Business

Overcoming the moat: Fostering an innovative AI industry

Published

1 month ago

August 26, 2023

admin

Victor Botev, CTO and co-founder at Iris.ai

In recent years, Artificial Intelligence (AI) models have undergone remarkable advancements, with smart models tailored to specific use cases emerging as strong contenders against proprietary Large Language Models (LLMs) developed by big tech companies. As our understanding of these models deepens, the performance gap between smaller, open-source AIs and LLMs is rapidly closing. The move towards smarter, customisable models emphasises the power of community-driven innovation and highlights the need to prioritise speed, efficiency, and data quality in AI development.

With the increasing speed at which the complexity of these models grows, questions around pausing development have inevitably arisen. Putting a halt to AI development altogether, however, could disproportionally affect small start-up developers. Should such policies come into effect they may well expand existing moats, which are the advantages enjoyed by incumbents that hinder new entrants into the market.

Moreover, even as start-ups have made gains in narrowing the gap between themselves and larger developers, they still face a unique challenge in the AI landscape, where the scale and quality of data is crucial for training LLMs. Hyperscaler firms have already accumulated vast amounts of data through their existing services, granting them a significant advantage. As the number of users utilising their platforms increases, so does the volume of data available for improving their LLMs.

In contrast, organisations with limited customer bases struggle to access such extensive data, hindering their ability to train and improve their AI models. Halting AI developments would only widen the gap, further impeding start-ups from catching up to big tech’s advancements.

With competition being essential for innovation, there are a variety of ways to foster the development of AI and avoid the moats of Big Tech from stifling the industry.

Niche Domain-Specific Language Models:

Instead of directly competing with big tech’s general-purpose LLMs, many AI developers are differentiating themselves by developing smarter language models tailored to specific domains. By specialising in verticals such as academia, healthcare, finance, or law, start-ups can deliver superior AI solutions that better understand the intricacies and nuances of specific industries. Specialised models can address domain-specific challenges and provide more accurate and relevant insights, effectively staving off competition from hyperscalers.

Many are prioritising acquiring domain expertise and collaboration with industry professionals to develop their AI models aligned with specific verticals. An iterative feedback loop between experts and model developers works to refine tools more efficiently over time, ensuring continued improvement and reliability. Moreover, by integrating industry-specific knowledge into their models, developers can provide tailored solutions that better meet the needs of customers in those sectors. This integration creates models that are more equipped to provide results for the specifics of a given field, better navigating the challenges of potential regulation, technical information and vocabulary. This targeted approach enhances their competitive advantage, as domain-specific language models can offer specialised insights and perform complex tasks more effectively than generalised models.

Start-ups specialising in vertical-specific language models are also exploring the potential of transfer learning. By using pre-trained general-purpose language models as a foundation and then fine-tuning them with domain-specific data, start-ups can achieve faster development cycles as developers are able to capitalise on the knowledge already embedded in the general models while working to tailor them to specific verticals.

Open-Source Community

Open-source initiatives have already played a pivotal role in democratising AI technologies and narrowing the gap between smaller competitors and hyperscalers. These initiatives promote collaboration, knowledge sharing, and code transparency, enabling developers to collectively advance AI capabilities. Open-source projects are added to by a wide variety of contributors, harnessing the power of collective expertise and expanding the availability of advanced AI tools and frameworks.

By engaging with the open-source community, those building AI models can access pre-existing libraries, frameworks, and resources, saving valuable development time and costs. Through these collaborations, many start-ups are enhancing their models, fostering innovation, and gaining recognition for their contributions.

The AI landscape is experiencing a paradigm shift towards smaller, customisable models that prioritise efficiency and effectiveness over sheer scale. The performance equivalence demonstrated by smarter models compared to their larger counterparts challenges the notion that bigger is always better. By leveraging their advantages, smaller companies can offer AI solutions that are more transparent, accessible, and capable of meeting specific industry needs.

This positive trend should not be taken for granted, however, and the expansion of moats should not only be a concern for the wider industry but also policymakers. As AI continues to advance, it is crucial for a balance to be struck between fostering innovation and ensuring fair competition. Any new legislation or regulations should consider industry best practices and the potential differences in impact across all of those involved in AI. A thriving ecosystem is one that aims to promote healthy competition, prevents monopolisation, and encourages the growth of diverse AI solutions.

Institutional bodies must focus on educating public authorities about the inner workings of AI and LLMs. By funding academics and critical bodies, granting them the resources to study and hold the big players accountable, institutions can help, not hinder, the open-source movement. This will do away with unclear, complex, and unhelpful regulation – allowing the community to publish and distribute their findings – naturally regulating the ecosystem and improving AI for the many, not the few.

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Business

How can law firms embrace automation and revolutionise their payments?

Published

14 hours ago

September 28, 2023

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 ago

September 27, 2023

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.