Technology

The Crucial Role of Leaders, Lawyers & Marketers in Gen AI’s GTM Strategy

9 Mins read

Vinit Tople - Former Product Leader in Big Tech with experience building AI powered consumer facing products. 

The Consumer Electronics Show 2024 in Las Vegas was abuzz with one overarching theme: AI. It marked the culmination of a year filled with AI powered prototypes across industries, showcasing the remarkable advancements in AI through Large Language Models (LLMs). The spotlight quickly shifted to the next step – the path from the prototypes to real products. While the prototypes demonstrated the magical power of LLMs, this final step to the real product is ridden with landmines – the risks and the harms of LLMs with all their inherent unpredictability. This article aims to 1) shed light on these challenges, 2) outline common approaches to address them, and 3) propose recommendations with supporting rationale. But let’s start with some background first.

Background

Traditional software programming faced limitations in tasks involving natural language processing (NLP). Examples included customer support via chatbot, product feedback analysis, voice- operated smart home, language translation for global websites, among others.  Although traditional AI addressed these, it struggled with complex conversations, understanding only short and simple sentences. (e.g. how do I cancel my order or turn on the porch light). The advent of LLMs in 2022-23 bridged this gap, almost achieving the elusive goal of true natural language understanding. Suddenly, longer sentences and even sentiments were being understood by AI, driving widespread excitement. But then, the limitations came to the fore soon. The LLM behavior was inconsistent. While it would understand sentences you didn’t expect it to, it would fail to understand sentences you expected it to. The unpredictability was jarring. Secondly, LLMs could even generate content (not just understand), but the generative ability is a double-edged sword. When it’s leveraged for writing poems or songs, it is hailed as creativity but when answering a legal/medical/business question, the same creativity came to be known as hallucination (making stuff up). That’s not all because there were other issues. The whole of the internet was used to train the model so there were concerns of copyright violation. Interactions with LLMs were being used to train/refine them further so that raised privacy concerns. In short – the list of yellow and red flags was long. 

The Catch-22

All the challenges aside, there is no denial that tremendous value lies in the LLMs. There is something on offer for every company and for a multitude of use cases.  The industry is brimming with ideas to unlock it – from productivity gains to process automation to analytics to novel customer facing features to innovative business ideas. The key challenge here is that the significant value of LLMs comes as a package deal with a non-trivial bundle of risk. That leads to the catch-22, which most leaders and companies are grappling with – not adopting the technology presents a serious opportunity cost and adoption is akin to walking into an area with landmines. 

Industry response

Industry responses to this dilemma vary based on a variety of factors. While some companies opted to observe from the sidelines, awaiting technological maturity, others decided to actively engage with LLMs. Among the latter, two predominant approaches emerged and that is the focus of this article. 

The first approach centers on establishing core LLM teams comprising of data science, engineering, and product functions only. The predominant focus of this approach is to reduce the risks and limitations of LLMs. Efforts include injecting ethics into LLMs, reducing bias, mitigating hallucination, validating knowledge, tightening privacy controls, customizing LLMs, and integrating with IoT systems. All of that is a valuable investment, even essential for the most part. However, what’s conspicuous by their absence (or at least the absence of active participation) is the role of functions beyond the core team. Beyond these core functions of science, engineering and product, the rest (legal, sales, marketing, operations etc.) are largely playing passive roles, ready to support as and when the core team is ready to engage them. By ‘ready’, the expectation being that the risks and limitations are brought down to acceptable levels. At the surface, this approach of ‘fix-the-issues-first-before-involving-others’ sounds reasonable, but is it? Some companies are looking at it differently and that’s the second approach. 

The second group has realized that waiting for the core team to bring the risks/limitations down to traditionally acceptable levels is not a pragmatic approach. The resolution or even mitigation of these issues down to the traditionally acceptable level could take a very long time. That’s just how complex these issues are. These are issues such as removing bias, injecting ethics, hallucination mitigation etc. They have also realized that the LLM builders (e.g. Big Tech and open-source communities) are better equipped to make progress on these exceedingly difficult challenges than their in-house teams, which are much smaller. Also, any improvements at the source (Big Tech/open-source community) will trickle downstream to them anyway. In other words, attempts to solve these foundational problems of LLMs with in-house resources has the risk of being a redundant effort or a ‘throw-away’ piece of investment. Recognizing that, these companies have chosen to focus their attention on aspects which only they can and need to solve. This includes steps specific to the company’s use cases like custom prompts, training with proprietary data, data validation against relevant sources, API integrations, relevant context injection, among others. While the focus on only the company-specific issues is one difference from the first approach, the second and the more notable difference is the presence of workstreams beyond science/engineering. That includes the efforts to craft a Go-To-Market (GTM) strategy, specific to their use case(s) in parallel to all the science and engineering efforts. In other words, these companies have adopted a multi-pronged or parallel approach (vs. serial) from the very outset, engaging functions beyond just the core ones. These companies have realized that ‘traditionally acceptable’ state is not a very practical proposition, at least in the short term. Instead, non-traditional paths need to be found beyond just science and engineering; it will take a village to extract value safely from this precocious but perilous technology. More specifically, their leaders, in partnership with the legal and marketing functions, are working alongside the core team to carve a way through these imperfections and even risks. 

The Right Approach

Like most decisions, there is no one-size fits all approach. However, there is good reason to hypothesize that the biggest success stories in the LLM adoption space will come from companies that recognize the difference between the two approaches above and choose the second – the multi-pronged approach from the outset. In fact, most companies in the first group will soon realize that while they kept applying traditional benchmarks of acceptance, a lot of competitors garnered large gains in the meanwhile by accepting the imperfections and working through them. It’s all just a matter of when this realization arrives, and its timing can be the differentiator in this dizzyingly fast race. 

More on the multi-pronged approach

AI will penetrate all the functions of any organization and the multi-pronged approach helps in building the muscle needed to adapt to this massive and imminent change. LLM powered solutions should not be left to the few scientists and techies. Every function will need to adapt and reinvent their roles. Taking the multi-pronged approach for determining the GTM strategy for the prototypes is the first step in that direction and could provide a competitive edge in the medium to long term. More specifically for now, Leaders, Lawyers and Marketers have their task cut out. While the specifics of what these roles need to change or do will vary by situation, below are some thought starters on how these roles will have an elevated significance in the AI era.  

Marketing needs to recognize that one aspect of their role has an outsized importance in the world of LLMs – it’s to position the product right, in terms of setting realistic expectations or even lowering certain expectations. Resisting the urge to sugar-coat limitations is imperative. Instead, transparency regarding product limitations can facilitate adoption rather than hinder it. That’s because the opinions about LLMs have oscillated too dramatically and too quickly, from fervent excitement to deep concern – often leading to excessive caution. A transparent and honest positioning will be a refreshing perspective and alleviate undue skepticism and apprehension, where appropriate. It can also be the differentiator between an acceptable level of backlash vs. outright rejection or even elevated legal risks. ChatGPT is a prime example of getting this just right. The product positioning (though not explicitly stated) was – “if you want the latest, most accurate information and total privacy, wait for others. In the meanwhile, for everything else, you have us. But use at your own peril.” This product positioning was an integral enabler of ChatGPT’s path to market despite the imperfections and risks. The rest, as they say, is history.

Legal teams are not mere support functions in the era of Gen AI. Like marketing, they will play a key role in any LLM powered product’s GTM strategy because the legal implications are one of the biggest holdups on that front. Many LLM powered products linger in prototype stage unresolved legal issues – who owns liability when the product misfires, where are the lines of accountability, privacy, and data protection concerns, could LLM responses hurt your brand, copyright infringement and so on. Many of these are legitimate concerns unique to LLMs. However, there also are questions, unnecessarily jumbled up with LLMs although they’re just a variation of existing concerns. For example, there is fear that giving data to LLM builders (e.g. Big Tech) for model training could result in the data being available to your competitors. That’s not an LLM specific problem – this concern exists even when companies move their IT infrastructure to the cloud or share data with partners. There are answers for this – contractual and technical. Jumbling these issues together is not helpful. Differentiating between these issues is crucial to focus on challenges truly unique to LLMs, a task in which legal functions play a pivotal role. However, to do this effectively, legal professionals must educate themselves on AI and LLMs extensively, stay updated on developments, rethink their approaches, and find the paths forward. Even then, their task – to craft T&Cs for products powered by an unpredictable technology – will remain extremely challenging. However, without this legal contribution, the value in the LLMs and the subsequent opportunity from it will remain trapped. Consequently, though an incredibly challenging task, it’s also a once-in-a-lifetime opportunity to play a differentiating role in this revolutionary technology.

Leaders have already been navigating the complexities of the AI revolution, tackling the macro questions for their organizations – what’s in it for us, how do we prepare/contribute and so on. While many leaders have authorized and supported the necessary investments, the savvy ones are one step ahead. Along with the resource investments, they are spearheading cultural changes in their organizations that this new world requires. First cultural change needed is to embrace and normalize ‘trial and error’ as integral to product strategy development, a practice largely limited to only R&D organizations until now. It is a departure from traditional practices where product specs were largely a function of what business and product teams laid out while engineering built it. With LLMs, there is a new brain in the room and what it can and wants to do can be discovered mostly through trial and error, at least as of now. This process is a much larger determinant of the final product than traditional processes. Second, leaders must establish a robust framework for risk assessment across all organizational levels. While traditionally, risk assessment relied on subjective judgment from leaders and lawyers, the complexity of risks associated with LLMs demands a more decentralized approach due to the sheer volume of it. That said, leaving it to a broad swathe of those on the assembly line (engineers, scientists and product managers) will also lead to issues. To ensure consistency in risk evaluation, there is a need for top-down guidance and support. Lastly, when these risks become reality and some setbacks occur, leaders must demonstrate unwavering support and solidarity. Their teams need that air cover to function effectively on this shaky ground. 

Conclusion

The flurry of LLM powered prototypes in 2023-24 demonstrated the enormous power of LLMs. But the vast chasm between the number prototypes and actual products revealed the gravity of challenges and risks in that final step – prototype to real product. While investment in the resolution or mitigation of these risks is essential, that itself is not enough.  Companies need to acknowledge that a significantly elevated level of uncertainty, risk and ‘trial and error’ are the new normal and here to stay. Consequently, the work to function in the new normal and get products out in the market even in the presence of some risks/imperfections must begin earnestly too. That will call for innovation not just in technology but also in key functions beyond the technical ones. Leaders, Lawyers, and Marketers (coincidentally another ‘LLM’) are that next line of attack and have their task cut out to define the GTM strategy. The answers they need to come up with aren’t clear yet and may not be for a while. However, these functions will need to begin organizing themselves to be effective in this new paradigm and this will take time. The individuals, leaders and companies that invest in this aspect upfront will build the necessary muscle and reap the rewards sooner than others.

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