Riding Digital Transformation’s Second Wave: The Age of AI

Earlier this year, an association of data science professionals asked me to talk about IT careers during an economic downturn. I wondered why they wanted me to address the subject – after all, economic downturns happen every so often. Then it hit me: they were asking me because I was so old that I was working in IT during the Great Recession of 2008-2009. It seems none of their members had lived through that period as IT professionals. I told them that I could go back even further – to the burst of dotcom bubble and the stagflation of the late 1970s and early 1980s. I joked that that I could even talk about life in technology during the Great Railroad Recession of 1893!

Snarkiness aside, we had a great session talking about how technology and IT roles have evolved over the past few years. More importantly, I explained how the technology organization has changed from its earliest days until now, when digital transformation has embedded technology into all aspects of the business.

When enterprise computing emerged in the 1950s, technology and technologists were in a support position. The business defined its needs and we reflexively developed technologies to meet those needs. Need an accounting system? We would build and deploy one. Looking for an MRP system? We’d build and deploy one. Rinse and repeat.

Then we entered the first wave of digital transformation, enabled by the internet and nearly ubiquitous connectivity and supercharged by Social, Mobile, Analytics, Cloud (SMAC). SMAC changed the way organizations operated and elevated the role of technology and technologists. Its capabilities defined new business opportunities. Social drove the world in general — and enterprises specifically — to share lots of new information. Mobile pushed its way into every aspect of life. No longer did we go to our technology; our technology came with us. Analytics enabled businesses to gather and analyze increasing volumes of data to personalize customer experiences and make more informed decisions. Cloud afforded IT organizations the flexibility and elasticity to change their operating models. SMAC revolutionized the way we build products and services, the way we market and sell products and services, the way we manage operations, and where and how we connect.

As IT leaders, we have had to progress from our support roles to business leadership positions guiding the organization toward a technology-rich future. But the evolution is not over. As we enter the second wave of digital transformation — the age of AI — IT organizations and our roles as IT leaders must evolve further.

AI Takes Center Stage

To be clear, there was no shortage of AI in the first wave of digital transformation. Indeed, the best work I have done thus far in my career has involved AI. In 2012, I led the development machine learning models capable of consuming thousands of data points to identify which students were at risk of dropping out and, based on the student’s history and profile, what interventions would most help that student succeed.

In addition to such machine learning and deep learning initiatives, the first wave of digital transformation also ushered in advances in Natural Language Processing (NLP). NLP gave us not only those (often irritating) first generation chat bots, but also offered language translation, inferential learning, and various automation opportunities (such as process mining, intelligent optical character recognition, and robotic process automation).

But while these forms of AI made huge impacts within organizations, they didn’t fundamentally change the way business operated or how people worked. These forms of AI required specialized skills and were not necessarily applicable to a wide range of use cases. These AI capabilities existed and they worked, but their impact was limited.

But all of that changed with the development of Generative AI (GenAI) and the ability to consume, understand, and synthesize mountains of data in response to a prompt. Suddenly anyone who could structure a prompt could access very powerful AI capabilities. Not sure how to write a Python query? You can ask GenAI and get a solid starting point. Need a draft process for incident management? You can ask GenAI for a synthesis of best practices.

All of the cool kids are using GenAI — and I always wanted to be one of the cool kids.

As with any form of AI, prudence is warranted. There will be plenty of churn as the technologies and providers quickly evolve. Veteran CIOs understand better than anyone how to approach emerging technologies with caution. There are also plenty of great use cases. Much has and will continue be written on how and where to apply GenAI in the enterprise. So, I won’t add to the chorus on either the most fruitful areas of opportunities or how to proceed in a rapidly evolving tech marketplace. Instead, I want to launch a conversation about the implications of GenAI for IT leaders and their teams.

Why — And How — IT Should Lead

Because GenAI makes AI much more accessible, it makes many aspects of our enterprise technologies accessible for the non-technologists in our organizations. More importantly, as GenAI converges with the other forms of AI (such as machine/deep learning, NLP, and automation) it can quickly enable autonomous operations.

Suppose I train a machine learning model to detect various types of anomalies. When the model sees something unusual it determines the cause of the anomaly, it could invoke GenAI to both respond to the anomaly and, using NLP, notify an operator of what is happening. If the situation requires a human intervention or decision, the system could, through GenAI, recommend remediation options. And if the data I use to train my models is complete and accurate, I no longer need a highly-skilled human to review and decide on responses.

As I think about all of the processes ripe for such autonomous solutions, I realize that as GenAI converges with the other forms of AI, what’s really happening is a radical democratization of technology. Sure, we may have a number of “citizen” technology roles today, such as citizen analysts (thanks to business intelligence tools) and citizen developers (thanks to low-code/no-code tools). It’s time for us to begin considering what other citizen roles, previously requiring high-level technology skills, are on the horizon.

On a more personal level, we need to explore how this will change the role of IT and its leaders. This second wave of digital transformation will certainly usher in some fundamental changes in this regard, and now is the time for us to begin to redefine our roles. Even with this deeper democratization of technology capabilities, IT should play a central role.

Our skilled leadership will be important. And, in my opinion, IT should take an ownership position in number of key value streams in this AI age, including:

• Data governance and validation: AI needs accurate and complete data. Who better than us to define and lead this process?
• Model governance and validation: Just like with data, we need to ensure that our models are accurate and complete.
• Technology and vendor selection: Every vendor now claims to offer AI and baked into their products and services. IT can take the lead in ensuring their data and models are accurate and complete.
• Technology skills development: As the use of advanced technologies expands, everyone will need a foundational set of technology skills. IT leaders, who have spent their careers upskilling and managing the changes in roles within their own organizations, can now do so more broadly.
• Mentoring and modeling agility, adaptability, and resilience: IT leaders learned how to confront and master uncertainty amid accelerated change during the first wave of digital transformation. As the second wave expands the blast radius of change — impacting roles, processes, tools, business models, products, services, and technologies across the enterprise — IT leaders are in the best position to model the agility, adaptability, and resilience required.

Now is the perfect time for us to define our future. As our organizations experiment with GenAI in particular and AI in general, we can take the lead on pilots for data governance and validation, model governance and validation, technology and provider selection, technology skills development and experimentation. We mastered the first wave and that puts us in the best position to lead the organization through wave two.