AI Beyond the Hype: Driving Real ROI in Your Organization
Cut through AI noise and focus on strategic adoption. Learn how to identify true business problems, prioritize effectively, and build an AI strategy that truly delivers.
Futurist AJ Bubb, founder of MxP Studio, and host of Facing Disruption, bridges people and AI to accelerate innovation and business growth.
Navigating AI Transformation: From Hype to ROI
Artificial intelligence is everywhere. It’s the topic on every leader’s mind, promising rapid transformation and growth, and sometimes, a whole lot of confusion. Is it a bubble? Is it changing everything? Maybe. But here’s the thing: understanding AI’s true impact means looking beyond the headlines and focusing on what it means for your organization.
My guest, Rupali Kumbhani, an executive leader grounded in strategy, innovation, and execution, helps us cut through the noise. She works with C-suite executives to build and implement enterprise strategies using technologies like AI, data, and cloud. Rupali has seen firsthand both the successes and the pitfalls of AI adoption. The goal? To embed AI strategically so it multiplies success, not just becomes another tech experiment.
The Current State of AI Adoption: Opportunity or Chaos?
The AI landscape feels wild. Recently, we saw headlines about OpenAI and Anthropic spending billions on enterprise adoption, while other reports suggest as many as 75% of executives feel AI is tearing their organizations apart. What’s going on?
Rupali sees organizations at different stages. Some, initially skeptical in 2022-2023, now realize AI isn’t going away and are just beginning their adoption journey. They’re focusing on preparing their enterprise, their culture, and their people. Others, who started earlier, are moving into more advanced stages, grappling with scalability, agents, and custom models.
A common thread in early adoption involves “hackathon” approaches – essentially, throwing AI at various problems to see what sticks. But this often doesn’t lead to enterprise-wide ROI. Rupali emphasizes a different approach, one that looks at the whole system:
“I’m leaning towards more system-level thinking. I feel if it is done in a siloed, it’s rare that eyeballs are coming. That’s where the organization is not seeing that ROI because working on two or three use cases does not tell you how AI will scale at the enterprise level.”
This means considering your infrastructure, your data availability, and the accuracy of your models across the entire organization. Skipping these foundational steps will lead to magnified failures, not multiplied success.
AI as a Multiplier, Not a Magical Tool
Many leaders approach AI as a “magical tool” that will solve all their problems. But this perspective often leads to disappointment. AI isn’t a strategy in itself; it’s a powerful multiplier for an existing strategy. If your underlying processes are broken, AI will only amplify those problems, making failures appear faster than ever.
Think of it like this: if you have a solid foundation and efficient processes, AI can accelerate them. If your foundation is cracked and your processes are a mess, AI will just crack them faster. Rupali shared an example of working with a financial CEO launching a new product. His initial question was, “Which AI tool will help me launch this?” Rupali’s response:
“From my side, it’s not a marketing tool. We should be focusing on the riches. The right AI marketing tool we have it here to solve your problem, but let’s take a step back. Look at the problems. What was happening in your previous product launch? His campaign was failing and it turns out to be that he did not have information about the targeted audiences.”
The solution wasn’t an AI tool, but a fundamental understanding of the target audience. Once that strategy was in place, AI could then optimize the marketing efforts. This illustrates a crucial point: AI assists human intelligence; it doesn’t replace the need for clear strategic thinking. We need to decide where AI can be most effective, and where it might introduce biases or security risks. These considerations need to be part of the strategy, not an afterthought.
Actionable Steps: Defining Measurable Goals and Prioritization
How do you move beyond vague notions of “having a problem” and pinpoint AI’s true potential? Rupali’s actionable steps focus on clarity and measurable goals:
Focus on the “Why” and “What,” Not Just the “How”: Leaders often jump to tools. Instead, start with why you need AI and what you want to achieve. This means defining very clear, smart, and measurable goals. A CEO might have a grand vision, but it needs to be translated into tangible, quantifiable outcomes. What does “making the organization one unit” actually mean in terms of infrastructure, applications, and processes?
Prioritize ruthlessly: Once you have measurable goals, you face the next challenge: everything feels like a high priority. Leaders need to work with their teams to identify the absolute highest priorities, considering factors like budget, resources, and risk tolerance. This isn’t easy; it’s often a back-and-forth conversation, but it’s essential for smart scaling.
It’s still challenging to get leaders to prioritize, even with AI’s speed. AI makes it seem like a magic wand, capable of instant solutions. But as Rupali explains, AI, like any technology, needs time and data to improve accuracy. The human element of critical thinking remains paramount. You need people who can differentiate between a truly effective AI use case and one that’s just novel. This evolving perspective is starting to shift mindsets, moving away from “AI replaces all” to “AI augments and enables.”
Human + AI: Augmentation, Not Replacement
I’m a big believer in human + AI, where AI serves as an augmentation tool. It empowers those with deep expertise to achieve more, faster. A lawyer, accountant, or researcher knows what “good” looks like in their field and can leverage AI to get there. But what does this mean for how roles are defined?
Rupali highlights the importance of critical thinking, adaptability, and agility. Jobs solely focused on administrative tasks or rote processes are most vulnerable to automation. However, if your role involves thinking strategically alongside administrative tasks, AI becomes a powerful partner.
Successful individuals in an AI-powered world will be those who:
Maintain critical thinking to select the right models and data for a given problem.
Understand how AI works and what it can deliver, without necessarily needing to code.
Possess curiosity and a willingness to experiment with new tools.
As Rupali puts it, “if you are not learning, and if you constantly stopping yourself, ‘I’m not going to learn these things because this is going to take my job away. I’m not going to touch it.’ You already are far behind. You yourself make more vulnerable to AI than anybody else.”
AI is a general-purpose tool. What excites me most is its ability to self-explain. You can literally ask AI how to use AI better. While regulated industries might be slower to adopt due to data security concerns, many organizations are building secure environments to allow employees to experiment. Training programs tailored to specific departmental needs – leveraging copilots for finance or creative tools for marketing – are essential. This helps avoid burnout and ensures employees feel supported in their learning journey.
Preparing for Success and Achieving Real ROI
One common oversight in AI adoption is the lack of preparation for “wild success.” Many organizations invest in initial pilots, but fail to consider the infrastructure and human resources needed if those pilots scale rapidly. If AI-powered sales tools generate a huge influx of leads, is your sales team ready to handle them? Is your customer service equipped for increased demand? Rupali explains:
“You need that input because more the data comes, you have to beat model and you have to train that more to give you that accuracy. If you are not feeding that scenarios, you are not eating that data. Your model is not worthy and that’s where you will miss that ROI.”
To truly achieve ROI from AI, you need a holistic approach that bridges strategy and execution. This means:
Robust Infrastructure: Your systems must be ready to handle the increased data flow and processing demands of AI models.
Data Governance: Clear policies on how data is collected, stored, and used are critical for model accuracy and ethical AI.
Scalability Planning: Anticipate what success looks like and build your capabilities to match, not just technically, but across all operational aspects.
When articles say AI is failing to deliver on its promised ROI, it’s often a symptom of underlying issues. Often, the ROI was never clearly defined or the execution strategy wasn’t thought through. It’s not AI’s fault; it’s a leadership problem. Blaming AI for biases, job losses, or lack of ROI often misses the point: humans program AI, and human decisions dictate its application. If we input biased data, AI will reflect it. If we plan poorly, AI will multiply the poor plan’s impact.
AI is here to stay. Whether you actively choose to adopt it or not, it will eventually impact your organization through your clients, stakeholders, or even your competitors. The choice isn’t whether to use AI, but how to use it strategically and ethically. Leaders need a mindset of adaptability, agility, and continuous learning. These skills, not just tech expertise, will drive success in an AI-powered world. Focus on the “why” and “what,” build a solid strategy, and integrate execution planning from the start. That’s how you turn AI from hype into real, measurable value.
For more insights and a deeper dive, read the full article on the Facing Disruption newsletter.