or highlighting faults. It’s about clarity. By getting everything out in the open, you can have those “Ah-ha!” moments: “So THAT’s why the onboarding process always takes forever!” or “No wonder we keep replying twice to the same customer, look at where the communication broke down.”

Mapping gives you a factual, shared view of your operations. It replaces assumption with evidence. Instead of guessing where things might be slowing down or who is doing duplicate work, you have it laid out in front of you. It’s the first step to fixing the issues because you can’t improve what you don’t understand. As the saying goes, “If you want to get somewhere, you need a map.” In this case, you need a map of your own business – warts and all.

From Chaos to Consistency: What You Gain

Uncovering the real workflows in your organisation isn’t just an academic exercise, it’s the starting point for tangible improvements. Once you see the inefficiencies and gaps, you can start closing them. Here are some big wins that come from mapping your processes and tidying them up:

Workflow Win Deep-dive Resource
Spot inefficiencies and duplicate effort The Hidden Power of Understanding Workflows
Measure and track process ROI How to Measure Digital Transformation
Align teams on one version of the truth Meet Your New Worst Enemy – Digital Sprawl
Scale confidently without chaos The Return on Investment from Digital Transformation
  • Spot inefficiencies: Immediately identify bottlenecks, redundant steps, or tasks being done manually that could be streamlined. (Why are we entering that data twice? Let’s fix that!)
  • Improve consistency: Get everyone on the same page with best practices. When every team follows a unified process, you deliver a more reliable experience both internally and to customers. No more five versions of the “right” way floating around.
  • Automate repetitive tasks: Find the tasks that make your team members internally sigh “not this again” and see if technology can take over. Automation is a lot easier once you’ve mapped out what’s happening. Free your folks from copy-pasting mania and let them focus on more valuable work.
  • Build better customer experiences: When your internal house is in order, it shows on the outside. Fewer balls get dropped. Customers get quicker, more accurate responses because your team isn’t scrambling behind the scenes. A smooth backend process means a smoother front-end experience.
  • Scale more confidently: Perhaps most importantly, you gain a solid foundation to grow on. Clear, documented workflows mean you can onboard new staff faster (they can actually read how things work), delegate tasks without worry, and handle higher volumes without things breaking. It’s like turning a rickety footpath into a paved road – much easier to add more traffic.

By mapping and then improving your processes, you turn the chaos into consistency. Teams know what to expect from each other. Work moves faster and with less fuss. You create a culture that values clarity and continuous improvement, rather than one that shrugs and says “that’s just how we do things.” It’s not about making everything rigid, it’s about creating smart guidelines so that everyone can do their best work without tripping over hidden obstacles.

A Friendly Nudge

If you’ve been reading this with a growing sense of “Oh dear, this is us,” don’t worry, you’re definitely not alone, and it’s never too late to straighten things out. The first step is simply recognising the gap between assumed workflows and real ones. The next step? Consider bringing in a fresh perspective to help map out the madness. Sometimes an outside pair of eyes can spot things insiders overlook.

This is exactly what we love doing at Yopla … acting as that friendly detective to help untangle your processes and get your organisation running like the well-oiled machine you thought you already had. No hard sell here, just a genuine offer – if any of the above rings true and you’re curious about uncovering what’s really going on under the hood, we’re here to chat. After all, every great journey starts with a good map, and we’d be delighted to help you draw yours. Here’s to smoother workflows and confident scaling!

Capability

X

Min read

Email Tracking. What it is, and How to Prevent it.

At Yopla we use email tracking across our non-confidential correspondence for a variety of reasons, but it's important to understand exactly what this means, and how to turn it off, if you want to. Read on to find out more.

Cyber Security

Digital Transformation

At Yopla we use email tracking across our non-confidential correspondence to measure effectiveness, ensure we are delivering on our commitments, update our clients with information that's relevant to them and drive automations that give us back free time. But, we also recognise that it can pose privacy and security risks for all email recipients, particularly those who may not want to be tracked without their knowledge or consent. This is a situation where our outward technology, risks becoming your inward technology (check out the blog article on inward vs. outward tech here).

So, in this article we'll look at how email tracking works, how to tell if an email has a tracking pixel, and how, if you choose, to prevent email tracking on your device.

A 2023 Zippia report states that in 2023, businesses and consumers sent and received approximately 347.3 billion emails per day worldwide. This is projected to reach over 376 billion by 2025.

How Email Tracking Works

Email tracking is a common practice that involves embedding a tiny image, called a pixel, into an email message. When the recipient opens the email, the pixel sends back information to the sender, such as when and how many times the email was opened, what device and email provider were used, and even the approximate location of the recipient.

The tracking pixel is a 1x1 pixel image (for comparison, that's about the size of a pinhead) which is inserted into the header, footer or body of an email message. It's usually transparent or matches the colour of the background, so won't be visible to the naked eye. The pixel is linked to a server that records when the image is requested; this is usually when the recipient opens the email.

Email tracking pixels can collect a wide variety of information, for example:

  • How many times the email was opened
  • What device or devices were used
  • What email provider was used
  • What region or city the recipient is located in
  • Whether the recipient clicked on any links in the email
Two-thirds of emails sent to personal accounts contained a tracking pixel, according to a review by Hey.

Email tracking pixels can also power remarketing, which allows for personalised ads to be shown to people based on their (in this instance) email activity.

If this sounds familiar, it is ... cookies do a very similar job but are small files stored on your browser when you visit a website enabling companies to track your browsing history across multiple websites ... pixels can only track your email activity within a specific email message.

Facebook do something similar with the Like button, Google across the websites that use their powerful website analytics tools and both Microsoft and Google across their web browsers. Amazon tracks users through its extensive use of cookies and personalised recommendation and bricks and mortar retailers through card use, loyalty cards and more.

32% of respondents agreed that they always accepted all cookies when prompted on visiting a website. The rate was highest among respondents aged 25 to 34 and lowest among the age group 45 to 54

How to Tell If an Email Has a Tracking Pixel

There are a couple of easy ways to tell if an email has a tracking pixel:

  • Use an email service or app that alerts you to the presence of tracking pixels, such as Hey or Mailbird.
  • Inspect the source code of the email message and look for any image tags that have a 1x1 size or a suspicious URL.

How to Prevent Email Tracking

If you want to prevent email tracking on your device, there are a few options:

  • Use an email service or app that blocks or removes tracking pixels automatically, such as Hey or Mailbird. These services or apps will also show you which emails have tracking pixels and what information they are trying to collect.
  • Use a browser extension or plugin that blocks or removes tracking pixels, such as Ugly Email or PixelBlock. These extensions or plugins will also let you see which emails contain tracking pixels and the info they're grabbing.
  • Use a VPN (virtual private network) service that masks your IP address and location. This will prevent the pixel from identifying your approximate location based on your IP address. However, this won't prevent the pixel from collecting other information, such as when and how many times you opened the email.

Conclusion

Email tracking is a widespread practice that helps marketers and salespeople measure and improve their campaigns and can improve customer engagement and experience. However, understanding how these technologies work, and putting yourself in control, is crucial to ensuring that they aren't infringing on your privacy and comfort.

If you would like to find out more about how email tracking works, the good, the bad and the ugly, please do get in touch!

Digital Transformation Strategies That Actually Work

Capability

X

Min read

Digital Transformation Strategies That Actually Work

Discover proven digital transformation strategies that put people first, improve efficiency, and build lasting capability within your organisation.

Digital Transformation

Insights

Proper digital transformation strategies have nothing to do with buying the latest software. They are about fundamentally changing how your organisation works, how it creates value, and how it serves both your people and your customers. It’s a complete rewiring of your business from the inside out.

What Are Digital Transformation Strategies Really About?

Most leaders we talk to are sick of the industry noise around digital transformation. They're constantly being sold complicated platforms and vague "digital journeys" that promise the world but deliver little more than headaches. The problem is that most of these so-called strategies begin with a technology shopping list, not with a solid grasp of the human and process challenges that need solving.

At Yopla, we see this completely differently. Real transformation starts with people. It's about slicing through the operational fog that bogs your teams down and bringing clarity to the decisions that matter. A genuine strategy doesn't just chase new tech; it aligns your organisation around shared goals, using technology as a precision tool to enable your people.

Moving beyond the buzzwords.

The phrase "digital transformation" has been thrown around so much that it has become almost meaningless. For any strategy to have a real impact, it has to be rooted in tangible, measurable outcomes. So, instead of focusing on implementing some big new system, a better approach is to zero in on fixing a specific, frustrating process.

Does your sales team waste hours manually pulling together reports instead of talking to clients? Do your finance and operations teams argue over conflicting data from a mess of different spreadsheets? These are not technology problems at their core. They are process and communication problems.

A solid strategy tackles these issues by asking some tough questions first:

  • Where is time being haemorrhaged in our current workflows?.
  • What information do our teams need to make faster, smarter decisions?.
  • How can we establish a single source of truth that everyone in the business trusts?.

By getting honest answers, you start to build a strategy that delivers a real dividend in free time and sharper operational focus. You can dig deeper by exploring what digital transformation actually is and why this distinction is critical.

A foundation for lasting change.

Putting people first is essential because it helps you avoid costly mistakes. When you invest in a new platform before you've streamlined the process it's meant to support, you risk just automating the existing chaos. You end up with a bad process that just runs faster, but it isn't any better. This is a classic pitfall that leads to failed projects, wasted money, and burnt-out teams.

The most common mistake is focusing on technology before understanding the people and process problems. A successful strategy starts by identifying operational friction and co-creating solutions with your team.

Our view is that a successful strategy must build a foundation for change that lasts. It should empower your team with the tools and insights they need to own their processes long after any consultants have gone. This is the only path to what we call digital sovereignty—building the capability inside your own organisation so that you control your future. This is how you create a business that is more open, more capable, and operationally resilient.