Credal.AI Blog The AI Enterprise Adoption Curve: Lessons Learned So Far

At Credal, we've seen a lot of enterprises go through their AI adoption journey. We thought we'd write down some of our observations, to help answer questions likeWhat does the typical AI enterprise adoption curve look like in regulated enterprises?What are the key use cases?What decisions do enterprises have to make and what should shape those decisions?What are the common sticking points / challenges enterprises face?What Adoption Looks Like In PracticeLet's take the example of one of our customers, a background checking provider. This diagram shows a (schematic) representation of what their adoption cycle looked like:As you can see, there are three distinct stages here:Early experimentation (AI Taskforce)At this stage, the company is still evaluating what to do and the main goal is learning. There's an early spike of excitement, hence lots of users, which quickly levels off.Early users include the CISO, AI Engineering Leads, some early adopters in OpsProduct used: vanilla Chat interface, no Docs connectionChat with Docs workflowsThis typically begins after a security audit has been passed and API integration has been set up. We can talk about two phases here:Chat on non-sensitive docsThe company connects some lower-security documentation to the platform to be able to do basic "Chat with Docs" type workflowsFor example, you can ask questions about HR benefits, or search Github IssuesUsage broadens somewhat, but still relatively narrowChat on Internal/Sensitive DocsAI is more securely integrated into the company and this includes more sensitive docs (e.g. feedback/performance data, answering compliance questionnaires etc). The company is more comfortable with broadening AI usage, has a firm security policy/posture, has done relevant compliance checks...Enterprise SearchUsers can ask any question at all that draws on company data and automatically get a response from the most relevant specific company data.Access controls are rigorously enforced in near-real time across multiple sourcesCore Operations WorkflowsThis is where the company begins to broaden beyond chat workflows and integrate AI into their core business processes.An example is the Gathering Feedback From Sales Calls workflow outlined here; another example is the AML/KYC workflow outlined here.Agent workflows, copilots, and LLM orchestration come into play here.AI adoption broadens to beyond chat completion, including code execution, data retrieval and moreIt begins with tremendous excitement, typically driven by executives or an AI task force. A handful of engineers experiment with open-source libraries, maybe try a vector database, and use third-party LLMs to prototype some workflows. As soon as they want to move that prototype into production, several common challenges rear their head: privacy, security and compliance when integrating company data on the one hand, reliability and maintaining high quality performance over the long tail of production queries on the other. Along the way, engineers encounter common challenges and questions, such as how best to implement ingestion and retrieval, whether fine-tuning actually helps, how the open source models compare to the proprietary models, which use cases will really work, should they try and stay model-agnostic or just go all in on OpenAI and on and on...Other observationsWhat can we learn from this? In practice, enterprises seem to go with one of two AI strategies:Ban all external tooling and try provide a specific sandbox that employees can use;Provide a set of tools experimentally that are integrated with your data, and guidelines about appropriate use, and allow employees to discover use cases that work with those toolsThe above path is a (successful) example of strategy #2.With strategy #1, companies will usually end up building their own internal wrapper around Azure OpenAI. Most of these wrappers will be straightforward - an integration with Slack/Teams that automatically logs all queries into a central place where IT can see what's going on. Around 260+ companies chose to buy ChatGPT Enterprise, but many of the customers with Enterprise accounts *also* have their own wrappers built on Azure as well, since ChatGPT Enterprise costs a lot (our customers have been quoted $40-$60 per user per month) for access to Enterprise friendly Chat. Businesses doing this will find their employees able to quickly adopt classic "chat" workflows. But ultimately, getting real value out of generative AI is all about equipping it with your data, so just giving your company a chat UI does not end up accelerating the business much: chat, by itself, is not that helpful.With strategy #2, you get more experimentation, innovation, and discover the use cases that actually matter. But, as mentioned above, enterprises end up struggling with how to solve a whole host of issues that come with the generative AI territory. This is where many companies are today. Over time you might see a few more sophisticated use cases; for example, using LLMs to accelerate KYC/ML processes, or parsing sales calls for customer feedback, or even end up having LLMs power core parts of your product.In tech companies we've worked with, we tend to see roughly 50% of the organization using AI tooling within 8 weeks of adopting a tool, and 75% after a year, if things go well. We don't have much data beyond a year since the technology is still so new, but it stands to reason that those numbers should just increase!Key use casesHow have we seen AI deployed in the enterprise, in practice?Engineering: Engineering teams are using LLMs both to accelerate their own productivity, but also to deliver new types of features in the core products that they ship. Engineering productivity use cases focus on coding assistants, the most popular of which are Github Copilot, Sourcegraph Cody, and Codeium.But we've also seen Engineering teams use LLMs behind the scenes in core product functionality they provide to their customers: today, LLMs are driving things like receipt matching, automated employee background checks, and more.Operations and FinanceAI finds significant application in automating processes such as Anti-Money Laundering (AML) / Know Your Customer (KYC) and Transaction Monitoring checks, receipt matching, and accounting. Legal and HR:Contract negotiationsAnswering customer privacy and compliance queriesHelping customers understand their HR benefits management.Marketing and Sales:Transcription, notes synthesis, and copywriting / comms workflows. What decisions do enterprises have to make and what should shape those decisions?1) Build vs. Buy?For core business processes, building in-house and owning the logic is key. A mature fintech firm might build its AML/KYC solutions to leverage its unique proprietary data; AML/KYC are core to the business. Or if you're a social network, you want to own the logic for doing AI-driven recommendations, and it's best to implement that yourself (using APIs as needed) rather than procuring this from a third-party. It's important to develop in-house expertise at AI; AI will be important, and having that expertise in your business is worth it; but point that effort at workstreams that are central/core to your business, and procure the rest.It can seem highly appealing to build everything yourself, but we think that's a mistake.For certain products, like call transcription and coding assistants the market has very mature offerings at a variety of price ranges, and these will likely function at least as well as something you build in house, but for a fraction of the expense and available to use today, and putting immense developer resources behind building a coding assistant that can beat Github Copilot, Sourcegraph's Cody, or Codium, is for most businesses unlikely to have positive ROI.Similarly, if you want to have an enterprise search product at your company, it's best to just go buy the one from the market, which has a range of mature options that can match most budgets. The maintenance burden of doing these in-house is usually too high.Another way you can look at this is what stuff do I need control over vs. what can I delegate? For example, do I need an opinionated chunking strategy for my use case or can I leave it to the provider? What about reranking, retrieval strategy, etc? Again, our view is that it depends on the use case: if core to your business, you need to own all the decisions; for basic use cases, just buy.2) Platform vs. Point SolutionsWe're opinionated about this one: we think for most use cases you should buy a platform for your core AI workflows, not point solutions. The problem with buying point solutions is that models update fast; what was the leading edge yesterday is not the leading solution today. So you need something that can adapt very fast, and is changeable and customizable to your needs. Yes, there are some concrete point solutions that people should just buy - Github Copilot for coding is a good example - but for almost everything else you want to buy a platform that makes it easy to configure/build AI solutions for your business and takes care of the things you don't want to worry about. It's also important that non-technical users can do them for themselves too; you don't just want to buy a developer platform, you want something that everybody at your company can use to experiment. 3) Single LLM Provider vs. Multi-LLM StrategyThis is about whether you should go with a single provider, such as Google, OpenAI, or Anthropic; or whether you should diversify. In short, it's important to have a multi-LLM strategy.One interesting observation in this regard: smaller companies tend to be more willing to lock themselves into a single LLM provider. The larger/more sophisticated the company, the less likely they are to want to do that. A related question is whether to go closed or open source. The LLM ecosystem is evolving fast so this could change anytime, but for now it seems that the leading fr