As companies embrace LLMs, what is the balance between flexibility and risk, and what does that mean for the build-or-buy debate? Although the build or buy dichotomy is a relatively ‘new’ hurdle for technology decision-makers, especially as in-house development teams upskill and build their proprietary solutions, there’s no doubt that the rise of GenAI technologies has taken that conundrum a step further. As organizations grapple with this topic, a fresh layer of decision-making comes into play. Should we be building our own GenAI solutions in-house (and can we?), or is it safer to be buying off-the-shelf products from established vendors?
As things change so rapidly, are we at risk of lock-in at the pace of tech advances, or are we investing huge amounts of resources into keeping up internally? You’ll be shocked to read that the answer, as with most strategic choices, is not clear-cut. I’m going to apply this thinking specifically to the legal sector, where the need for AI is especially prevalent, and the adoption more considered. As in any industry, though, when assessing the build vs. buy decision for LLM solutions, firms must carefully consider their specific use cases, data privacy and security requirements, and long-term ROI. Flexibility is paramount, as the chosen solution must adapt to the firm’s unique needs and integrate effectively with existing systems and workflows. The lure and limits of building in-house GenAI services For companies with more mature software development capabilities, building a custom GenAI application can seem appealing. The APIs underpinning models like GPT-3 are remarkably powerful, enabling developers to quickly spin up basic proof-of-concepts that feel nearly complete. A law firm could connect to the API and prompt it to generate case summaries or first-pass contract reviews. Voilà - a working solution in hours! However, this apparent simplicity belies the reality. Crafting a robust, enterprise-grade GenAI application involves far more than API plumbing. Sensitive data handling, security audits, intuitive UX design, ongoing maintenance as models evolve - the hidden iceberg of work is substantial. For companies lacking seasoned software teams, attempting this undertaking is risky at best. Moreover,  building in-house raises critical questions about data privacy and security. Law firms must ensure their GenAI solutions comply with strict confidentiality requirements and protect client information. Any lapses or breaches could have severe legal and reputational consequences. The convenience and constraints of buying GenAI services On the other end of the spectrum, procuring a subscription to a turnkey GenAI service like Microsoft's Copilot or OpenAI's ChatGPT Enterprise is fast and easy. These generic tools can capably handle a wide array of use cases out of the box, from ideation to writing to analysis. Simply sign up and start prompting. However, one size does not fit all. A lawyer who unwittingly relies on a ChatGPT-generated brief riddled with hallucinated case law precedents could face serious consequences in court. GenAI models are powerful but error-prone, especially when applied naively to specialized domains. Careful human oversight, ideally guided by customizable verification workflows, remains essential. Externally sourced LLMs may also have limitations in terms of flexibility and integrations. Off-the-shelf tools are rarely designed to work seamlessly with a law firm's specific document management systems, CRMs, or bespoke data sources. This lack of integration can hinder adoption and negate efficiency gains. In contrast, custom-built solutions can be tailored to a law firm's exact requirements and orchestrated to plug into existing infrastructure. However, this flexibility comes at a steep development cost and may not be feasible for firms without substantial in-house IT resources. Best of both? Increasingly, forward-looking law firms are seeking a happy medium between the rigidity of pure-play buys and the burdens of bespoke builds. The benefits here include working with partners that have already done the heavy lifting of developing secure, enterprise-hardened GenAI platforms but also providing the API and tooling extensibility to tailor the last mile more specifically. When evaluating potential partners, prioritize those that offer a modular, API-driven architecture, allowing you to leverage pre-built components for common use cases while retaining the flexibility to customize and integrate with your existing systems. This approach enables law firms to start quickly with proven solutions and then iterate and extend as their needs evolve. Increasingly, firms are now looking for partners that aren’t locked into a single LLM or vendor. The GenAI landscape is evolving rapidly, with new models and capabilities emerging all the time. That means law firms need to ensure that they always have access to the most powerful and appropriate language models for their specific applications. What’s the cost difference? In this sort of context, it’s less about the cost differential and more about cost predictability. Cost comparisons between building and buying can be complex, as hidden expenses often lurk beneath the surface. In-house development may require significant upfront investment in infrastructure, talent acquisition, and training, while ongoing maintenance and updates can add unpredictable drains on IT budgets. Conversely, SaaS subscriptions may have lower startup costs but can accumulate substantial fees over time, especially as usage scales. CXOs should carefully model the total cost of ownership for both options over a multi-year horizon, factoring in both hard costs and soft considerations like time-to-value, opportunity costs, and reputational risks. Engaging an experienced partner to guide this analysis can provide valuable perspective. Today’s middle ground has also entered consumption-based pricing into the arena, helping law firms avoid the sticker shock of upfront licenses and better align costs with realized value. The ability to start small and scale usage as adoption grows is key to maximizing ROI and minimizing risk. Bridging the gap We are all sadly familiar with the risks of vendor lock-in and grinding in-house builds, but conversely, we may well have had positive experiences of both. The point is that there’s no one-size-fits-all approach and no decision flow that points to ‘build’ in one context, ‘buy’ in another, or ‘both’ in the third. The modularity enabled by the API-driven nature of modern GenAI opens the door to composable solutions that blend the best of buy and build. With the right decisions, companies can stay agile, avoiding over-investment in bespoke tech that may rapidly become outdated in the fast-moving GenAI space, while still tailoring capabilities to their unique needs. With such rapid advances in technology, being first-movers in this profession can be risky, and organizations tend to counter the rush to act with the cost of delay. In the long run, this cost can mount far higher than first thought. We’re navigating uncertain plains at the moment, and the key is to chart a balanced course, working with partners that will elevate your capabilities and not hinder them. Flexibility, extensibility, and strategic integration are the pillars of GenAI's effectiveness today. As law firms unlock the potential of LLMs to drive efficiency and competitive advantage, they can combine the ease and speed of pre-built foundations with the flexibility and control of customizable components. The GenAI world is advancing by the second, with decisions now centered primarily on minimizing risk and maximizing ROI. As companies embrace LLMs, what is the balance between flexibility and risk, and what does that mean for the build-or-buy debate? As companies embrace LLMs, what is the balance between flexibility and risk, and what does that mean for the build-or-buy debate? Although the build or buy dichotomy is a relatively ‘new’ hurdle for technology decision-makers, especially as in-house development teams upskill and build their proprietary solutions, there’s no doubt that the rise of GenAI technologies has taken that conundrum a step further. As organizations grapple with this topic, a fresh layer of decision-making comes into play. GenAI Should we be building our own GenAI solutions in-house (and can we?), or is it safer to be buying off-the-shelf products from established vendors? As things change so rapidly, are we at risk of lock-in at the pace of tech advances, or are we investing huge amounts of resources into keeping up internally? Should we be building our own GenAI solutions in-house (and can we?), or is it safer to be buying off-the-shelf products from established vendors? As things change so rapidly, are we at risk of lock-in at the pace of tech advances, or are we investing huge amounts of resources into keeping up internally? You’ll be shocked to read that the answer, as with most strategic choices, is not clear-cut. I’m going to apply this thinking specifically to the legal sector, where the need for AI is especially prevalent, and the adoption more considered. As in any industry, though, when assessing the build vs. buy decision for LLM solutions, firms must carefully consider their specific use cases, data privacy and security requirements, and long-term ROI. Flexibility is paramount, as the chosen solution must adapt to the firm’s unique needs and integrate effectively with existing systems and workflows. The lure and limits of building in-house GenAI services The lure and limits of building in-house GenAI services For companies with more mature software development capabilities, building a custom GenAI application can seem appealing. The APIs underpinning models like GPT-3 are remarkably powerful, enabling developers to quickly spin up basic proof-of-concepts that feel nearly complete. A law firm could connect to the API and prompt it to generate case summaries or first-pass contract reviews. Voilà - a working solution in hours! software development However, this apparent simplicity belies the reality. Crafting a robust, enterprise-grade GenAI application involves far more than API plumbing. Sensitive data handling, security audits, intuitive UX design, ongoing maintenance as models evolve - the hidden iceberg of work is substantial. For companies lacking seasoned software teams, attempting this undertaking is risky at best. Moreover,  building in-house raises critical questions about data privacy and security. Law firms must ensure their GenAI solutions comply with strict confidentiality requirements and protect client information. Any lapses or breaches could have severe legal and reputational consequences. The convenience and constraints of buying GenAI services The convenience and constraints of buying GenAI services On the other end of the spectrum, procuring a subscription to a turnkey GenAI service like Microsoft's Copilot or OpenAI's ChatGPT Enterprise is fast and easy. These generic tools can capably handle a wide array of use cases out of the box, from ideation to writing to analysis. Simply sign up and start prompting. However, one size does not fit all. A lawyer who unwittingly relies on a ChatGPT-generated brief riddled with hallucinated case law precedents could face serious consequences in court. GenAI models are powerful but error-prone, especially when applied naively to specialized domains. Careful human oversight, ideally guided by customizable verification workflows, remains essential. Externally sourced LLMs may also have limitations in terms of flexibility and integrations. Off-the-shelf tools are rarely designed to work seamlessly with a law firm's specific document management systems, CRMs, or bespoke data sources. This lack of integration can hinder adoption and negate efficiency gains. In contrast, custom-built solutions can be tailored to a law firm's exact requirements and orchestrated to plug into existing infrastructure. However, this flexibility comes at a steep development cost and may not be feasible for firms without substantial in-house IT resources. Best of both? Best of both? Increasingly, forward-looking law firms are seeking a happy medium between the rigidity of pure-play buys and the burdens of bespoke builds. The benefits here include working with partners that have already done the heavy lifting of developing secure, enterprise-hardened GenAI platforms but also providing the API and tooling extensibility to tailor the last mile more specifically. When evaluating potential partners, prioritize those that offer a modular, API-driven architecture, allowing you to leverage pre-built components for common use cases while retaining the flexibility to customize and integrate with your existing systems. This approach enables law firms to start quickly with proven solutions and then iterate and extend as their needs evolve. Increasingly, firms are now looking for partners that aren’t locked into a single LLM or vendor. The GenAI landscape is evolving rapidly, with new models and capabilities emerging all the time. That means law firms need to ensure that they always have access to the most powerful and appropriate language models for their specific applications. What’s the cost difference? What’s the cost difference? In this sort of context, it’s less about the cost differential and more about cost predictability. Cost comparisons between building and buying can be complex, as hidden expenses often lurk beneath the surface. In-house development may require significant upfront investment in infrastructure, talent acquisition, and training, while ongoing maintenance and updates can add unpredictable drains on IT budgets. Conversely, SaaS subscriptions may have lower startup costs but can accumulate substantial fees over time, especially as usage scales. CXOs should carefully model the total cost of ownership for both options over a multi-year horizon, factoring in both hard costs and soft considerations like time-to-value, opportunity costs, and reputational risks. Engaging an experienced partner to guide this analysis can provide valuable perspective. Today’s middle ground has also entered consumption-based pricing into the arena, helping law firms avoid the sticker shock of upfront licenses and better align costs with realized value. The ability to start small and scale usage as adoption grows is key to maximizing ROI and minimizing risk. Bridging the gap Bridging the gap We are all sadly familiar with the risks of vendor lock-in and grinding in-house builds, but conversely, we may well have had positive experiences of both. The point is that there’s no one-size-fits-all approach and no decision flow that points to ‘build’ in one context, ‘buy’ in another, or ‘both’ in the third. The modularity enabled by the API-driven nature of modern GenAI opens the door to composable solutions that blend the best of buy and build. With the right decisions, companies can stay agile, avoiding over-investment in bespoke tech that may rapidly become outdated in the fast-moving GenAI space, while still tailoring capabilities to their unique needs. With such rapid advances in technology, being first-movers in this profession can be risky, and organizations tend to counter the rush to act with the cost of delay. In the long run, this cost can mount far higher than first thought. We’re navigating uncertain plains at the moment, and the key is to chart a balanced course, working with partners that will elevate your capabilities and not hinder them. Flexibility, extensibility, and strategic integration are the pillars of GenAI's effectiveness today. As law firms unlock the potential of LLMs to drive efficiency and competitive advantage, they can combine the ease and speed of pre-built foundations with the flexibility and control of customizable components. The GenAI world is advancing by the second, with decisions now centered primarily on minimizing risk and maximizing ROI.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

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Hacking GenAI Integration: Build, buy or both?

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Dawid Kotur

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Hacking GenAI Integration: Build, buy or both?

Too Long; Didn't Read

Dawid Kotur

STORY’S CREDIBILITY

Opinion piece / Thought Leadership

AI-assisted

About Author

TOPICS

THIS ARTICLE WAS FEATURED IN...

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