Author: Sandra Carrico, Vice President Engineering & Chief Data Scientist at GLYNT.AI, a machine learning solution for retrieving unstructured data from documents. Sandra is a member of the Forbes Technology Council. This post originally appeared on Forbes.com
Today, most enterprise companies are getting started with artificial intelligence (AI). And with AI talent so scarce, your top team will often breathe a sigh of relief when the first AI hires are made. But in my experience, it takes a highly skilled team of non-AI software engineers to build out the engineering infrastructure and product features that transform AI from a proof-of-concept to a trusted product that delights users. In fact, we estimate our company spent 15 times more on the non-AI elements of our product than the AI itself.
We started building our AI product three years ago. Over those years, we built out the product features needed to make AI easy to adopt. Here’s how our spending added up:
AI Infrastructure: 11 Times The Cost Of An AI Expert
The costliest challenge we had to confront was the software system that moves data in and out of our application — and organizes the library of machine learning models. Every AI system needs this functionality, which is known as the orchestration and pipelining system.
Because the orchestration and pipelining system is fundamental to the performance of the AI product, we looked for suitable existing tools on the market. There was a mismatch between the tools for fairly simple AI models and solutions and our complex use case. Reluctantly, we built our own, one that could manage our complex AI solution while instantly scaling up or down with the load at hand, process data quickly and keep our customers’ data secure.
AI Access Aia API: The Cost Of One AI Expert
AI is a means to an end, producing better data, insights, heuristics, pattern matching or faster processing than traditional software. Enterprise AI solutions must be nestled into business workflows and connected to what comes before and after. We found much faster acceptance of our AI product once we offered APIs which simplify these connections.
With a published API, our customers simply integrate the service. Ironically, they need no AI expertise, but through the API connection, their company gains the power of an AI-solution. At our end, the team spent long months preparing the API.
In a recent blog post, Mark Zuckerberg highlighted the importance of APIs in his personal AI project, and to no surprise, companies that that ease API connections are highly valued. Apigee was purchased by Google for $625 million in 2016, and last year, Salesforce bought Mulesoft for 10 times that amount, $6.25 billion.
Data Protection For Every Customer: The Cost Of One AI Expert
Luckily, we got this one right from the start. Our early customers insisted that their data, documents and AI models reside within their system, abiding by best practices for corporate data security and governance.
The problem for most AI algorithms is that great results depend on huge sets of training data. The typical practice by AI companies is to pool data and documents from all customers, an open door across the enterprise governance boundary. In contrast, our AI system needs only a handful of documents for training, so all data and documents stay within the customer’s governance system.
To comply with customer needs, we built out the software infrastructure to support this key feature, known as a multi-tenant architecture. Data deletion policies, robust security measures and other features are also needed in most cases.
It is difficult to underestimate the customer adoption impact of how an AI-based product aligns with corporate data governance strategies. As a recent Forbes article notes, “Moving into 2019, data governance is no longer optional because it underpins data security, compliance and privacy.”
Market surveys show large corporate spending ($22 billion per year by 2023) and rapid growth in spending (almost 20% CAGR) for data governance and master data solutions. In particular, on-premise configurations that are not connected to the cloud, are often seen as a solution. This poses particular challenges for AI products that require continual feeding of labeled data to their large training corpus.
Looking across the AI landscape, we see that others have had to spend quite a lot on this too. For example, OpenAI has invested more than $1 billion in building a similar collection of tools, and they report that $1 billion is not enough!
Human Control Of The AI Experience: Twice The Cost Of An AI Expert
The fear that AI is a black box is pervasive, but we found another huge pain point: control. Legacy software systems often require software engineers to manage processes, fix every issue and package up results. Users of many corporate processes want to regain control. They want to use complex software themselves, no coding required. And, the implications go further. As one Forbes writer put it, “Companies who build trust with their customer base via transparency and factual information that can be verified with data are expected to have the competitive edge.”
AI products need to provide a welcoming and easy-to-use interface to the AI product. We built a user interface to our AI system, iterating versions with multitudes of users. Ironically, it a user interface on an AI product that provides transparency and direct control to the corporate user. From what we see, this is a must-have requirement from the market.
The 15-Times Total
There’s a lot of good news in our 15-times total. Our experience shows that 15 times the cost of an AI expert is a reasonable and bounded number. AI projects are not a runaway train; they’re projects that can be planned and executed to minimize risk. In tackling your own project, be sure to keep these steps in mind:
• Plan your data flows and user permissions.
• Build your AI prototype.
• Build a primitive first-pass on the infrastructure to support the AI.
• Add the human interface to the system.
• Mature the AI and the infrastructure together through a series of coordinated releases.
This list feels a bit familiar, and experienced software executives will recognize a step-wise, agile approach. As the list shows, the secret ingredient is not a large team of AI experts; it’s rather the supporting software team and infrastructure that creates the complete solution.