From Prompts to Platforms: The Journey of AI-Enabling Business Processes
It's apparent that many of the companies that have tried to adopt AI in the past few years, haven't had the success they were hoping for. There's often initial promise, followed by a Wylie-Coyote-style fall off the AI cliff.
Coming from 20+ years working with AI/ML systems, one important reason for this is clear: Adopting AI is never as simple as introducing a new tool. It's a journey that touches the whole organization, from needing to clarify strategic goals, to understanding and updating processes, to building a platform/infrastructure layer to support the technology.
This piece is about that journey: what it means for companies, what questions to ask yourself and your colleagues, and what to watch out for to make your path as smooth as possible.
First, Clarify your Goals
Especially using AI and automation for efficiency (either internally or in a SaaS product), you need to carefully think through the fundamentals - what pain do people have, how are you solving that pain, and, do you need AI to solve it?
Why? Because AI tools are inherently non-deterministic, meaning that they look for patterns and apply them. They all "hallucinate" when the patterns they have uncovered don't apply correctly 100% of the time (which they never do). That means these systems are more expensive and complex to use for business operations than standard, deterministic software. If you apply the slight randomization introduced by ML/AI systems to your (or your customers’) critical business processes, it will be a disaster if you don't know what you're trying to accomplish.
One example: At a machine learning company I worked at, we had a system that was applying algorithms to essentially sift through very large data sets and propose actions to our customers, some of which could be fully automated in workflows (including by maintaining state and chaining automations together).
It wasn't working very well. The error rate was high, the proposed actions were often not correct, and overall, it needed enough hand-holding that it wasn't realizing the promise of streamlining operations and reducing manual effort.
To fix this, we first ripped out the machine learning to make sure we had the business process right for our customers before trying to make it "smart." During that process, we realized that we hadn’t fully understood the goals and current processes, which was making it very hard to use ML effectively. Once we completed this process and added ML back in strategically, the whole system was much more effective because we were using the right tools for the job, and, we knew what exactly the job was.
Here are some questions to ask yourself and your colleagues:
Are you sure you know what problem(s) you’re trying to solve?
Are there multiple ways to solve the problem? If so, which might be simplest?
Does it need AI, or are there other, deterministic, ways to approach the problem?
How sensitive is this problem from a business perspective? Can you manage the risk?
Second, Take an AI-First Perspective
Using AI requires a different way of thinking about processes than more standard approaches do. This is because of the non-determinism (discussed above), and the fact that often when we are adopting AI for business processes, we intend for it to operate in a largely automated fashion, which means accuracy at scale is key.
There have been a lot of reports of companies giving employees generative AI tools hoping for efficiency gains, just to get complaints about overwork and difficulty getting benefit from the tools instead. The reason behind this is clear: it's because in order to use AI effectively, the process has to change. It's not the case that employees can just use AI and automatically reduce their workload.
This means you need to fully understand the processes you're automating, determine where AI will have the most impact, and then decide how you will automate overall; where you will use AI, how you will control quality, where you will have humans in the loop, etc.
Also, AI needs data. If you want to run business processes with AI, you need to make sure the system can get the data it needs, it can keep state if needed, the outputs end up in the right places, and that the appropriate quality controls are in place, based on business sensitivity. Understanding these details will help you determine what kind of technical solution you need and how robust it has to be.
I've gone through the full "AI-ification" of large-scale business processes end-to-end twice (multi-year projects) and consulted on several other such projects. In both the E2E cases, we had to map out the steps in all processes to make flows more explicit and include details about data and where humans fit in. Then there was tweaking to really get things working. This can take a lot longer than you think (many months in some cases).
The end products were much more streamlined with better throughput, but it took time to get it right. If you just give AI to your team and tell them to go for it, results will definitely vary based on the people you have using it. If you want consistency and real team-wide efficiency, that takes process design.
Questions to ask:
Do we know how we would (or do) solve it manually?
Do we know (exactly) what data we need and where it is?
Do we need to create outputs that will be used in other inputs, or that will need to be inspected by humans?
Do we know what controls (compliance, risk, etc.) we currently have in place? Will this be sufficient for automation and AI?
Third, Understand the Technology Options
There are lots of different ways and degrees to use AI. And a lot of them require at least some level of "platform." By platform here, I mean the technology used to move data around, build and run models, drive automation, manage quality, and enable human-AI interfaces. Essentially, the infrastructure making the AI-ificiation of the business processes possible.
There are a number of ways to approach this. First, before making technology decisions there are some questions to ask:
Is this process business critical? If so, you need a solution with better scalability and robustness.
How important is accuracy? Does it need to be 100% correct, or is 80-90% okay? If it needs to be above, say, 85% on average, you also need to put rules and quality controls into place.
How many people will be involved and at what stages of the process? You have to explicitly plan for people in the system, which affects technology decisions.
How much and how often does the process change? Is it very static, or does it require flexibility? The more flexibility you need, the more "platformy" your solution needs to be.
How fast and how frequent does it need to be? Does it run every day? Every second? How long can you afford to wait for results? The faster it needs to be, the more "platformy" you need to be.
Once you understand this, you can start thinking through the options. There is essentially a spectrum, from simple human prompt usage, to Airtable or similar tools that loosely chain together tools and people, to more automated and robust AI orchestration platforms.
There's a lot to learn here, and it deserves it's own piece, but the main point is that you have to understand what you're trying to do before you can choose an approach. And the available approaches vary a lot in terms of implementation time, cost and capabilities. Basically, the more fast, robust, business-critical, automated, and frequent a process needs to be, the more you need a robust platform-based solution.
In any case, this is usually a crawl-walk-jog-run adoption path, as opposed to just going all-in on a platform from day one.
Finally, Plan for the Crawl-Walk-Jog-Run of AI Adoption
Most successful AI adoptions I've seen follow a pretty predictable pattern. Companies that try to skip steps usually end up back at the beginning, just with less budget and more skeptical stakeholders.
Crawl: Individual Usage and Experimentation This is where most companies start - and where many get stuck. People use ChatGPT or Claude for individual tasks. Maybe you set up some team subscriptions. You run a few hackathons. Some people love it, others don't see the point. Usage is inconsistent and results vary wildly based on who's using the tools and how skilled they are at prompting.
This stage is valuable for building familiarity and identifying where AI might help, but it's not where you'll see real business impact. The key here is to capture what you learn and start identifying patterns in where AI is actually helpful versus where it's just novelty.
Walk: Process Integration and Tooling This is where you start building AI into specific business processes. You might create custom prompts for common tasks, build simple workflows that combine AI with human review, or integrate AI tools directly into your existing software stack.
At PURE, we built a VS Code plugins that help with code review, commit messages, and unit testing. And we adjusted our development processes to use these tools in a consistent way across the organization. We made adoption into a project, as opposed to just making the tools available.
The goal here is to reduce friction and increase consistency. Instead of everyone figuring out their own approaches, you're creating shared tools and processes that work the same way every time.
Jog: Systematic Process Redesign This is where you start actually changing how work gets done, not just adding AI on top of existing processes. You map out your (or your customer’s) workflows, identify where AI can take over certain steps entirely, and design quality control mechanisms to catch errors.
This stage requires real investment - in time, people, and often technology infrastructure. You're not just using AI, you're rebuilding processes around it. But this is also where you start seeing real efficiency gains and business impact.
Run: AI-Native Operations This is the end state that most companies are aiming for - where AI is deeply integrated into how the business operates. Processes are designed from the ground up to leverage AI capabilities. Quality control is automated. Human oversight is strategic rather than tactical.
Few companies have actually reached this stage yet, partly because the technology is still evolving, and partly because it requires fundamental changes to how organizations work.
What Success Looks Like
Successful AI adoption doesn't look like replacing humans with robots. It looks like humans and AI working together in ways that leverage the strengths of both.
The companies I've seen succeed focus on:
Clear, measurable goals for what they want AI to accomplish
Deep understanding of their current processes before trying to change them
Systematic approaches to technology decisions and quality control
Patience with the learning curve - both technical and organizational
The AI adoption cliff is real, but it's avoidable. The key is treating AI adoption as an organizational capability you're building, not a technology you're deploying. Take the time to do it right, and you'll get results that actually matter to your business.