Embracing Digital Transformation

Episode #297: The Myth of Easy AI: What Leaders Keep Getting Wrong

Host: Dr. Darren Pulsipher
Guest: Dr. Ashwin Mehta, CEO and Founder, Methodology
Date: October 14, 2025

Overview

This episode confronts one of the most persistent misconceptions in the AI landscape today: the myth that implementing AI is "easy" and guarantees instant returns. Host Dr. Darren Pulsipher and guest Dr. Ashwin Mehta critically analyze why so many AI and generative AI projects fail to deliver value, and dissect what public sector and enterprise leaders keep getting wrong about digital transformation in practice. Blending humor, music metaphors, and practical advice, the conversation explores how organizations can set realistic expectations, prioritize the right processes, and create real business value through systematic, needs-based technology adoption—rather than chasing hype.

Key Discussion Points & Insights

1. Breaking Down the "Easy AI" Myth

• Viral Promises vs. Reality
  Social media, vendor pitches, and tech news feed unrealistic expectations that AI can revolutionize any business, quickly and with little skill or effort.

  • Quote:

    "Usually you have lots of posts where you'll say, with no coding experience, no experience and no skills, you too can pick up this thing for $5 and revolutionize your business. And I think the expectation that this stuff is easy needs to kind of go away a little bit. You have to put work in to get something out."
    — Ashwin Mehta [00:00]

• Leaders’ FOMO and the Copycat Syndrome
  Many leaders push for "doing something with AI" because of fear of missing out, competitive pressure, or vague executive mandates—without clarity on problems or desired outcomes.

  • Ashwin: "We want to do something with AI, but we don't really know what. Or we have this massively sprawling landscape...and we don't really know how to make sense of it." [05:32]

2. Why AI Projects Fail

• Lack of Strategy and Clear Use Cases
  Many businesses hastily embark on AI projects post-ChatGPT (Nov 2022) lacking technical expertise, purpose, or measurable metrics, leading to sprawling, directionless initiatives.

  • Ashwin:

    "If you have something that you want to do and you mobilize people and technology and all of these things against that purpose, you have things that you can measure. ...I think a lot of companies just went off on this little odyssey without necessarily having a purpose, measurable success criteria, all of the skills to maintain something." [09:28]

• Questionable Research and Hype
  The claim that “95% of GenAI projects are failing” (recently cited from an MIT report) is seen as both “pretty bold” and methodologically suspect—highlighting an industry-wide rush to conclusions.

  • Dr. Darren: "You know, I actually think it leads to what we're seeing in the industry as a whole. It's this, I need speed and I cut corners to get to that speed because if I don't do it, someone else will." [11:50]

• Dilution of Terminology & Expectations
  With so much hype, basic data science and automation efforts are mistaken for transformative AI, and leaders lose track of what actually generates lasting business value.

  • Ashwin: "I do think that even in the...AI space, we have a dilution of terminology, we have a dilution of understanding when it comes to some of the basics." [12:32]

3. Setting the Right Approach: From Problem to Solution

• Start With a Real Need, Not the Technology
  Powerful business transformations require recognizing actual pain points, then mapping the appropriate solution—sometimes AI, but not always.

  • Ashwin (with a Dragon Ball Z analogy):

    "The transformation that happens is this guy...he needs to do a thing and he surpasses himself. He transforms and he says, this power comes because there is a need. And if we think about that statement, that is a statement that applies generally to business. Do you have a need? Do you have a pain point?" [14:49]

  • Solutions should fit the problem, not the other way around.

• Don’t Skip the Basics: Automation Before AI
  Many productivity and process advantages are attainable with "old fashioned" automation (like Robotic Process Automation, RPA)—without generative AI's complexity or risks.

  • Ashwin:

    "Start with your processes, start with some broad based automation and see if you need AI, because not everything needs to be AI." [16:47]

• Tiered Complexity & Value
  A business might start with simple workflow automations (e.g., auto-replying to emails) and only invoke more advanced (and riskier) AI capabilities for nuanced or highly variable needs.

  • Ashwin:

    "If you want to analyze your entire inbox for 25 different types of query, we start to get into the space where we could build complex automations, or we could use something that's a little bit agentic..." [18:41]

4. The New Toolset: Why Barriers to Entry Are Dropping

• Low-Code, APIs, and Integrations
  Modern platforms (including SaaS mainstays) offer plug-and-play automation and AI features, and an explosion of APIs lowers the technical barrier even for small organizations.

  • Ashwin:

    "We have very capable platforms now which are easy to understand, maybe low code platforms... We have a proliferation in the market of readily available APIs..." [22:43]

• Rethinking Processes, Not Just Automating the Old Ones
  The right AI or automation can wipe out redundant, ad hoc, or human-to-machine translation steps, and leaders should use this moment to reengineer and streamline processes.

5. Who Should Lead & How to Find Real Experts

• Learning is Easier, But Skill Still Matters
  It's never been easier to access information and learn new tools, but organizations still need time, aptitude, and discernment.

  • Ashwin:

    "It's never been easier to learn this stuff. We have information absolutely everywhere... but it requires you to have the time and the aptitude and, you know, not to be scared off by certain things." [24:43]

• Opinion vs. Delivery: Vetting “Experts”
  The AI talent market is crowded with self-proclaimed gurus, and it's hard to distinguish real delivery capability from online popularity.

  • Ashwin:

    "There are self-proclaimed experts. Yeah, yeah. And it's sometimes difficult to scrutinize the person who's popular on LinkedIn versus the person that can do the thing for you. And it's difficult. I'm not going to sugarcoat that. Many companies will learn the hard way..." [24:43]

  • Look for consultants with a methodical approach—prioritizing use cases, rapid prototyping, and leveraging your existing tech stack over those offering vague platitudes about "ethics" or "supporting people".

• Scrutinize Proposals & Strategy
  Demand systematic approaches, clear prioritization against real criteria (budget, skills, data), and be wary of buzzwords that don't map to action.

  • Quote:

    "A strategist might talk about, we don't want to replace people, we want to support people. Great. It's a talking point. It's not a strategy, it's a talking point." [27:32]

Notable Quotes & Memorable Moments

• Music as a Metaphor (and a Creative Outlet)
  Ashwin’s love of guitars (and sitar) shaped both his creative and technical journey:

  "When you first touch a football, when you first pick up a guitar, you're not automatically in a stadium playing to thousands of people... This expectation that things are easy, that if I just pick this up, if I just ask one or two questions, somebody will say, there’s this tool over there that's just going to solve all of your problems."
  — Ashwin Mehta [06:34]

• Dragon Ball Z on AI Transformation

  "He transforms and he says, this power comes because there is a need. ...That's really the starting point for all of this stuff."
  — Ashwin [14:49]
  (Prompting laughter and agreement from Darren.)

• The MIT Report Skepticism

  "I was surprised at the bold statement...and the other is I was surprised by the quality of the research. I thought it was pretty low."
  — Ashwin [09:28]

• On Consultant Hype

  "There are consultants and consultants. And I think any folks who've been into the tech consulting space probably have a methodical approach. ...A consulting approach that is systematic in this way is more likely to produce value than somebody who has a strong opinion about AI..."
  — Ashwin [25:20, 27:32]

Key Timestamps

• 00:00-06:34 — Setting the Stage: AI Hype & Social Media Myths
• 06:34-09:16 — Why Leaders Chase AI (FOMO, Unclear Mandates)
• 09:16-14:06 — MIT Report Debate & Failure of Strategic Alignment in AI Projects
• 14:06-16:47 — “Dragon Ball Z” Analogy: Business Transformation Stems from Need
• 16:47-18:41 — Automation > AI: Start Simple, Not Every Problem Needs GenAI
• 18:41-22:43 — Types of Use Cases: From Keyword Automation to "Agentic" Systems
• 22:43-24:43 — The New Toolset: Low-Code, APIs, Lowering Barriers, Process Re-engineering
• 24:43-28:32 — How to Vet Real AI Experts vs. LinkedIn “Gurus;” The Importance of Methodology
• 28:32-30:34 — Delivery Over Opinion: Systematic Approaches vs. Fluffy Talk

Actionable Takeaways

• Set realistic expectations about AI: most value comes from systematically solving business pain points, not chasing trends.
• Leverage basic automation first; only add AI where it brings clear, additive value.
• Scrutinize AI proposals and consultants for systematic, practical methodologies.
• Remember: meaningful transformation starts with business needs, not with “the latest tech.”

Guest Contact & Further Resources

• Ashwin Mehta on LinkedIn:
  Find regular posts, articles, and insights.
• Website: methodology.com
  White papers and DIY frameworks available.
• Instagram: Regular posts on podcasts and practical AI tips.

Episode Tone & Style

The conversation is frank, humorous, and grounded, mixing pop culture and music metaphors with hard-nosed management advice. Both Dr. Pulsipher and Dr. Mehta eschew hype in favor of realism, urging business and public sector leaders to critically engage with the myth of "easy AI" and focus on value-driven, well-planned transformation.