The New (Intelligent) Transformation: A Guide for AI Adoption
Photo by Suzanne D. Williams on Unsplash
This article first appeared in Medium.
In the future, every company — including the one you work for now — will have to be an “AI company” to survive. That’s not hyperbole, it’s a fact. It’s the same reason why every company has had to become a technology company — regardless of your product or service. Why? Because every other company in your space will try to figure out how AI (and Generative AI in particular) can help them become more efficient and effective in serving their customers.
And that’s an important point: leveraging AI is not only about efficiency. As I wrote in a previous article, that’s missing the point. Generative AI will enable companies to unlock potential and do things better in ways that were not possible before. Like everything. From your product offerings to serving your customers to how your teams collaborate across your organization. Every. Thing.
But now that I’ve pumped you up (or freaked you out), let me splash a little cold water reality on this topic: We are only at the very beginning of this transformation journey. The new AI-driven business transformation (previously known as “digital transformation”) hasn’t even begun in earnest. If you’re a fan of analogies, we are at the “forming the cocoon phase” part of the journey.
AI transformation, which I’ll refer to as “Intelligent Transformation” throughout this article, will have a similar arc to digital transformation. It is an initiative that slowly gains momentum across industries as established legacy companies try to embrace the new technology and evolve their business and operating models. Eventually, it will snowball into a ubiquitous initiative (and buzzword) across industries.
So, how do companies approach intelligent transformation? In most cases, they should start small. Initially, Intelligent transformation will have three phases, which I’ll describe as the crawl, walk, and run concept.
Crawl: Individual and team productivity gains.
Walk: Unlocking new domain-specific opportunities and capabilities.
Run: Organization-level transformation.
Newer companies, and especially start-ups, will move through these phases more quickly (and perhaps even skip the first two) as “AI-native” companies — meaning they have the luxury to build their company and operating model from the ground up with AI as the foundation. However, most (established) companies will have to evolve methodically and essentially rewire their DNA. The best way to do this is by focusing on specific areas of your company first, i.e., verticals, instead of trying to rewire the entire company all at once.
The Case for Vertical Integration
For the context of this article, a “vertical” can have multiple meanings. A vertical can refer to an industry (e.g., retail, healthcare, hospitality) or areas in your business (i.e., departments such as marketing, HR, and product). I will mainly refer to the departments and domains that comprise your company. Why? Because this is how a company can start small and then snowball that momentum into something bigger.
Starting small — or crawling — is about enabling and encouraging your employees to play around with Generative AI tools in their day-to-day activities. This achieves several things at once. From a change management perspective, you empower your employees to take ownership of how the company will evolve and “transform” day-to-day operations. It also gets your employees experimenting with a new technology for (presumably) low-risk scenarios, innovating with their colleagues on how best to augment their roles.
What does this look like within a vertical? Let’s pick a vertical I’m very familiar with: product development. For our purposes, product development includes product management, engineering, and UX design.
Crawl: Individual and Team Productivity Gains
Starting with the first phase (Crawl) within a company’s product development vertical, you first give employees access to generative AI tools, provide some (reasonably light) parameters around how to use them, and then step back and let your teams experiment and figure out what works best.
You will likely see your product managers experiment with analyzing data, brainstorming with an LLM, or generating content. They may also experiment with using an LLM to draft a product framework (e.g., Jobs to Be Done), do basic market research, or analyze data such as NPS survey feedback.
Engineers will start trying out code generation or bug detection assisted by generative AI — even automated testing and QA are use cases where new AI tools can play a role. UX can leverage AI for initial user research, summarizing qualitative data, and ideation for creating personas, user journey maps, and more. There are countless ways that individuals and teams can start to experiment with generative AI tools that make them better at their roles.
However, you should not take a completely hands-off approach. You will need to create a community and strongly encourage — incentivize even — sharing learnings and ideas. You must realize that your employees may be reluctant to be totally transparent about leveraging tools that make them much more efficient at their jobs. It’s natural for them to wonder if they are simply helping the company figure out a way to replace them. Or, at best, if they are just helping set the expectations for performance higher while getting compensated the same.
You may need to take it a step further, though, and share a compelling vision for what AI means to your company’s future. Getting buy-in — and establishing trust between leadership and employees — will be essential in a company’s transformation journey.
Walk: Unlocking Vertical, Domain-Specific Opportunities
Continuing with the product vertical scenario, you enter phase II — or the Intelligent Transformation Walk phase — when you can operationalize the generative AI use cases that individuals have experimented with and found success with. But it’s more than just operationalizing the proven use cases across your org. It’s about starting to rethink the end-to-end process of the domain. In the case of the product domain, product managers, engineers, and UX designers can begin to merge their workstreams as they embed more AI capabilities in their tasks and processes.
I predict that we will see accelerated prototyping, streamlined and robust AI-generated summaries of meetings and workshop sessions, automated drafts of PRDs, and expedited feedback loops, resulting in increased speed to market. As AI and LLMs become more tightly integrated, the entire end-to-end process will evolve, from discovery to deployment and everything in between.
Run: Organization-level Transformation
The product vertical scenario I described above is actually a microcosm of what organization-level transformation, also known as Run/Phase 3, looks like. The reason is that even though engineering, product, and UX are often under the same broad umbrella, they are specific domains with unique processes and standards. The scenarios I described above cut across these different groups in a way that can reshape operations within and between these groups. At the macro level, organizational level transformation will reshape how verticals work together and how the company operates.
Imagine this: the Finance team streamlines its financial analysis processes, enabling quicker decisions and more accurate forecasting. This agility in financial operations opens the door to faster funding reallocations for the product roadmap, allowing for nimbler prioritization and resource alignment. Meanwhile, as the product team transitions from rapid prototyping to deployment, the marketing team leverages AI-driven insights to craft hyper-personalized campaigns for each market segment, launching them in tandem with the product and driving immediate growth. These interconnected advancements demonstrate how vertical transformations can snowball into organization-wide innovation.
While what I described above is theoretical, we are already starting to see glimmers of these use cases emerge today. Searching through LinkedIn articles doesn’t take long to see where generative AI use cases are trending for companies. However, there is one catch…
Before You Walk, You Need to Stand
Before you walk (and run), you need to be able to stand. Being able to stand implies you have solid ground underneath you. In the case of generative AI, you need a data foundation to support the walk and run phases of Intelligent Transformation. This topic is easily multiple articles all by itself. But I’ll just hit on the main concepts to help you understand what is needed. IBM has an easy-to-understand framework, The AI Ladder, that explains what is required to support an enterprise’s AI strategy. IBM has created a Coursera course, and the concepts/content are relatively accessible for people with some basic familiarity with data and AI. I’ll summarize the essential concepts but encourage you to check out the course or other resources to learn more.
The AI Ladder — A Framework for Deploying AI in Your Enterprise
Infuse — Operationalize AI throughout the business
Analyze — Build and scale AI w/ trust and transparency
Organize — Create a business-ready analytics foundation
Collect — Make data simple and accessible
Modernize information architecture — make your data ready for an AI and multi-cloud world (this step is considered ground level)
Starting at the bottom, each company will need to “climb” this ladder to scale an AI strategy. And the first step (even before you begin climbing) is to modernize your information architecture.
In other words, your company needs a data strategy. Your employees may not be limited in Phase 1 — Individual and Team Productivity Gains. However, phases 2 and 3 will undoubtedly be constrained if you haven’t “climbed the ladder” to establish a solid foundation to build your AI strategy.
Conclusion
Without a doubt, each company will need to transform itself into an AI company just like all established companies have had to (digitally) transform themselves into a technology company over the last 10–15 years. (See Marc Andreessen’s article Why Software is Eating the World for reference). At the same time, this evolution (revolution?) is still nascent and only just beginning to gain momentum. The transition will lurch forward in fits and starts like a freight train just starting to move forward, but progress will gradually become steady and unstoppable.
Get on board early by creating opportunities for your employees and teams to use generative AI tools as a formal resource in their toolbox. As momentum builds, look for ways to reshape the end-to-end processes for the vertical domains in your company. Finally, harness the momentum generated by your vertical teams and ride the organizational-level transformation that begins to remake your company’s operating model from the inside out. Intelligent transformation will be a journey. Taking that first, simple step is the smartest thing your company can do right now.
