By Betsy Burton
I recently read several postings on LinkedIn about the role of an AI architect. Interestingly the postings refer to several roles for an AI architect, including:
- Working with data scientists and AI technical leads to define use cases
- Define technical implementation
- Help guide the AI technology architecture and select technologies
- Audit AI tools, standards, and practices
- Work with security and risk leaders to reduce risk
Now, these are all good tasks that AI architects must be involved with or at least guide. However, the first and most important effort is to define a business architecture that effectively defines a business strategy, model, capabilities, processes, operations, and organizational (human) change management that benefits from the use of AI.
AI Is About Business and Technology
Almost every enterprise service or product I find on the market today has some “AI” in it. The market hype surrounding AI-enabled solutions is incredibly high. This is not a surprise since AI represents a fundamentally new approach to developing solutions. Any new or emerging product/solution is going to leverage AI. But not all AI solutions are equal, or even what we might think about as intelligent. Organizations risk wasting a lot of time, energy, and money if they don’t adopt AI-based solutions with a specific future-state need in place.
Adopting AI-enabled solutions is as much a people, process, organization, and strategy issue as they are technology. The best AI-enabled solution will go nowhere if it doesn’t address a future-state business need. And it certainly won’t help if business users and leaders don’t accept it.
Adopting AI-based solutions requires outside-in customer, user, and business thinking; not inside-out technology first thinking. In other words, AI-enabled solutions should never be adopted as a technology in search of a problem or opportunity.
AI Business Architecture
The first and most important aspect AI Architects need to take on is working with their business partners, CTO and business architecture peers to determine what future-state business vision and need is best enabled by AI-based solutions. Then they must drill down and specifically identify what business models, processes, and operations would look like in the future given different AI options.
Is your organization planning to use AI-enabled bots or agents to interface with customers and partners? Does your organization plan to leverage AI advisors to help knowledge workers make complex decisions? Does your organization need to change its operating model to take advantage of AI-enabled digital labor? There is a myriad of questions that need to be addressed before considering any technologies.
AI architects also need to work with people managers, leaders, HR, and customer experience leaders to ensure that the solutions enhance and augment their experience.
Common business architecture tools that can really help with this effort are business capability models, value-chain models, business model canvas, process modeling, and user/customer experience modeling.
Bottom Line
Granted, the emergence of AI-enabled solutions is an exciting evolution – which by the way we have been working on since at least the early 1980s. It is however not immune to the overhype we have experienced with other emerging technologies. Organizations risk wasting investments if they are adopting AI because it is viewed as the “thing to do;” there are too many different solutions that require a clear understanding of the business context.
AI architects must help their business and technology leaders make effective and valuable investment decisions by creating deliverables that put these decisions in the context of business, people, process, operations, and business ecosystem.