Technology
Will AI lead to a better business?
Published
2 weeks agoon
By
admin
Article by engineer Sara A. Al-Emadi, Research Associate at Hamad Bin Khalifa University’s Qatar Computing Research Institute (part of Qatar Foundation), an expert in AI, deep learning, and cutting-edge technology.
Thanks to the availability of large amounts of data, the advancement of computer storage systems, sensors and networking technologies, Artificial Intelligence (AI) solutions, based on techniques such as Machine Learning (ML) and more specifically Deep Learning (DL), have been growing rapidly. Recently, ChatGPT has disrupted the natural language processing field, offering businesses new, efficient and cheap ways of integrating such a system into their on-ground products, assessing workflow management, designing smart dashboards and forecasting future events.
Examples of AI technologies that have reshaped traditional business in terms of day-to-day applications varies, below are few examples:
- Recommendation systems: Learning patterns from data gathered from users have been the essence of the success of modern-day AI systems. An example of this case is streaming videos and content or browsing products on online platforms such as YouTube, Spotify, Netflix or Amazon. In general, a recommendation system works as follows: the user will search for items/movies/songs or click on an item in the webpage and their search/selection data will be fed into a DL model where it will learn the user’s preferences. Next, it will predict and recommend items with a higher chance of the user liking them or tailoring towards the preferability of the user. Such systems have improved the probability of commercial and social media platforms significantly.
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Sara A. Al-Emadi
Mitigating security risks through monitoring and anomaly detection: Due to the advancement of technology, cyber-security attacks are becoming very complex such that the current detection systems are not sufficient enough to mitigate or address them. However, thanks to AI, these anomaly attacks can be detected before they even take place. Therefore, in one of our studies, we analysed the use of AI, more specifically the DL model, to detect network intrusion detection attacks [1]. We observed that such a system was able to detect attacks with a very high accuracy. Recently, AI systems have shown their effectiveness in protecting physical premises against drone attacks by detecting and identifying malicious drones using the noise generated by their propellers [2]. This illustrates the promising usage of AI systems to enhance cyber and physical security.
- Financial analysis: Historical financial data, customer behaviours, market trends, and economic indicators are fed into AI systems to assist in investment decisions in the financial and property markets.
- Marketing and advertisements: AI has enabled businesses to tailor their offerings to individual customers’ preferences by analysing historical data. Therefore, enhancing the customer’s experience through increasing their engagement in the content provided which increases the profits generated by these companies through exposure and the creation of effective targeted marketing campaigns.
Despite the outstanding performance of modern AI models in various applications, these models are designed based on the a training samples collected from the same distribution, consequently, experience a significant performance degradation when deployed in real environments. Scaling AI systems across businesses is a challenge and their ease of deployment is a challenge. A key contributor to this phenomenon is the gap between the distributions of the samples which it was exposed to during the design process and the samples observed in the deployed environment. For example, a self-driving car can be designed based on samples gathered from Europe. However, when deployed in Asia, the AI model will fail. To address this issue and while being limited to small data samples during the design process, my current research at QCRI focuses on proposing new techniques to address this issue in order to bridge the gap by improving the generalisation ability of modern Al to new and unseen data.
Current trends in AI, such as the boom in the integration of ChatGPT in many businesses across different sectors such as chatbots which can be found in healthcare systems, restaurants and hotel reservation systems started to have a visible impact on businesses in terms of how they are operating, the expertise required to attract and hire and the costs associated with such an integration. Additionally, transparency on how these models are learning along with the lack of explainability provided for how the decisions are been made by these models remain as open research questions. Without a clear understanding of how these decisions are being made, deploying these models could lead to many ethical and in many cases dangerous decisions that can harm the consumers, clients, patients and communities. Therefore, in my opin ion, we will witness more and more integrations of these systems in many businesses. However, these systems are unlikely to diminish job opportunities for individuals, in fact, they will enhance how people do their jobs, the same way computers have enhanced our business and led to great breakthroughs in the industry.
References:
[1] S. Al-Emadi, A. Al-Mohannadi and F. Al-Senaid, “Using Deep Learning Techniques for Network Intrusion Detection,” 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), Doha, Qatar, 2020, pp. 171-176, doi: 10.1109/ICIoT48696.2020.9089524.
[2] S. Al-Emadi, A. Al-Ali, and A. Al-Ali, “Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks,” Sensors, vol. 21, no. 15, p. 4953, Jul. 2021, doi: 10.3390/s21154953.
Business
How can law firms embrace automation and revolutionise their payments?
Published
19 hours agoon
September 28, 2023By
editorial
Attributed to: Ed Boal, Head of Legal at Shieldpay
Once again, AI is dominating international headlines. This time, it’s due to a closed-door meeting this month between tech leaders and US senators to discuss the technology’s regulation.
AI and automation isn’t just for the likes of Big Tech. We’re seeing predictive and automated technologies transform almost every sector and the legal industry is no exception. In fact, recent research from HBR Consulting found that 60% of law departments had implemented a legal data analytics tool last year and more than 1 in 4 indicated they were using AI for at least a single use case.
However, adoption isn’t without its challenges. Reticence remains among some and there’s also the danger of ‘transformation fatigue’ slowing real progress. If law firms want to reap the many benefits of automation – including revolutionising their payment processes – these challenges need to be carefully considered and thoughtfully addressed.
An area of great opportunity
Often seen as conservative, the legal industry has been gradually warming up to the idea of automation and technology.
While some pioneering firms have been quick to embrace automation tools, others remain cautious about disrupting their established workflows. As we navigate this landscape, it’s clear that certain areas of legal services are ripe for innovation.
One area is contract management. The process of drafting, reviewing, and managing contracts has traditionally been time-consuming and prone to human errors. Automation can alleviate these pain points by streamlining the entire lifecycle of contracts, from creation to renewal, thereby enhancing efficiency and reducing risks.
Another promising domain is legal research. Thanks to advancements in natural language processing and machine learning, legal professionals can now leverage AI-powered research tools that analyse vast volumes of legal data to provide accurate insights and case precedents swiftly.
But, while progress is undoubtedly being made, the legal sector still lags other sectors when it comes to innovation.
What’s getting in the way of progress?
This isn’t always down to a resistance to change. Often, it’s a result of firms spreading their resources too thinly across numerous technology initiatives.

Ed Boal
Attempting to tackle everything at once can result in ‘transformation fatigue’, where the benefits of individual innovations get diluted – leading to frustration and slower progress.
Before legal firms embark on digital transformation projects, a critical first step is introspection. Recognising and acknowledging areas where legacy processes and manual tasks still hold sway is paramount to optimising the impact of automation.
For many firms, archaic practices continue to consume valuable time and resources, diverting attention from higher value, billable tasks. One often-overlooked area is payments.
Legal firms play a critical role in complex transactions, from M&A and real estate deals to litigation and arbitration payments. The associated admin and processes represent a drain of firms’ time and resources. Spanning everything from collating stakeholder payment details and verifying payee identity to ensuring compliance with Know Your Customer (KYC) and Anti Money Laundering (AML) regulation, this adds unnecessary stress for lawyers – who would rather dedicate their time and expertise to their clients’ legal needs.
The repercussions of such time-consuming financial processes reverberate throughout the entire organisation. Administrative burden weighs heavily on the team, affecting productivity and ultimately, the bottom line: recent research from Shieldpay, surveying the UK’s Top 100 law firms, found that almost 1 in 3 (32%) say KYC collection and verification checks take 4-9 working days.
At the same time, firms are exposed to significant financial risk which can make handling client funds a costly endeavour. Not only are they penalised with fines if found to be in breach of stringent client account rules but firms are also subject to hefty premiums for Professional Indemnity (PI) insurance. No wonder 73% of all legal professionals and 90% of junior law professionals are concerned about the risks and time costs associated with holding client funds.
Revolutionising payment transactions
In short, manual payment processes are more than just an inconvenience for modern law firms. They can damage relationships with clients – who have come to expect a fast, painless and automated payout experience in a digital world – and impede revenue generation by tying up top talent in an endless cycle of paperwork and (unbillable) admin.
So how can firms take the pain out of legal payments?
Fortunately, new payment technologies have emerged as a formidable ally. Third-party payment providers offering solutions for law firms, such as escrow and paying agent services for specific transactional deals, or more embedded payment solutions such as managed accounts (TPMAs) – i.e. outsourced client account functions – offer secure and instant transactions, while prioritising transparency and automation.
TPMAs operate as an escrow payment service in which the third-party – a licensed external payments partner – receives and disburses funds on behalf of a firm and their client(s).
With advanced encryption ensuring data security, working with a regulated payment partner means legal professionals and their clients can engage in financial transactions with peace of mind – while law firms benefit from improved operational efficiency.
And the advantages don’t stop there. Enhanced transparency builds a sense of confidence and trust, while the elimination of manual data entry and repetitive tasks allows legal professionals to devote more time to legal services and fostering stronger relationships with their clients.
AI and automation has much to offer the legal sector. But its adoption must be carefully planned in order to avoid transformation fatigue that risks stalling progress altogether. With typically shallower pockets than Big Tech giants, it’s important for law firms to focus their efforts on specific areas that could benefit from automation, rather than rush to overhaul their entire way of working, all at once. This controlled phase-out is the key to avoiding adoption frustration, seeing a real impact on profits and productivity and setting firms up for real, lasting change.
Business
In-platform solutions are only a short-term enhancement, but bespoke AI is the future
Published
2 days agoon
September 27, 2023By
editorial
By Damien Bennett, Global Director, Principal Consultant, Incubeta
If you haven’t heard anyone talking about artificial intelligence (AI) yet, then where have you been? Conversations about AI and its advantages to society have been a key talking point over recent months, with advances being made in the generative AI race and ChatGPT opening a whole plethora of possibilities. Many have highlighted the advantages of AI, but notably it’s ability to create human-like content.
But these discussions have only scratched the surface of what AI is capable of doing. It is for far more than just essay writing, adding Eminem to your rave and photoshopping dogs into pictures.
In marketing, we have been using AI for years, for everything from analyzing customer behaviors to predicting market changes. It’s enabled us to segment customers, forecast sales and provide personalized recommendations, having a huge impact on how our industry works.
It is even, for the more savvy marketers of the world, becoming a key tool in maximizing budget efficiency – which is apt, considering over 70% of CMOs believe they lack sufficient budget to fully execute their 2023 strategy.
Now, as AI becomes more intelligent, the number of efficiencies it can unlock continues to rise. Not only can it help brands get the most out of their available resources and identify any areas of waste, but it can also help highlight new opportunities for growth and maximize the impact of your budget allocation.
The trick, however, is to veer away from the norm of using in-platform solutions with a one-size-fits-all approach and create your own, bespoke solutions that are tailored to your business needs.
Pitfalls of in-platform solutions
In-platform solutions aren’t by any means a bad thing. In fact, built-in AI tools have become increasingly popular, owing to their ease of integration, user-friendly interfaces and minimal set up requirements. They come pre-packaged with the platform, offering the user the ability to leverage AI technologies without the need for in-depth technical expertise or the upfront cost of building a solution from scratch.
However, the streamlined and accessible nature of in-platform AI solutions comes at the expense of complexity and customization. They are designed to serve a broad user base, but for the most part are built using narrow AI solutions with predefined features and workflows.
This makes them great for assisting with common AI tasks, but they lack the flexibility to tailor functionality towards unique business requirements or innovative use cases, limiting the potential efficiencies and cost savings that can be unlocked. Additionally, if a business’ competitors are using the same platform, they are probably using the same AI solution, meaning any strategic advantage gained from these will be reduced.
Bespoke AI solutions, on the other hand, may carry a higher initial investment – but can offer a significantly more attractive ROI over a short amount of time.
Why customized and adapted AI is the key
The difference between bespoke AI and in-platform solutions is similar to that between home cooked food and a microwave meal. Yes, it is more time consuming to prepare, and yes it likely carries more of an upfront cost, but the end result is going to be far more appealing and will carry more long-term value (financially… not nutritionally).
That’s because bespoke solutions, by nature, will have been tailored to address your brands specific needs and challenges. These custom-built tools allow for much greater efficiencies by streamlining workflows across different channels, automating more complex tasks, and providing deeper, more relevant insights.
The increased level of optimization can significantly improve productivity and reduce operational costs over time, offering a higher ROI. The increased flexibility of bespoke AI also allows brands to implement innovative use cases that can significantly differentiate them from their competitors.
The data analyzed can be specifically chosen to match business requirements, as can the outputs of the AI tool, providing a significant advantage when understanding and acting on the insights provided.
Additionally, these tools are, by nature, more scalable. They can be updated, upgraded and expanded as needs change, ensuring they continue delivering value as the business grows. They can also be designed to integrate with any existing IT infrastructure, from CRM systems and databases to marketing platforms and sales tools – leading to more efficient and effective decision-making.
Managing finances with AI
It’s no secret that AI in marketing automation has, and will continue to, revolutionize the way marketing is done. It has a bright, if slightly terrifying, future and can help CMOs to unlock new efficiencies, maximize the impact of their budgets and increase their ROI. And as this technology becomes more advanced, its impact will only increase.
But we already know that…and so does everyone else.
So, in order for businesses to make themselves stand out from the crowd , they must look to fully adopt the power of AI. Creating a customized and unique AI solution could be the way to set yourself apart from your competitors. A bespoke AI tool can provide brands and businesses with features unique to them and their business needs. As a result, companies will benefit from more useful data and better results to make more data-driven decisions for their business. Ultimately, this will help brands to maintain a competitive edge over their competitors, deliver ROI and most importantly optimize their budgets.
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