The 5 Ds for AI Project Deployment Success

TLDR: Adopting AI-enabled technologies requires more than buying a license. Enterprise IT groups need to examine their readiness for AI-led innovation using the five D’s of AI deployment.

The Artificial Intelligence (AI) rush is on, and it is headed for the enterprise. As AI-driven excitement and hype spreads through financial markets and investors, every imaginable software company will jump into the arena with real and sometimes vapor-ware enhancements to their products. The marketing efforts from these software vendors will increase as each vies for attention and corporate budgets. Next, pressure on Information executives (CIO, CTO, CISO) will increase to find and deploy the technologies to improve competitiveness and reduce costs. In the rush to embrace the new technologies, we need to understand how to evaluate your organization’s readiness for AI automation, and target business areas most likely to be successful.

AI deployments depend on a series of components to function correctly. These ingredients can be described in the following model:

Let’s look more closely at each D on the list:

DEFINED PROCESS

Prior to engaging in any project, the business stakeholders must agree on how the process works, and how it should ideally work. What is the desirable outcome? How should exceptions be handled? Automation of any process depends on understanding the process. Let’s say your organization has an informal planning and budgeting process that varies depending on the mood of the owner. In this business function, you will struggle to find a way to find any improvements using AI. On the other hand, if you have a well-structured planning process with consistent variables and clear KPIs, then you will have much more success in automating the process.

DIGITIZED TASKS

Manual or offline tasks will be the “high hanging” fruit for AI. It could take months of effort to understand them, design applications to support them, and monitor the outcomes. A task that has been migrated to an application or database in the past will be much more easily understood and automated in an AI platform.

DATA COLLECTED

AI-driven processes have data as their foundation. Prior to automating a process, you should have at least 3 months of data collected for non-cyclical processes; 1–2 years for cyclical processes. This data will guide the design, implementation, and control of any process. Data that is collected in an unstructured data lake is a good start; but a conformed, integrated data set is essential to understanding all inputs and outputs for a process. A data warehouse will accomplish the task of conforming and integration but will lack the overall semantic model that gives business context. The ideal platform for Ai-automation is a knowledge graph, with interconnected processes in documented relationships.

DEPLOYMENT PATH

The next element is to evaluate how the AI software would be deployed. For off-the-shelf solutions, would there be compatibility with existing technologies in the organization, or would it require extensive integration and consulting work? And for custom built solutions, using open-source tools, do you have the resources and skills to successfully implement and sustain a solution? Organizations that are already hosting data and applications in the cloud will have an advantage in integrating new AI-driven software, which is most often native to the cloud.

DEMAND

The final requirement is that there is business value for and interest in, or demand for, the project. Is this project significant to revenue or expenses in the organization? Will it move the needle enough to justify the effort and expense in the deployment? Early in the AI journey, an organization may have a low bar for proving ROI of innovation projects, but that blank check will quickly end if results are not proven.

To illustrate these steps, let’s use as a case study a consumer-products company that seeks to generate hyper-customized marketing content for each customer based on the behavioral, transactional, and demographic information for the customer. Using a generative AI tool, like ChatGPT or Bard, they could connect to the API, provide information about the customer, and clearly define the format of the content; they could also request an optimized marketing plan for the customer. Based on the output from the tool, they could generate targeted email, social media, or paid marketing content for the customer, and designate the micro-campaign variables. The goal would be to increase the click-through rates and purchases from the customer.

Using our 5 D’s, we can evaluate whether the content marketing processes are ready and likely to be successful in an AI deployment. They may decide that the high-level budgeting project for overall marketing spend it too ill-defined to be automated. But down a layer at the campaign planning and execution level, the tasks have been digitized in various software packages, several years of well-organized historical data are available, the target outcome is known (higher sales and lower ad spend) and they are able to integrate with the ChatGPT API with a Python connection and campaign workflow automation tool. The final step is to look at reference projects or conduct a small pilot project to determine how much time can be saved in the campaign management process, and how much of a lift in customer spending could be expected. If all these elements are in place, then the project has a high likelihood of success.

Finally, it is important to consider that the AI Deployment stack consists of four layers, with data as the foundation. Fortunately, the modeling/algorithm layer will be funded by wall street investors and academic research. The application layer will quickly be filled by software vendors extending their current products or introducing new tools with API connections. But there is no way to buy or outsource the data or business processes. The internal capabilities in designing business processes and utilizing data will determine which organizations will be the winners and losers in the AI-driven innovation race.

As you examine the various functions in your organization for potential candidates for AI-automation, you can use the 5 D’s to score the readiness of each business process and focus on those areas most likely to be successful. The best preparation for future automation projects is to start today in digitizing your offline and manual processes and investing in collecting, integrating and managing your data.