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Harnessing AI for Language Service Providers

Richard Brooks

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AI for LSPs

AI for LSPs

In my recent webinar, “Beyond Translation: Harnessing AI for Language Service Providers,” in partnership with Elia we explored the game-changing role of artificial intelligence (AI) in the language services industry. The session brought together a diverse group of professionals eager to learn how AI can help them stay competitive, creative, and efficient in an increasingly dynamic market.

Throughout the webinar, we discussed the transformative potential of AI and its impact on various aspects of language service providers’ (LSPs) operations. From streamlining workflows and enhancing quality assurance to unlocking new opportunities for growth and innovation, AI has emerged as a powerful tool for LSPs looking to thrive.

Key themes that emerged from the webinar included the importance of mastering AI technologies to amplify creativity, leveraging AI for strategic decision-making and market analysis, integrating AI into sales and marketing efforts, and navigating the rapidly evolving AI landscape with an agnostic approach to language models. Following the webinar, I had the opportunity to engage in numerous conversations with attendees and industry colleagues. These discussions further underscored the significance of the themes covered and provided valuable insights into how AI is being applied in real-world scenarios within our industry.

Through these post-webinar exchanges, I gained a deeper understanding of the innovative ways LSPs are harnessing AI to streamline processes, enhance quality, and deliver more value to their clients. It became clear that those who are proactively exploring and implementing AI solutions are positioning themselves for success in an increasingly competitive market. These conversations also highlighted the importance of knowledge sharing and collaboration within our industry, as we collectively navigate this transformative technology and unlock its full potential for our businesses.

The purpose of this blog post is to provide a deeper dive into the practical applications and strategies discussed. We’ll explore real-world examples, best practices, and actionable insights to help LSPs harness the power of AI effectively. Whether you’re just starting to explore AI or looking to optimise your existing AI-powered workflows, (hopefully) this post will offer some guidance and inspiration for your journey.

The Hype Cycle and Generative AI

Slide 12 (pictured below) introduced the concept of the Hype Cycle, this is a graphical representation of the maturity, adoption, and social application of specific technologies. Developed by Gartner, this model helps businesses understand the potential and trajectory of emerging technologies, allowing them to make informed decisions about when and how to invest in these innovations.

According to the Hype Cycle, generative AI, which includes advanced language models like GPT-4 and image generation tools like DALL-E, is currently at the peak of inflated expectations. This suggests that the technology is garnering significant attention and excitement, with many businesses eager to explore its potential applications.

For language service providers, the hype surrounding generative AI presents both opportunities and challenges. On one hand, the increased interest in AI-powered language solutions may lead to a surge in demand for LSPs that can effectively integrate these technologies into their offerings. This could open up new markets and revenue streams for forward-thinking LSPs.

On the other hand, the hype around generative AI may also create unrealistic expectations among clients, who may assume that AI can completely replace human translators or instantly solve all their localization requirements. LSPs must navigate this hype carefully, educating their clients about the realistic capabilities and limitations of AI while emphasising the continued importance of human expertise in the language services process.

As the Hype Cycle suggests, generative AI is likely to experience a “trough of disillusionment” as the initial excitement wears off and the limitations of the technology become more apparent. LSPs that have built their strategies around the hype may find themselves struggling to deliver on their promises or differentiate themselves in a crowded market.

There is a need to strike a balance between embracing the potential of these technologies and maintaining a realistic understanding of their capabilities. By combining the power of AI with the irreplaceable value of human expertise, LSPs can navigate the hype and deliver truly transformative language solutions to their clients. However, it is crucial for LSPs to focus on authentic AI integration that goes beyond surface-level claims and delivers real value to their clients, avoiding the pitfalls of “AI-washing” and ensuring they can walk the walk, not just talk the talk. Adding the words AI Enabled to an electric toothbrush is just silly. 

Finding and Serving Clients Who Value Your Services

The inspiration for this talk came from a conversation I had with John Terninko. We were astonished at the procurement practices of some larger LSPs when reselling the language services of mid-sized and smaller LSPs. While it’s understandable that they want to maximise margin, using AI as a tool to pressure suppliers into lowering their prices is not a sustainable (or innovative) approach. This short-sighted strategy may provide short-term gains but can ultimately damage the long-term health and diversity of the language services industry.

Slide 15 in the presentation emphasised a crucial point for language service providers: the importance of targeting clients who recognise and appreciate the value of their services. This concept is closely aligned with Michael Porter‘s (and later Cliff Bowman’s) work on competitive strategies, particularly their ideas about differentiation. Porter argues that companies can achieve a sustainable competitive advantage by offering unique or superior products and services that meet the specific needs of their target customers. By focusing on differentiation rather than solely competing on price, companies can attract clients who are willing to pay a premium for high-quality, specialised solutions.

A differentiation strategy involves creating a product or service that is perceived as unique or superior in the market, allowing the company to charge a premium price. For LSPs, this means identifying the specific needs and preferences of their target clients and developing innovative, specialised language solutions that meet those needs in a way that sets them apart from competitors.

Cliff Bowman, a professor of strategic management at Cranfield School of Management, developed the “Strategy Clock” model as an extension of Michael Porter’s generic strategies. The Strategy Clock is a tool used to analyse and develop competitive strategies by considering the perceived value and price of a company’s products or services. This helps companies understand their current competitive position and guides them in making strategic decisions to improve their market standing. By considering the relationship between price and perceived value, companies can choose the most appropriate strategy to meet their objectives and outperform competitors.

LSPs can use the Strategy Clock to assess their current competitive position and develop strategies that align with their target clients’ needs and preferences. By focusing on differentiation and delivering high-value services, LSPs can attract and retain clients who are willing to pay a premium for specialised, high-quality language solutions. Servicing end customers directly can be more expensive due to additional costs associated with account management, marketing, and strategic planning. To offset these costs and maintain profitability, LSPs must seek higher margins from these clients. However, this approach can be more sustainable in the long run, as it allows LSPs to build a diversified portfolio of customers, reducing their dependence on a single client or market segment. By spreading risk across a wider range of clients and industries, LSPs can better weather market fluctuations and maintain a stable revenue stream, while still delivering the high-quality, specialised services that their clients value.

Large Language Models

Large Language Models (LLMs) are a type of artificial intelligence that can process, understand, and generate human-like text. LLMs are trained on vast amounts of data, often exceeding 10 terabytes (TB) in size, allowing them to comprehend and respond to natural language in a way that mimics human communication.

The training process for LLMs involves feeding the model with massive textual datasets, such as books, articles, and websites. The model then analyses and learns from the patterns, structures, and contextual relationships within this data, developing a deep understanding of language. This training process is computationally intensive, requiring powerful hardware and significant time to complete.

Once trained, an LLM can be fine-tuned for specific tasks or domains, allowing it to adapt its knowledge to specialised contexts or industries. This fine-tuning process is less resource-intensive than the initial training and enables LLMs to be customised for various applications in the language services industry.

There are two main categories of LLMs: open-source and closed-source models. Open-source LLMs have their source code and training data publicly available. This means that anyone can access, modify, and use these models for their own purposes, subject to the terms of the open-source licence. Open-source LLMs foster collaboration, transparency, and innovation within the AI community, as researchers and developers can build upon and improve existing models. On the other hand, closed-source LLMs, like OpenAI’s GPT-4, Gemini and Claude, are proprietary and have their source code and training data kept private. These models are typically developed by large tech companies or research institutions with significant resources and expertise. While closed-source LLMs can offer state-of-the-art performance and capabilities, their lack of transparency and limited accessibility can hinder wider adoption and innovation.

LSPs often work with high-profile clients who have strict data protection and privacy requirements, necessitating the use of LLMs in secure environments. To address these concerns, LSPs may host LLMs within their own controlled infrastructure, implementing robust security measures like encryption, access controls, and monitoring systems. They may also employ data anonymization and pseudonymization techniques to protect sensitive information during LLM training and inference processes. Additionally, dedicated security and compliance teams ensure that LLM usage aligns with industry best practices and regulatory requirements. Prioritising security and confidentiality in their use of LLMs is also often a signifier of value (helping you to differentiate your company). 

The AI I use 

Incorporating AI tools into my daily operations has significantly boosted my productivity, enabling me to tackle a broad spectrum of tasks with a blend of creativity and analytics. These tools haven’t completely taken over my responsibilities but have improved my ability to execute tasks more efficiently and effectively. 

Below is a list of the AI tools that I use, ranked by their impact and utility in my workflow. While benchmarks for evaluating these technologies exist, my focus here is on sharing personal insights and preferences based on real-world use.

  • Claude 3 by Anthropic. The Claude 3 family includes three models; Haiku, Sonnet, and Opus, each offering progressively enhanced capabilities. This range allows users to choose the best mix of performance, speed, and cost for their needs.
  • Gemini by Google. Google changed Bard into Gemini. It comes in 3 flavours: Ultra, Pro, and Nano.  Gemini sets itself apart with its ability to access up-to-date information through Google Search, providing it with a real-world knowledge base that some other models need plugins for.
  • ChatGPT by OpenAI. The household name for LLMs. ChatGPT gained popularity with its conversational style and ability to generate creative text formats. While  impressive, it’s important to remember that ChatGPT can sometimes provide incorrect or misleading information.
  • pi.ai/talk. A conversational AI tool with an emphasis on providing summaries and factual answers. pi.ai/talk excels at distilling information into easy-to-understand chunks.
  • Elicit. This platform focuses on research assistance. Elicit can help you find relevant academic papers, understand complex concepts, and organise your research notes.
  • Groq. Specialises in working with structured data. Groq’s strength lies in its ability to understand and manipulate information within tables and spreadsheets.

I use the following tools to create visuals, streamline graphic design processes, and create new images from simple text descriptions. 

  • Adobe Firefly 2. Adobe’s creative tool leans towards graphic design and visual creation. Firefly 2’s strengths lie in its integration with Adobe’s suite of products, making it a good choice if you’re already in the Adobe ecosystem.
  • Canva. Known for its user-friendliness, Canva makes graphic design accessible. Its wide variety of templates and ease of use make it a favourite for a quick social media graphic, presentation, and more.
  • Leonardo AI. This tool focuses on image generation and editing with high levels of customization. Leonardo AI allows for fine-tuning of images, making it a good choice for users seeking detailed control.
  • DALL-E 3 (ChatGPT 4). OpenAI’s image generator has gained popularity for its ability to create remarkably realistic and imaginative images from text descriptions. DALL-E 3 sets itself apart in its ability to translate complex ideas into visuals.
  • Pika. Specialising in pixel art generation. Pika’s is good for creating retro-inspired images and graphics for games.
  • Midjourney. Popular for its lifelike and (sometimes) dreamlike output. Midjourney generates artistic images, especially those with fantasy or surreal themes. You will have seen midjourney images without realising. 

Running your own LLM

While accessing large language models through services like ChatGPT/Claude are convenient, there are reasons to consider running your own LLM locally, these include:

  • Privacy & Security: When you interact with a third-party LLM, your prompts and the generated responses may be stored and analysed. Running your own LLM means your data stays entirely on your machine.
  • Customisation: With a local LLM, you can fine-tune the model specifically for your tasks. This leads to more tailored and relevant output.
  • Experimentation: Having direct access to the model allows you to delve deeper into how LLMs work, opening possibilities for research and innovative applications.
  • Cost-Effectiveness (Potentially): While setting up your own LLM requires hardware, in the long run, it might be more affordable than paying for API usage with large providers.

Getting Started with Ollama

One of the easiest ways to run a variety of LLMs locally is with Ollama.ai. Here’s how to get started:

Prerequisites: Ensure your computer meets Ollama’s requirements, including a powerful graphics card (GPU). You can find detailed specifications on their website (https://ollama.ai). You’ll be looking at a top of the range MacBook Pro or a PC with RTX 4090’s and lots of RAM. 

Follow these steps to get started:

  • Download Ollama: Visit the Ollama website and download the installer for your operating system.
  • Installation: Follow the provided instructions to install Ollama on your computer.
  • Choosing an LLM:  Ollama supports various open-source LLMs. Popular choices include Llama 2, Mistral 7B and Gemma. Consider factors like model size and performance when making your selection.
  • Running Your LLM: Launch Ollama and follow its user-friendly interface to load your chosen LLM model and start generating text.

Important Note: Running LLMs locally requires significant computational resources. If your hardware is less powerful, you will experience slower response times or limitations on the size of models you can use.

Context Window

Context window, in the realm of language AI models, refers to the maximum number of tokens (words or word pieces) that a model can process or generate in a single input or output sequence. It determines the amount of contextual information the model can consider when performing tasks such as text generation, translation, or comprehension.

To understand the significance of the context window, let’s first define what a token is. In natural language processing, a token is a unit of text, usually a word or a subword (part of a word). Language models process and generate text using these tokens.

The size of the context window varies among different language models. For instance:

  • GPT-4 has a context window of 32,768 tokens (32K).
  • Gemini 1.5 Pro boasts a larger context window of 128,000 tokens (128K).
  • Claude Opus, developed by Anthropic, has an even larger context window of 200,000 tokens (200K), with the potential to handle up to 1 million tokens (1M) for specific use cases.

To put these numbers into perspective, 200,000 tokens roughly equate to 150,000 words or around 500 pages of text – the length of a typical textbook.

A larger context window allows a language model to understand and generate longer, more complex passages of text while considering a broader range of contextual information. This capability is crucial for tasks such as long-form content generation and maintaining coherence across extended conversations or narratives. By feeding LLMs more information about the task at hand, we can significantly improve the quality and relevance of the generated outputs.

Prompt Frameworks

Both Anthropic and OpenAI have developed comprehensive prompt frameworks that provide guidelines and best practices for interacting with their language models effectively. These frameworks offer valuable insights into crafting prompts that elicit accurate, relevant, and contextually appropriate responses from the models. I highly recommend exploring these resources and incorporating the techniques they discuss when working with their respective models.

For those who are new to prompt engineering or looking for a more straightforward approach, I often use a simpler framework called RTF (inspired by the old Rich Text Format file extension). RTF stands for Role, Task, Format, and serves as a basic template for structuring prompts.

Here’s how the RTF framework works:

  • Role: Define the role or persona you want the language model to assume. This could be a specific job title, a fictional character, or a subject matter expert. By assigning a clear role, you help the model understand the context and perspective from which it should respond.

Example: “Act as a seasoned marketing consultant with expertise in social media strategies.”

  • Task: Clearly specify the task or action you want the language model to perform. This could be generating content, providing recommendations, answering questions, or any other specific output you require. Be as detailed and specific as possible to guide the model towards the desired outcome.

Example: “Develop a comprehensive social media campaign plan for a new product launch.”

  • Format: Describe the format or structure in which you want the language model to present the output. This could include the type of content (e.g., a report, a list, a script), the level of detail, or any specific formatting requirements. Specifying the format helps ensure that the model’s output aligns with your expectations and is easy to work with.

Example: “Please provide the plan in the form of a detailed outline, including sections for goals, target audience, content strategy, platform selection, and success metrics.”

By combining these three elements, you can create a clear and concise prompt that effectively communicates your requirements to the language model. Here’s how the complete prompt would look using the RTF framework:

“Act as a seasoned marketing consultant with expertise in social media strategies. Develop a comprehensive social media campaign plan for a new product launch. Please provide the plan in the form of a detailed outline, including sections for goals, target audience, content strategy, platform selection, and success metrics.”

The RTF framework is a great starting point for prompt engineering, as it helps you organise your thoughts and articulate your needs in a structured manner. As you become more comfortable with the process, you can expand upon this basic template and incorporate techniques from more advanced frameworks like those provided by Anthropic and OpenAI.

Some additional tips for effective prompt engineering:

  • Be specific and detailed in your instructions to minimise ambiguity and ensure the model generates the desired output.
  • Use clear and concise language, avoiding jargon or complex terminology unless necessary for the task at hand.
  • Provide examples or templates when possible to help the model understand the expected format and style of the output.
  • Experiment with different phrasings and prompts to find what works best for your specific use case and the language model you’re working with.
  • Iterate and refine your prompts based on the model’s outputs, continuously improving the quality and relevance of the generated content.

Mastering the art of prompt engineering and leveraging frameworks like RTF, you can unlock the full potential of language models and achieve better results in your projects. Remember, the key is to communicate clearly, provide sufficient context, and guide the model towards the desired outcome.

Uses for AI in an LSP

AI has been making strides in linguistic tasks such as translation and localization, this is not my area of expertise and I am more interested in its potential applications in the language services industry beyond these areas. AI can be a powerful tool for strategic marketing, positioning, and sales training, helping LSPs to better understand their target markets, differentiate themselves from competitors, and equip their sales teams with the knowledge and skills needed to succeed. I’ll provide some ideas and examples below but I’m sure you can think of some of your own too. 

Market Research and Analysis

AI-powered tools can help LSPs gather and analyse vast amounts of data about their target markets, including customer preferences, industry trends, and competitor activities. By leveraging techniques such as sentiment analysis, topic modelling, and predictive analytics, LSPs can gain valuable insights into market dynamics and make data-driven decisions about their marketing and positioning strategies.

Example: An LSP uses AI to analyse social media conversations and online reviews related to their services and those of their competitors. The insights gained from this analysis help the LSP identify key strengths and weaknesses, as well as emerging trends and opportunities in the market. Based on these findings, the LSP refines its marketing messages and develops targeted campaigns to better resonate with its ideal customers.

Competitive Intelligence

AI can help LSPs monitor and analyse their competitors’ activities, including their marketing strategies, pricing models, and service offerings. By staying informed about the competitive landscape, LSPs can make strategic decisions about how to position themselves in the market and differentiate their services from those of their rivals.

Example: An LSP employs AI to continuously track and analyse their competitors’ websites, social media presence, and online advertising. The AI system alerts the LSP to any significant changes or new developments, such as the launch of a new service or a major rebranding initiative. Armed with this competitive intelligence, the LSP can quickly adapt its own strategies and tactics to maintain a competitive edge in the market.

Personalized Marketing and Sales

AI can enable LSPs to deliver highly personalised and targeted marketing and sales experiences to their prospects and customers. By analysing customer data and behaviour, AI-powered systems can recommend the most relevant content, offers, and approaches for each individual, increasing the likelihood of engagement and conversion.

Example: An LSP implements an AI-driven lead scoring and nurturing system that automatically qualifies and prioritises incoming leads based on their likelihood to convert. The system then delivers personalised email campaigns and content recommendations to each lead, tailored to their specific interests and needs. As a result, the LSP sees a significant increase in lead engagement and conversion rates, as well as improved customer satisfaction and loyalty.

Sales Training and Enablement

AI can be a valuable tool for training and enabling LSP sales teams, helping them to develop the skills and knowledge needed to effectively engage with prospects and close deals. AI-powered training platforms can provide personalised learning experiences, adapt to individual learning styles and paces, and offer real-time feedback and coaching.

Example: An LSP deploys an AI-powered sales training platform that simulates real-world sales scenarios and conversations. The platform uses natural language processing and machine learning to analyse each salesperson’s performance and provide tailored feedback and recommendations for improvement. Sales team members can practise their pitches, objection handling, and negotiation skills in a safe and interactive environment, ultimately leading to increased confidence and effectiveness in actual sales situations.

Customer Service and Support

AI can help LSPs deliver faster, more efficient, and more personalised customer service and support. AI-powered chatbots and virtual assistants can handle routine inquiries and tasks, freeing up human agents to focus on more complex and high-value interactions. Additionally, AI can analyse customer feedback and sentiment to identify areas for improvement and inform service enhancement initiatives.

Example: An LSP implements an AI-powered chatbot on their website to handle common customer inquiries and support requests. The chatbot uses natural language processing to understand customer intent and provide relevant information and solutions. For more complex issues, the chatbot seamlessly hands off the conversation to a human agent, along with a summary of the interaction and any relevant context. This AI-assisted approach leads to faster resolution times, increased customer satisfaction, and more efficient use of human support resources.

Financial Analysis of Markets and Companies

AI can assist LSPs in conducting in-depth financial analysis of both target markets and individual companies. By processing and analysing large volumes of financial data, such as market trends, company financial statements, and economic indicators, AI-powered tools can help LSPs make informed decisions about market entry, pricing strategies, and investment opportunities.

Example: An LSP uses AI to analyse the financial performance of potential clients in a new target market. The AI system processes data from various sources, including annual reports, news articles, and industry databases, to assess each company’s financial health, growth potential, and localization needs. Based on this analysis, the LSP identifies the most promising prospects and develops tailored proposals and pricing strategies to win their business.

Value Calculation for Localization

AI can help LSPs demonstrate the value that localization creates for their customers’ businesses. By analysing data on customer engagement, sales performance, and market share across different languages and regions, AI-powered tools can quantify the impact of localization on key business metrics and help LSPs build a compelling case for their services.

Example: An LSP employs AI to analyse the sales and customer data of a client who has recently expanded into new international markets. The AI system compares the performance of localized and non-localized content, products, and marketing campaigns, revealing that localized versions consistently outperform their generic counterparts. Armed with this data-driven proof of localization’s value, the LSP can justify higher prices for its services and secure long-term contracts with the client.

Negotiation Training on Specific Scenarios

AI can play a valuable role in training LSP sales teams to handle specific negotiation scenarios effectively. By simulating realistic negotiation situations and providing real-time feedback and guidance, AI-powered training tools can help salespeople develop the skills and confidence needed to achieve favourable outcomes for both the LSP and its clients.

Example: An LSP creates an AI-powered negotiation training module that focuses on common objections and challenges encountered when selling localization services. The module presents salespeople with a series of interactive scenarios, such as a client questioning the need for localization or demanding steep discounts. As the salesperson engages in the simulated negotiation, the AI system analyses their responses and provides feedback on their performance, suggesting alternative approaches and strategies for overcoming objections and reaching mutually beneficial agreements.

A list of resources I referred to in either this blog post or the presentation. 

Foundational Papers

Thought-provoking Essays

News & Communities

Blogs & Learning Resources

Additional Resources

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Richard Brooks

From the boardroom to the classroom, Richard crafts solutions for businesses worldwide. His MBA insights, combined with his CEO experience, make him a sought-after strategist and captivating public speaker.

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