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Could AI Transform Your Business?

Artificial Intelligence (AI) is a rapidly growing and evolving field. In recent years, we’ve seen generative AI models such as ChatGPT and Midjourney develop from being the stuff of science fiction to becoming tools that everyone, from office workers to college students, uses as a part of their day-to-day work.

The AI industry is expected to have a value of more than $500 billion by 2024, and businesses that aren’t using AI in some capacity are likely missing out on some significant competitive benefits.

Whatever industry you’re in, AI has the potential to transform your business by:

  • Automating repetitive operations: AI can handle mundane customer service queries, generate reports and review log files, freeing human staff members to focus on more nuanced or complex tasks.
  • Facilitate informed decision making: Processing vast amounts of data is difficult for humans, but AI tools can help us make sense of data quickly, identifying patterns or anomalies and highlighting them to help humans make better decisions.
  • Improving the customer experience: AI tools can learn a customer’s preferences and help a business provide tailored recommendations to each customer. This allows businesses to provide a personalized service to all of their clients, no matter how many they have.
  • Increasing scalability: If a business has identified workloads that can be handled by AI, automating those workloads can allow the business to scale more quickly than would otherwise be possible.


The flexibility and power of AI make it an invaluable tool for any business. The AI industry is still in its infancy, and we’ve only just started to explore the use cases of this technology across various industries.

As machine learning and generative AI models improve and the industry matures, businesses that do not embrace these technologies are at risk of seeing themselves left behind.

At, we provide a range of AI consulting services to help businesses of all sizes integrate AI into their workflows. We use the INTELLIGENCE methodology to help clients understand the benefits and use cases of AI, the challenges organizations face when implementing AI solutions, and the ethical considerations that should be considered when using artificial intelligence.

We’ll also outline how to introduce AI into your business efficiently so the transition from legacy workflows to AI is as smooth as possible.

Introducing the INTELLIGENCE Methodology

At, we offer various services to help businesses take full advantage of artificial intelligence and machine learning. Our team of AI experts is here to advise you on how to unlock the transformative power of AI for your organization.

We provide AI solutions carefully tailored to your business, whatever stage of AI adoption you’re currently at. From strategy development to implementation and consulting, our team can assist you on your artificial intelligence journey.

The INTELLIGENCE methodology covers each aspect of AI, highlighting the factors your organization should consider as it upgrades its systems and workflows for an AI-powered world. It covers:

  • Infrastructure and Integration
  • Novel Algorithms and Neural Networks
  • Technological Foundations of AI
  • Economic Impacts
  • Legal and Ethical Frameworks
  • Limitations and Challenges
  • Industry Applications
  • Global Trends
  • Ecosystems
  • Navigating Transformation and Your Digital Strategy
  • Customer Experience
  • Evolution and Future Trends

In this ebook, we’ll address each of these in turn, arming your organization with the knowledge you need to take advantage of the full power of AI.

I is for Infrastructure and Integration

Organizations such as OpenAI have brought AI to the masses. Still, if your organization wants to take advantage of machine learning by using models trained in-house, you’ll need an AI infrastructure capable of processing huge volumes of data.

The hardware requirements for even modest deep-learning models are significant. It’s not uncommon for these models to require 40+ GB RAM and tens of gigabytes of GPU memory just to get started with a small generative model. Using such models at scale would require even more memory and computing power. Rather than running such hardware on-premises, many organizations use cloud platforms instead.

The Advantages of Using Cloud Infrastructure for AI Models

Cloud platforms offer on-demand access to computing power, with flexible pricing models that mean you pay only for the resources that you use. This can be invaluable for organizations that don’t want to pay the up-front cost to purchase the hardware required to run AI models. It’s also useful for businesses with unpredictable or occasional needs. Usage-based pricing tends to be quite economical, as cloud platforms benefit from the economies of scale, allowing them to rent out their hardware for relatively low prices.

In addition to these pricing benefits, there are practical benefits associated with using cloud infrastructure. When you use a cloud platform, the cloud services provider is responsible for the infrastructure, so your IT team isn’t responsible for managing the hardware. You don’t have to worry about hardware upgrades, network outages, or other similar issues; all you’re responsible for is the software running on the resources you’re renting.

Some cloud providers offer AI as Service solutions to access pre-configured models, saving you time and allowing you to get up and running with AI quickly and easily.

Using APIs to Access the Power of AI

One common challenge when transitioning from legacy workflows to AI-empowered ones is getting the data you need into the AI model and automating the workflow itself.

Many leading AI providers now offer APIs to help developers add AI functionality to their applications, whether creating chatbots, processing data, or aiding decision making. Some leading AI APIs include:

  • Amazon AI Services / Amazon SageMaker: Build and train machine learning models and take advantage of object recognition, image and video analysis, and natural language processing AIs.
  • Google Cloud AI: Ready-to-use models for natural language tasks, transcription, video analysis, and easy integration with other Google Cloud services.
  • IBM Watson: A multi-purpose AI capable of transcription, text-to-speech conversion, sentiment analysis and natural language processing tasks.
  • Microsoft Azure Cognitive Services: Another multi-purpose AI that also offers easy integration with Microsoft products such as Power BI.
  • OpenAI: Access ChatGPT functionality via its API to easily integrate it into your applications.

The above is just a handful of the APIs available to developers. If you don’t want to run your own models but want to be able to use AI processing on a per-request basis, APIs offer a cost-effective way to test AI functionality. Many AI providers offer pay-as-you-use pricing models, so there’s little risk to testing AI features, and even small organizations can access the same powerful machine learning models that big businesses are using.

Before you go live with any API integration, test it carefully to ensure that it’s accurate and that the AI model has been adequately trained for your use case. In addition, if you’re offering AI in a public-facing setting where a user can send free-form prompts, test it carefully for security to ensure the AI won’t accidentally leak sensitive data.

N is for Novel Algorithms and Neural Networks

When the hype surrounding ChatGPT first surfaced, many detractors quickly dismissed the system as little more than a very sophisticated form of Markov chain generator. This is an oversimplification of the technology, but the basic concept is true. ChatGPT essentially works by classifying data sets and writing responses by looking at the probability of what word would come next.

Of course, in the explanation above, the phrase “classifying data sets” covers a huge amount of sophisticated computing work. Machine learning algorithms are incredibly complicated, and we’re seeing many novel algorithms and neural networks being developed to solve interesting problems in various industries.

Exploring New Machine Learning Methods

Traditional machine learning can be divided into two categories — supervised learning, where a human feeds carefully tagged and classified data into an algorithm and reviews answers provided by the algorithm to ensure it’s “learning” correctly, and unsupervised learning. In unsupervised learning, the computer sorts data into groups or patterns without training, with the results being reviewed later.

New machine learning algorithms take a different approach. For example, semi-supervised learning uses small data sets provided by supervised learning combined with larger sets of unlabeled data. This semi-supervised training approach often allows for faster training while simultaneously providing better results.

Another approach is transfer learning, where a model that’s been trained in one task transfers the knowledge from that task to another or uses knowledge from the previous task to solve future, more difficult challenges. Transfer learning is a massive step forward compared to traditional models that could only solve the challenges they were trained to handle.

Real-world Applications of Novel Algorithms

Neural networks and novel algorithms are already helping scientists make significant progress in their research. One recent example of novel algorithms being employed in scientific fields is that of the Sparse Convolutional Neural Network (SCNN), which physicists are using to help them more quickly and accurately scan images looking for relevant data.

Physicists spend a lot of time examining sparse images, which could contain thousands of empty or non-relevant pixels. SCNN can automate the scanning of those images, reducing the time researchers have to spend examining them. It’s hoped that in the next few years, they’ll be able to feed images from the Large Hadron Collider into the neural network, speeding up the data analysis performed by researchers using the LHC by a factor of 50 or more.

T is for the Technological Foundations of AI

The term Artificial Intelligence was first mentioned in the 1950s to describe efforts by computer scientists to create machines that were capable of thinking or learning. The idea was quickly picked up by science fiction writers, who immediately began to explore the potential of such technologies, as well as ethical concerns. If a computer can think, does that mean it’s alive?

The pioneering efforts of scientists such as Alan Turing and John McCarthy sparked slow but steady progress in the development of AI. Early models were programmed with focused algorithms to perform specific tasks, such as solving a mathematical problem or playing a game of chess. Still, limitations in the computing power available at the time meant that those early researchers were unable to develop fully autonomous AI. As happens so often in the world of research, as the hype died down, so too did research funding, and we saw a long “AI winter.”

Today, we have computers with terabytes of storage and tens of gigabytes of RAM, performing at speeds the researchers of the 50s and 60s could only dream of. These advances have brought about a renaissance in AI.

As research into AI has intensified, we’ve seen significant advances in the technology and its key components. The foundations of modern AI are:

Machine Learning Algorithms

Machine learning algorithms are the code that helps machines learn to solve problems or perform tasks without being specifically programmed to handle those things. Rather than following static instructions, these algorithms learn from the data they’re fed and gradually optimize their responses based on that data.

Natural Language Processing

Natural language processing is a specific branch of AI that focuses on how humans and computers interact. Thanks to natural language processing, humans don’t have to learn syntax rules before they can instruct computers. Rather, they can address the computers with the same conversational language they’d use to interact with humans and still be understood.

Natural language processing isn’t just used to program computers. It’s also used for data analysis tasks such as sentiment analysis or when a generative AI model is asked to summarize a large block of text.

Neural Networks

Neural networks are a mathematical model that’s inspired by the human brain. These networks contain artificial neurons that are connected to facilitate complex pattern recognition. Novel neural networks can learn in more sophisticated ways than more traditional machine learning models. This has enabled significant breakthroughs in image and video recognition, natural language processing and other areas.

E is for the Economic Impact of AI

Artificial Intelligence has the potential to double annual global economic growth rates by 2035, according to research by Accenture. This growth is expected to come from three key areas:

  • An increase in labor productivity as AI and machine learning enable more efficient time management.
  • Intelligent automation creating a virtual workforce that can quickly and accurately solve common problems.
  • New revenue streams emerging as emerging technologies enable as-yet-unforeseen innovations.

Artificial intelligence may reduce demand for workers in some roles as routine tasks become increasingly automated. In the coming years, capital-intensive industries such as transport and manufacturing will likely see significant changes, with robots and autonomous vehicles becoming more commonplace.

Data processing and customer service jobs may also see major changes, with machine learning handling a lot of analytics tasks, and AI being employed to improve the customer experience.

PriceWaterhouseCoopers estimates that in North America and Europe, many workers are at risk of losing their jobs to automation by 2030. The percentage of jobs at high risk in those territories varies between industries but is calculated at between 23% and 76%. Other territories have a lower risk of automation. This is due to their economies being driven less by industries that are vulnerable to automation or, in the case of Japan, workers spending less time on manual tasks and more on management.

We’ve already seen the first of what is likely to be many massive shifts in terms of the employment landscape. According to the Challenger Report by Challenger, Gray & Christmas, Inc., 3,900 job losses occurred in May 2023 in the United States due to AI.

The report doesn’t specify which sectors the AI-related job losses were in or how the jobs became automated. However, these job losses likely came from the jobs that are easiest to automate with software and where it’s feasible and safe to move quickly with automation.

Advocates for AI point out the impressive power of tools such as GPT-4, which has shown itself capable of passing the bar exam. However, passing tests is different from performing a job where the questions and challenges being faced are less predictable. For now, human knowledge and the ability to understand context are still essential for many jobs.

Safety concerns, the cost of implementing more complex automation, and the availability of training data to teach machine learning models will slow the adoption of AI in some sectors, such as health care and public services. There are legal and ethical considerations to take into account when implementing AI in areas where there could be public safety issues. We’ll discuss these in the following chapter.

L is for Legal and Ethical Frameworks for Ethical AI

Job losses caused by ChatGPT and other AI systems have been likened to the Industrial Revolution, when many skilled laborers found their jobs were made obsolete due to machines. The original Luddites were skilled textile workers who destroyed machines, not because they were scared of technology, but because they were protesting against unscrupulous manufacturers who used machines fraudulently and deceitfully to save money and avoid the accepted labor practices of the time.

The Luddites wanted to see machines operated by skilled, well-paid workers who produced high-quality goods. Instead, machines were replacing those skilled workers and producing a poorer product.

Similar disputes are playing out all over the world today. Amazon warehouse workers are now working alongside robots, and many workers have complained about the conditions they’re forced to work in. One worker told CNBC, “Someone the other day said we’re treated like robots — no, robots are treated better.”

Job losses caused by the adoption of AI could be seen as an inevitable element of progress. After all, new jobs in the form of “prompt engineering” are being created. As AI takes over repetitive tasks, it could be argued that the technology helps businesses scale better, potentially opening up other skilled positions for human employees due to that growth. However, AI’s impact on employment isn’t the only thing that’s raising questions among those concerned about ethical AI use.

The Question of Bias in AI Models

AI models are typically trained on large volumes of data relating to the task they’re performing. If the data is in any way biased, or a specific demographic is under or over-represented in that data, this could (and has) lead to the model being biased.

As the use of AI for things like hiring, insurance and risk assessments becomes more commonplace, there’s the potential for biased AI to have a significant negative impact on people’s lives.

For example, an AI model could deny someone a bank loan based on their postcode or their ethnicity. A facial recognition system could erroneously highlight someone based on their ethnicity, or an AI used to perform initial screening on job applications may favor someone based on their sex.

Issues like those listed above are already being seen in the real world. Amnesty International recently reported on the way facial recognition algorithms frequently misidentify the faces of people of color, with these errors sometimes leading to wrongful arrests.

In addition, Amazon discontinued using an AI hiring tool after it was found the tool was biased towards men. The model showed a preference for CVs containing words such as “executed” or “claimed,” which men use far more often than women.

Developers and users of AI must be mindful of how training data can influence the model and be alert to any signs of bias. AI is a product of the world that created it, and if it’s shown the results of a biased system, it will likely copy that system. If your goal is to eliminate bias, start by paying close attention to the data that goes into the training model.

L is for Limitations and Challenges of AI Technologies

Machine learning and generative AI models have advanced significantly over the last couple of years. ChatGPT 4, Midjourney and other similar models can respond to natural language prompts with output that is, on the surface, quite creative and suggests an understanding of the question or instruction being given.

The ability of these models to do this is impressive and is a major contributing factor to the level of media hype that AI has seen. Longer-term and more extensive use of AI quickly reveals its limitations, however, including:

Limited Ability to Understand Context

Models such as ChatGPT that are trained on broad data sets have a limited understanding of the context of the information they’re being fed. This sometimes leads to the model providing an incorrect or unrelated answer to the question because the model picked up on certain keywords but didn’t understand the question.

This issue is less likely to occur with AI models trained on a limited dataset for one specific job, making AI ideally suited to tasks such as security automation, analytics and reporting.

Susceptible to Bias in The Training Model

The issue of bias is something we addressed in the previous chapter, but it’s important enough to repeat. Machine learning models simply reflect the data that are fed into them. Fortunately, it’s possible to train bias out of models by pre-processing the data or using other bias correction techniques.

NIST emphasizes that where biases are found in an AI model, it’s not always enough to simply go back to performing the previously automated process the old-fashioned way. All too often, the bias found is a symptom of systemic bias in the organization. With proper training, the AI model could help improve a previously biased process.

Limited By a Mathematical Paradox

Large language models such as ChatGPT can produce authoritative-sounding answers to almost any question. Because their language makes them sound confident, laypeople may assume the answers these models provide are correct.

Alan Turing and Kurt Godel identified a mathematical paradox that showed that it’s impossible to prove whether certain mathematical statements are true or false, and some computational problems can’t be solved with algorithms. If a mathematical system is rich enough to describe the arithmetic we use in our lives, that system can’t be used to prove its own consistency.

That paradox is still relevant today and highlights the limits of AI. AI models are vulnerable to incorrectness and not knowing that they’re incorrect.

This weakness is one we’re unlikely to be able to address in the next couple of generations of AI. However, we can work around that limitation by making AI more transparent and giving users clearer advice about the capabilities of the generative AI they’re working wit

I – Industry Applications of Artificial Intelligence

AI has the potential to revolutionize a variety of industries. From applications in customer service to autonomous vehicles, we’re likely to see AI taking on an increasing number of roles that humans traditionally performed.

Some common applications include:

Autonomous Robots

Robots are already being used in Amazon warehouses and Tesla assembly lines, as well as in some restaurants worldwide. AI-augmented robots have pathfinding and collision detection capabilities, as well as the ability to recognize objects and respond to basic commands, making them ideal for performing repetitive tasks.

Smart Devices

Home assistants and smart devices have a basic form of AI in them, and we’re starting to see similar devices being used in businesses, too. For example, factory machinery can be equipped with smart features that can raise an alert if a fault is detected or communicate with other devices in the factory to keep workflows moving at an optimal rate.

Autonomous Vehicles

From delivery drones to self-driving taxis, we’re gradually seeing a rollout of autonomous vehicles in some parts of the world. Even in areas where self-driving vehicles aren’t yet allowed, modern vehicles have an increasing number of AI-augmented features, such as lane detection or collision warnings.

Natural Language Bots and Customer Service

Many organizations are using chatbots to provide customer service. Chatbots can serve as an initial screening device to ensure customer queries are directed to the correct department. In addition, they can often handle basic queries themselves, such as taking payments or responding to questions about the location of a delivery.

A well-designed chatbot could cut the volume of customer service queries needing to be handled by a human significantly, reducing queuing times for those with more complex queries and increasing customer satisfaction.

AI as a Diagnostic Tool In Healthcare

The healthcare industry is being transformed by artificial intelligence. Some hospitals are using AI tools to assess patient risk, and these tools have helped cut unnecessary hospital visits, reducing wasted nursing hours and allowing healthcare professionals to focus on more urgent tasks.

AI is also useful for examining X-rays, tissue samples, and other diagnostic data, helping specialists arrive at more accurate diagnoses.

Financial Analysis

The data-heavy nature of the financial industry makes it an ideal match for AI models. Financial sector workers can use AI’s speed and accuracy to process large volumes of data. Models can assist with fraud detection, algorithmic trading, risk assessments and more.

Analytics and Modeling

AI models can help businesses predict seasonal demand and make better-informed decisions by rapidly analyzing past trends and simulating future outcomes. The travel and transportation industries, as well as the retail industry, are using AI extensively for this purpose, but the same principles apply to either industry, too.

Personalized Offers and Loyalty Programs

Retailers can use AI to improve their loyalty programs by providing personalized recommendations and special offers to consumers based on their spending habits. AI models can also identify shoppers who have changed their habits, such as visiting a store less frequently or visiting a different branch, and respond to those changes in a way that helps retain customer loyalty.

Personalized recommendations are particularly useful for online retailers. By offering better recommendations in the “people also bought” section of a store or as part of an abandoned cart offer, retailers can increase the average purchase per customer and reduce cart abandonment rates.

More Effective Online Advertising

Advertising companies are using AI to improve the placement of advertisements. For example, sentiment recognition can help ensure ads are only shown next to positive discussions, preventing a customer’s advertising budget from being wasted on displaying their ads to people who are critical of a product.

AI tools may also be able to better understand keyword context, reducing the risk of irrelevant advertisements being shown on discussion forums. While many AI models are not perfect when it comes to picking up on context, they’re usually better than the simple keyword filtering that came before them.

The above are just a few examples of how AI can be used in various industries. Modern generative AI models are powerful and flexible and can assist human workers in almost any job role.

G is for Global Trends in the AIaaS Industry

The AIaaS market is expected to grow by around $28,774 million between 2022 and 2027, driven by the increasing power of AI models and the widespread acceptance of cloud computing services across various industries.

Affordable, scalable cloud infrastructures are helping drive the AIaaS market worldwide. The flexible pay-as-you-use pricing makes cloud services accessible to businesses worldwide, regardless of their size and budget. However, the industry will likely see many changes over the next few years.

Increasing Mergers and Acquisitions

The next few years are likely to bring with them a number of mergers and acquisitions. Large organizations looking to embrace AI and cloud services are looking to acquire start-ups with the expertise these organizations need, allowing them to expand their reach and deliver improved AI services.

Companies entering AI range from banks and retail organizations to healthcare providers, all needing scalable, reliable AI solutions compliant with national and international regulations. Acquisition activity will help drive the growth of the AI industry.

According to a recent report by Technavio, we can expect significant growth in the retail and healthcare segments. AIaaS is expected to play a particularly large role in transforming the healthcare sector globally by facilitating better healthcare service delivery and improving how patient data is managed.

Regulatory Questions and Uncertainty

Current regulatory frameworks were written before AI was being used at scale, and there are many questions about the safety, security and ethical aspects of using AI. An international meeting of thought leaders in the UK at the AI Safety Summit saw work on the State of the Science Report to Understand Capabilities and Risks of Frontier AI. The summit agreed to further investigate artificial intelligence, and the next few years will see further debate and reviews of regulations to ensure they’re still fit for purpose in an AI-enabled world.

New Security Challenges

AIaaS gives developers access to powerful AI tools that can be deployed in various ways, but using these tools opens up the potential for new security risks. Malicious prompts could trick an AI into taking unexpected actions or disclosing personal information.

We’re already seeing prompt injection attacks, where malicious actors use creative ways to bypass the safety rails put in place by developers. In response to these attacks, developers of AI-augmented applications are looking at ways to filter inputs before they reach the AI model.

The Growth of AI-Focused Jobs

While AI has the potential to take over some jobs, it also opens up the possibility of new jobs in other areas. From prompt engineers to those who manage and train the models being used, those who have AI skills are very much in demand.

E is for The AI Ecosystem and Partnerships

Many organizations are working on AI implementations. From traditional cloud services platforms like Google Cloud and Microsoft Azure to organizations such as OpenAI and open-source developers working on independent projects.

In such a fast-moving industry, we believe the future of the AI ecosystem is one of openness and collaboration. For AI to continue developing and improving, AI platforms and models must work alongside technology providers and application developers to support easy deployment, training and integration.

We’re already seeing this in many parts of the industry. Midjourney and Osmo have worked with Google Cloud to help developers deploy their own models to the cloud, and companies such as Snorkel AI and Gretel have also come to the platform, making training AI models a much easier task.

Microsoft has its own AI Cloud Partner Program and has attracted Meta’s LLaMA and the Falcon models to their platform. Azure also provides easy access to Open AI and various tools and APIs, allowing AI models to work with Microsoft’s cloud software.

Most major cloud services providers also have compliance products that will help your organization meet the regulatory requirements for your industry more quickly and easily. Taking advantage of these products and partnerships can save your IT team a lot of time and reduce the need to reinvent the wheel when it comes to deploying AI.

If you’re considering implementing AI in your organization and are planning on hosting your models in the cloud, look at the partnerships each provider is running and consider whether they’re likely to be able to meet your needs in a low-friction way. As part of our AIaaS offerings, we can help you identify the best products and partners for your requirements.

N is for Navigating Transformation and Your Digital Strategy

Introducing AI to your organization can be a complex process. Digital transformation programs often stall because of poor communication or a failure to achieve alignment with key team members and stakeholders.

AI transformations can affect many different parts of an organization, so all key members of the team must be aligned and understand the program’s goals, risks and challenges, as well as their role in implementing the transformation.

Achieving Alignment With Your Stakeholders at All Levels

The first step in any digital transformation should be to ensure agreement and understanding at the C-Suite level and among other team members and stakeholders. This should cover the goals of the program, the desired outcomes, and any commitments needed from different teams.

For example, if you’re looking to implement an AI customer service system to handle basic queries, you’d create a road map outlining the KPI improvements the organization can expect from implementing this system. That could be reduced average call waiting and handling times and improved CSAT scores in the quarters after the system is implemented, and transformational value in the longer term as the AI system reduces the load on human customer service agents.

To ensure these systems are implemented properly, you’d need support from IT teams. You’d also need to have stakeholders in the customer service and training teams on board, as they’ll need to educate customer service agents on how to work with any new ticketing system or pick up on chats previously handled by AI agents.

Choosing the Scope for Your AI Projects

One issue often faced by organizations early on in their AI transformation journeys is choosing which workflows to automate. Some teams start out too small because they’re risk-averse and feel it would be better to implement just a few minor use cases. Others start out with too broad of scope, spreading themselves too thin and launching poorly coordinated AI initiatives.

Starting with a limited scope can backfire, as you’ll still have to go through an extensive planning and research phase, but the return you’ll see will be limited because of the small scale of the automation. Starting too large can also backfire due to the complexity of any AI project.

Treat AI projects just as you would a migration to the cloud. Pick low-risk but high-impact workflows to delegate to AI, and aim to do so in a highly coordinated way to maximize your returns.

Plan for Scaling

Another area where some organizations fall is scalability. When considering AI solutions, don’t just consider your current needs. Consider how the AI platform or service you’re looking at adopting would be able to grow with you.

If you’re looking at deploying in-house AI models, do you have the computing resources to scale the model to handle more queries if required? If you’re outsourcing, does the provider or cloud platform offer affordable, on-demand scaling? AI can offer significant productivity improvements, allowing you to expand your operations. Plan ahead so you don’t find the AI model itself limiting you.

Understand Your Data

For AI to be used to its full effect, it needs to be trained on high-quality data. If you don’t have enough data to feed into your AI systems, or the data is not in an organized format, you will run into challenges when working with it.

You can streamline your long-term AI adoption by building reusable data products that can be integrated into multiple AI solutions. For example, create data products for customer data, products, and business assets. Take advantage of APIs to feed data from data lakes into the various AI systems you implement.

Proactively Monitor Compliance

The regulatory landscape surrounding data processing is complex. Depending on where your organization is based and the territories it operates in, you could be required to comply with many different regulations for payment processing and data handling.

Whether you’re operating your systems in-house, outsourcing to SaaS providers or working with a cloud services platform, you’ll need to confirm that the systems you have in place are currently compliant with any relevant regulations.

Since AI is such a young field, governments worldwide are still debating how it should be regulated. In November 2023, the UK hosted an AI Safety Summit that representatives of 28 countries attended to discuss AI technologies’ potential risks and benefits. If you’re interested in using AI technologies within your organization, staying up-to-date with the results of such discussions and any changes to local or national regulations is essential.

C is for Customer Experience and Personalization

AI can improve the customer experience in a variety of ways. The most obvious is through chatbots and other forms of customer service automation. Still, machine learning can also be deployed in more subtle ways to improve the customer experience by offering better product recommendations or otherwise personalizing the user’s interactions with an app or website.

Improving Customer Service With AI

According to the Gartner Report “How Can Generative AI Be Used to Improve Customer Service and Support,” Generative AI can be applied to serve as an assistant to humans delivering customer service, for example:

  • Automating recurring tasks
  • Resolving low-complexity issues
  • Performing specific tasks within the realms of its training model

Gartner believes generative AI could help companies reduce the number of support staff they need by 20%–30% by 2026 but that current models can’t handle complex issues requiring human judgment.

However, as AI models can handle simple queries quite quickly, using them as part of your customer service strategy could reduce your average call handling times and call waiting times. It could also reduce the number of abandoned contacts, thereby increasing the number of issues resolved on the first contact.

AI as a Personalization Tool

Anyone who uses social media is already familiar with the power of algorithms to serve personalized content. The sophistication of TikTok’s algorithm helped it grow to have 1 billion users spread across 154 countries in just three years.

Users spend an average of an hour a day on TikTok, consuming videos designed to keep them engaged and entertained. The addictive nature of TikTok and other social media platforms is something that many addiction specialists and even Silicon Valley workers have raised ethical concerns about. Still, the power of the algorithm is undeniable.

Similar algorithms can be used to offer personalized product recommendations, both in-app and as email recommendations, or to decide what vouchers to send out to consumers who are part of a loyalty program.

Identifying Changes in Customer Behavior

Customer retention departments could benefit from using AI to prioritize which consumers to contact. AI models can review customer activity and identify consumers showing signs of disengagement from a brand. These customers could be offered vouchers, discounts or other benefits to bring them back to the company.

Similar models can be used to help sales staff identify customers who might be receptive to upgrade offers or who could be cross-sold other products. For the company, this presents an opportunity to get more sales at a relatively low cost, and for the consumer, this means any sales contacts they receive are more relevant to them and, therefore, less likely to be unwelcome.

Improving the User Experience in Software

Powerful software applications are often quite complex and have a steep learning curve. One emerging area of AI is the idea of the software “learning” which features you use and tailoring the user experience to your habits.

Personalized user interfaces can greatly improve the user experience by putting the most frequently performed tasks front and center. Users benefit from a more seamless, intuitive user interface that adapts to their habits and is tailored to their preferences.

AI tools could also serve as an extension of technical support, noticing if a user appears confused or “lost” within an application and providing advice about what to do next. Microsoft tried this many years ago with Clippy and other Office Assistants, but these assistants were of limited use at the time. Now that we have more powerful AI systems, we could see the return of these assistants but in a more intelligent form.

E is for Evolution and Future Outlook

AI is advancing at an incredible pace, and that’s likely to continue over the next decade. The generative AI models we’re using today are impressive but flawed. As the technology matures and we better understand where AI can be used to good effect, whole industries will be revolutionized.

In the near future, we can expect AI to enable significant advances in scientific fields as researchers use the power of machine learning to process massive data sets and evaluate incoming data in real-time.

We’ll also see AI bring in the next generation of consumer experiences, bridging the gap between the digital and analog worlds. Other next-generation technologies, such as VR / AR and the Metaverse, have been vaporware for decades. While products offering such experiences are available, they’ve failed to gain mainstream traction and are primarily used by tech enthusiasts and video gamers. AI has the potential to improve such products, allowing the mass adoption that has always been just around the corner.

Your Personal AI in Your Pocket

The most popular AI models, such as ChatGPT and the open-source GPT-J, MidJourney, and LLaMA, require large volumes of training data and significant amounts of processing power to work well. GPT-J requires around 24GB of GPU memory at runtime, so users wishing to run a model locally would need to have multiple GPUs.

Fortunately, LLM and Generative AI models are getting more efficient. It’s already possible to run an AI model comparable to GPT-3 in terms of performance on a laptop or a smartphone, although it requires some technical skill to set up.

With consumers becoming increasingly privacy-conscious, we’ll likely see a drive towards local AI models capable of running on modern smartphones, making private and secure AI-driven personal assistants a reality.

More Legal Clarity

There’s a lot of uncertainty surrounding issues such as intellectual property rights with AI. This uncertainty has caused some companies to err on the side of caution and avoid using AI-generated artwork or text as part of their products. In the coming years, we’ll see the law catch up regarding these issues, providing clarity on issues such as whether it’s safe to train AI models on data taken from the web, and then use those models to create assets for commercial use.

AI and Edge Computing

There’s likely to be a trend of AI being deployed at the edge, closer to where IoT devices are generating data. This trend will help reduce latency and enable real-time data processing to support innovations such as autonomous vehicles and smart buildings.

Quantum Computing

Quantum computing presents threats to computer security and potential breakthroughs in AI’s capabilities. Should quantum computing become commercially available at scale, we’ll see the development of new neural networks that can solve problems previously considered impractical or beyond current AI models.

The use of AI is already so commonplace that most people interact with an AI-powered service in their day-to-day lives, whether in the form of a customer service chatbot or a recommendation algorithm. If your business isn’t already using AI, you risk being left behind as your competitors take advantage of the productivity-enhancing and cost-saving benefits of modern AI tools, as well as their capacity to provide added value to their customers.

Getting Started With AI

If you’re considering implementing AI in your organization, our team of AI experts can assist you. Our services include AI strategy development and consulting for all sizes of business.

Whatever stage your AI implementation is at, we can help you review your current workflows, assess your requirements and draw up a plan for migrating legacy workflows to AI.

Our expertise includes:

  • Machine learning
  • Automation
  • Natural language processing
  • Predictive analytics

We can assist with training machine learning models, including traditional supervised and unsupervised training methods, as well as more sophisticated reinforcement learning. We understand every business is different and will work with you to assess the data you’re working with and the best way of achieving reliable and secure outcomes with that data.

Our team can also assist with understanding the legal and ethical implications of implementing AI within your business so you can feel confident you’re operating in compliance with any regulations relevant to your industry and jurisdiction.

Getting started with artificial intelligence may be easier than you think. Our AIaaS solutions offer flexible, competitive pricing, making them accessible to businesses of all sizes. In addition, as part of our consulting services, we can help you understand which parts of your business would be the easiest to augment with AI and how to get managers and key stakeholders on board with the transition.

By choosing to implement your AI strategy, you’ll help your organization provide better value for its customers, become more productive, and make data-driven decisions. Our experienced AI experts will guide you along the way, helping you prioritize which workflows to transition to AI to ensure a smooth and seamless changeover while maximizing your return on investment.

If you’d like to know more about implementing AI in your organization, contact us today to book a consultation.

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