Power of automation to augment productivity in the back office has been clear for decades, but the recent emergence of advanced AI and ML tools offers a step change in what automation can accomplish, including in regulated industries, says Paul Meyer at iOCO.
AI is one of the most misunderstood concepts in the modern computer era. Today, almost all discussions around it start with generative AI, but AI and the opportunities it presents for organisations are much broader.
Definitions of AI abound. One source notes AI is defined as a machine’s ability to perform the cognitive functions we usually associate with human minds. Gartner notes AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.
But the most compelling applications of AI and ML technology can often be found behind the scenes with organisations increasingly finding substantial efficiency gains by applying AI and ML-powered tools to back-office procedures such as document processing, data entry, employee onboarding, and workflow automation.
The power of automation to augment productivity in the back office has been clear for decades, but the recent emergence of advanced AI and ML tools offers a step change in what automation can accomplish, including in highly regulated industries.
AI is not just another technology or business case. Although interacting with AI may seem simple from the user’s perspective, it involves many sophisticated technologies working together behind the scenes — big data, natural language processing, ML and more. Integrating these elements requires expertise, strategy and insight.
Use cases
The impact of cognitive technologies on business will grow significantly in the immediate future. Consider just one aspect of cognitive technology, generative AI. McKinsey calls generative AI the next productivity frontier, which could add trillions in value to the global economy. Nearly every corporate function is expected to radically change. In fact, the nature of work itself will be transformed.
According to McKinsey’s 2022 Global Survey on Artificial Intelligence, uptake and implementation of AI has more than doubled—from 20% of respondents having adopted AI, in at least one business area in 2017, to 50% in 2022.
MIT Technology Review remarks it is easy to understand the growing popularity of AI in challenging economic times as it can help meet ever increasing customer expectations with organisations being asked to do more with less.
AI is becoming mainstream and is not just another technology or business case. It will shape the future, what it means to be a company and some claim possibly even what it means to be human.
The straight answer to this is that all business sectors stand to be transformed through the implementation of AI and Machine Learning. These solutions automate and optimise all manner of processes and tasks. For example, retailers may use AI-powered chatbots to handle customer queries, track orders, and respond to refund requests.
In deploying AI to perform these routine tasks businesses improve response times and enhance customer experiences.
At the same time, financial institutions are discovering the power of ML to identify anomalies within large volumes of data that may indicate fraud—an early warning system against financial loss. Organisations across industries can employ AI and ML tools to extract and analyse information from documents, such as invoices, contracts, and reports, and to reduce the burden of manual data entry while speeding up processing times and minimising human error.
So, the fact of the matter is that we are now way beyond digital. It is the next era of computing and opportunity.
For decades computers have been able to process information according to predefined rules. While computing systems have done an incredible job of processing information, programming business rules and automating processes to improve communications, productivity and efficiency but cognitive systems take it to a new stratosphere. And we are only scratching the capability surface.
Cognitive systems are defined by their ability to think. They can learn, reason and act based on proficiency and experience, as opposed to following rules that have been programmed. This is what sets cognitive computing apart.
Managing data
AI requires data and models. The more data you feed it, the wider the visibility it can provide into the business. But the old saying garbage in, garbage out is certainly true of AI algorithms especially Large Language Models, LLMs – which can be seen as the pinnacle of AI development, but they are only one of many AI components.
They are a subset of generative AI, which is itself a subset of machine learning. The right AI tool will vary according to application. Large language models are revolutionising content management and transforming the ways in which we interact with unstructured content.
The latter refers to the process of extracting insights, patterns, and useful information from unstructured data sources which do not have a predefined model or format. This makes it more challenging to analyse when compared to structured data, which is organised in a specific format such as tables or databases.
Unstructured data comes in various forms, including text, social media posts, emails, audio recordings, images, videos, and more. These data sources often contain valuable information that can be utilised for decision-making, understanding customer sentiment, market analysis, fraud detection, and other business applications.
Unstructured data analytics involves applying techniques and technologies to extract meaningful information from such data and are considered a hidden gold mine for businesses. With their unprecedented ability to comprehend context, extract semantics and knowledge, and automate content management tasks, LLMs are unlocking new value in the content management system.
The emergence of generative AI and large language models has created a pivot point for the IT industry, where experimentation and differentiation can happen on a large scale. The ability to embed smart algorithms everywhere is not only possible, but essential in order for organisations to compete and prosper.
Let us start with operational data which are the transactional pieces, including ERP records and CRM data, such as payroll, supply chain, order entry, production data and development data. An effective AI solution will extract meaning and knowledge from this data which is real-time and typically not part of the training set of an LLM.
Then there is experience data which is information that is exchanged with customers, including elements that affect the experience of the customer, such as support. Learning data is a new form of data that represents the state of the AI algorithm and includes everything that has been learned from the models.
However, it should be noted that organisations may lack in-house LLM expertise and the right knowledge to get the most benefit from AI in order to construct learning data.
Cybersecurity
A recent research report estimated the global market for AI-based cybersecurity products was about $15 billion in 2021 and predicted to surge to approximately $135 billion by 2030. Morgan Stanley reports that in the evolving landscape of artificial intelligence, AI, both cybersecurity teams and hackers are using AI to their advantage.
Security leaders must look to the adoption of technologies, strategies and skills that can enable managed, secure access. According to Forrester, simply blocking access is not a sustainable position, inevitably leading to many employees finding ways to bypass IT visibility and control.
Understanding and reacting to threats in real time is essential as is the choice of cybersecurity partner – you need one capable of tackling risks by applying AI-powered solutions with comprehensive monitoring capabilities.
Cybersecurity suppliers increasingly rely on AI in conjunction with more traditional tools such as antivirus protection, data-loss prevention, fraud detection, identity and access management, intrusion detection, risk management and other core security areas.
Morgan Stanley goes on to confirm that because of the nature of AI, which can analyse enormous sets of data and find patterns, it is uniquely suited to tasks such as:
- Detecting actual attacks more accurately than humans, creating fewer false-positive results, and prioritising responses based on their real-world risks.
- Identifying and flagging the type of suspicious emails and messages often employed in phishing campaigns.
- Simulating social engineering attacks, which help security teams spot potential vulnerabilities before cybercriminals exploit them.
- Analysing huge amounts of incident-related data rapidly, so that security teams can swiftly take action to contain the threat.
Additionally, AI has the potential to be a game-changing tool in penetration testing, intentionally probing the defences of software and networks to identify weaknesses. By developing AI tools to target their own technology, organisations will be better able to identify weaknesses before hackers can maliciously exploit them. Preventing breaches before they occur will not only help to protect individual and company data but will also reduce business IT costs.
Key takeaways
- AI is not just another technology or business case.
- The most compelling applications of AI and ML technology can often be found behind the scenes.
- According to McKinsey’s 2022 Global Survey on Artificial Intelligence, uptake and implementation of AI has more than doubled.
- Financial institutions are discovering the power of ML to identify anomalies within large volumes of data that may indicate fraud.
- Organisations across industries can employ AI and ML tools to extract and analyse information from documents and to reduce the burden of manual data entry.
- The fact of the matter is that we are now way beyond digital. It is the next era of computing and opportunity.
- Cognitive systems are defined by their ability to think. This is what sets cognitive computing apart.
- Cognitive systems can learn, reason and act based on proficiency and experience, as opposed to following rules that have been programmed.
- The right AI tool will vary according to application.
- Large language models are revolutionising content management and transforming the ways in which we interact with unstructured content.
- Revolutionising content management refers to the process of extracting insights, patterns, useful information from unstructured data sources which do not have a predefined model or format.
- With ability to comprehend context, extract semantics and knowledge, and automate content management tasks, LLMs are unlocking new value in the content management system.
- Morgan Stanley reports that in the evolving landscape of AI, both cybersecurity teams and hackers are using AI to their advantage.
