congruentX

Chief Executive Officer

Avanade

North America Lead - Digital Sales and Service

Tribridge

Senior Director - CRM

HP Enterprise Services

Practice Principal Manager - Microsoft Dynamics CRM

Tectura

North America Director of Professional Services - CRM

Microsoft

Services Executive

I Help Change the Way Intrapreneurial Chief Revenue Officers and CIOs, Turn Their CRM From Friction to Fuel so They Can Outgrow Their Competition.

I looked into MIT's "95% of AI pilots fail" headline.  It was a masterclass in "𝗮𝗻𝗰𝗵𝗼𝗿𝗶𝗻𝗴 𝗯𝗶𝗮𝘀".

It was the first piece of information many leaders saw, and it anchored the entire market in fear.

But what if the 𝘢𝘯𝘤𝘩𝘰𝘳 itself is flawed? 

I studied the MIT report. And I have to thank the "Everyday AI Podcast" for their breakdown of it.  

First off - I' m not Mark Benioff, Satya Nadella, Sam Altman or a data scientist. If anything, I'm more of a "plumber for AI."  I just help people get business outcomes from Microsoft AI.   

That said, - -  I have to call this "𝙥𝙤𝙤𝙥" out.

The "finding" is much more of a  (insert your own plumbing joke here) .

Let's look closer.
1. 𝗧𝗵𝗲 𝘀𝗮𝗺𝗽𝗹𝗲 𝘀𝗶𝘇𝗲. The 95% failure stat was "primarily extrapolated" from 52 qualitative interviews. Fifty-two. A GLOBAL market trend from 52 conversations?
   
2. 𝗧𝗵𝗲 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝘀𝘂𝗰𝗰𝗲𝘀𝘀. The study defined "failure" as no P&L impact within 6 months. Six. Months. The timeframe seems 𝙙𝙚𝙨𝙞𝙜𝙣𝙚𝙙 to find failure?  
   
3. 𝗧𝗵𝗲 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 𝗯𝗶𝗮𝘀.  The researchers 𝘴𝘱𝘦𝘤𝘪𝘧𝘪𝘤𝘢𝘭𝘭𝘺 𝘴𝘰𝘶𝘨𝘩𝘵 𝘰𝘶𝘵 organizations "willing to discuss AI implementation challenges."  More design?
   
This report seems to be a dramatic statistical outlier.

Other data looks very different.

• 𝗜𝗗𝗖: $3.70 ROI for every $1 invested 
  
• 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 & 𝗘𝗦𝗚: 92% of early adopters report positive ROI.  
  
• 𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆: 97% of senior leaders investing in AI see positive returns.  
  
• 𝗪𝗵𝗮𝗿𝘁𝗼𝗻: A new multi-year study- three-fourths (75%) of enterprises report positive returns
  
So why publish a study with this much FUD?

Well - The report's conclusion 𝘫𝘶𝘴𝘵 𝘮𝘪𝘨𝘩𝘵 be a sales pitch for a proprietary project from the authors' own lab: 𝗠𝗜𝗧 𝗡𝗔𝗡𝗗𝗔.   

A 250k priced project that 𝘫𝘶𝘴𝘵 𝘩𝘢𝘱𝘱𝘦𝘯𝘴 to be the "key solution" to the very "divide" the report claims to have "discovered".    

Here's the irony: the study 𝘥𝘪𝘥 find some truth, but buried it.

They call it the "𝗦𝗵𝗮𝗱𝗼𝘄 𝗔𝗜" 𝗽𝗮𝗿𝗮𝗱𝗼𝘅. While 95% of "official" top-down pilots are supposedly "failing"... ...𝟵𝟬% 𝗼𝗳 𝘁𝗲𝗮𝗺 𝗺𝗲𝗺𝗯𝗲𝗿𝘀 are 𝘴𝘶𝘤𝘤𝘦𝘴𝘴𝘧𝘶𝘭𝘭𝘺 using AI every single day on their own.  

AI isn't failing. The technology works.

The failure is organizational. It's a disconnect between bottom-up adoption and top-down strategy.

This is the gap we work hard to bridge at CongruentX.

Your board wants "board-level" results. Your IT team needs guardrails and security. Your users are already getting on-the-ground impact, with or without you.

You need a framework to connect all three. We call it an 𝗘𝗻𝘃𝗶𝘀𝗶𝗼𝗻𝗶𝗻𝗴 𝗟𝗮𝗯.
And you don't need to pay $250,000 for it. In fact if we don't Find ROI - you don't pay.

What do you think? Was this a fair study, or a brilliant (some might use other terms)  marketing campaign? 

#GetAIRight #GetCopilotRight #AiFueledRevenueOutcomes

Chuck Ingram

I Change the Way Intrapreneurial Chief Revenue Officers and CIOs, Use Their CRM as Fuel so They Can Outgrow Their Competition.

“You did as much in a day as our last partner did in two weeks.”

"And you are getting our requirements right."

That’s what a large financial services company client told us after the first week of a recent CRM engagement. 

Two things made the difference: who we assign to projects, and how we work.

Some people would say the way we do things is really different. I would say that's becoming frontier.

First—the people. 

We embed senior people with the client team from day one. They in person or virtually sit beside the users, listen to how the business actually runs, and listen for digital friction.

They’re not junior analysts passing notes up the chain—they’re senior CRM and revenue experts - they’re in the top 1% of their field. And they stay with the client.

Second—the way we work. 

We don’t start by asking clients for requirements. 

Instead, every project starts with a testable hypothesis produced by a highly experienced Architect.  Our architects use our proprietary Copilot AI agents trained on industry data, revenue patterns, go‑to‑market motions and jobs to be done to produce a hypothesis about the client requirements. 

It’s easier for a client to react to that and find the tweaks.   Plus, we can send that out more widely, letting more people in diverse roles react. That’s easier than starting from scratch, and we get more feedback. 

Then in the workshops with clients, we can focus on the delta - not starting from scratch.
That’s how we move faster, reduce rework, and deliver outcomes right the first time.

Ever felt like there were too many people on a project you were working on? 

#GetCRMRight #OutcomesNotHours #AIFueledRevenue #BecomingFrontier

I Change the Way Intrapreneurial Chief Revenue Officers and CIOs, Use AI and CRM as Fuel so They Can Outgrow Their Competition.

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