Fernando Academia Jr., CDMP, PCM

C&S Cabinets

Full Stack Marketer

Concertina Team

CIIT College of Arts and Technology Inc.

Instructor

Netcore Cloud

Customer Success Manager

TL;DR Digital Marketing

President & CEO

Brand30

Member

BorderBuddy

MoEngage Inc.

Autogrow

Marketing Strategist

Growth Blazers

Full Stack Marketer | Digital Marketing Instructor | Speaker/Trainer

Over the last decade, I’ve worked across FinTech, E-commerce, and SaaS, helping businesses unlock the power of digital marketing and MarTech.

In 2020, I realized that the strategies and successes I’ve helped others achieve could be applied to my own ventures, allowing me to assist even more businesses in my own way.

I took action.

I joined masterminds and business groups, executed campaigns firsthand, and consulted as a marketing strategist and customer success expert. Through these experiences, I’ve built a growing community of professionals eager to learn and apply the strategies I’ve developed.

Now, I’m preparing to launch my newsletter, Future-Forward Marketing, where I’ll be sharing actionable insights that have fueled my success.

My mission: To help you master the complexities of MarTech and digital marketing, turning them into powerful growth engines.

My results:

→ 10+ years of experience driving growth in FinTech, E-commerce, and SaaS
→ Managed multimillion-dollar portfolios, ensuring high retention and growth
→ Educator at CIIT Philippines, empowering the next generation of digital marketers
→ Preparing to launch Future-Forward Marketing, a newsletter packed with actionable insights and strategies

If you’re looking to build a robust, data-driven marketing strategy, I’m here to help.

Fernando Academia Jr., CDMP, PCM, CSSWB

Last year, I watched an enterprise burn through a massive budget on a platform their organization couldn't digest.

The technology was perfect.
World-class vendor.
Proven at scale.
Executive champion.
Unlimited budget.
External consultants.
Everything textbooks say you need.

Six months later? Dead.

Not because the platform failed. Because the organization couldn't absorb it.

After 15 years implementing marketing technology across Southeast Asia, I've seen this pattern destroy more transformations than any technical failure:

Organizations have an absorption capacity. Exceed it, and everything fails.

Think of your organization as having a digestive system for change. Feed it too much, too fast, and it rejects everything. Even the good stuff.

I learned this lesson repeatedly across enterprises serving millions of customers.

Major platform implementations failed not because the technology was wrong. 

They failed because they required:
- Workflows that didn't exist
- Skills nobody had developed
- Cultural shifts nobody prepared for
- Integration patterns that weren't documented
- Governance models that weren't designed

This painful education led me to develop a MarTech Maturity Framework that measures absorption capacity across four dimensions:

*Integration Debt: How many manual handoffs exist between systems?
*Data Debt: How fragmented is your customer information?
*Process Debt: How many workflows still run on spreadsheets?
*Vendor Debt: How much overlap exists between tools?

The rule is simple: Score below 60% on any dimension, and new technology multiplies problems instead of solving them.

One client scored 85% on technical capability but 35% on process maturity. We spent six months fixing processes before touching technology. Result? 22x ROI improvement using mostly existing tools.

Vendors will never tell you this.
Consultants won't admit it.

But here's reality:
Most organizations can successfully absorb about 30% of what vendors promise.

Not because vendors lie.
Because organizations overestimate their readiness.

You wouldn't run a marathon without training. Yet companies attempt digital transformation without building organizational fitness first.

The solution isn't moving slower. It's building absorption capacity systematically. Strengthen your foundation before adding weight.

Your Absorption Reality Check:
- How many transformation initiatives has your organization "completed" versus actually adopted?
- What percentage of your tech stack features do teams actually use?
- How many platforms solved one problem but created three new ones?

If you're uncomfortable with the answers, you're not alone.

Want to dive deeper? I've just published below a comprehensive analysis combining insights from Oxford Saïd Business School's latest research with real-world implementation patterns across telecommunications, fintech, and digital platforms.

#DigitalTransformation #MarTech #AITransformation #ChangeManagement #OrganizationalChange

Fernando Academia Jr, EMBA, CDMP, PCM

Marketing Technology | Building SukiNomics | 15+ Years helping eCommerce/SaaS/FinTech scale with MarTech & Strategy

View newsletter: Future-Forward Marketing

Insights from Saïd Business School, University of Oxford's latest webinar on intelligent systems, validated through 15+ years implementing enterprise 

Open article: From Oxford to Operations: 10 Critical Lessons on Leading AI Transformation That Actually Works by Fernando Academia Jr, EMBA, CDMP, PCM • 9 min read

From Oxford to Operations: 10 Critical Lessons on Leading AI Transformation That Actually Works

Your marketing team has more tools than ever. They're using less than a third of them.

Every modern marketing organization runs on four foundational systems:

→ CDP (Customer Data Platform): Your single source of truth for customer data. Unifies profiles across every touchpoint.
→ DAM (Digital Asset Management): Your single source of truth for brand content. Eliminates hours wasted searching for the right assets.
→ MAP (Marketing Automation Platform): Your engine for scalable engagement. Delivers the right message at the right time.
→ CXM (Customer Experience Management): Your orchestration layer for the entire customer journey. Connects acquisition to advocacy.

Together these four markets exceed $50 billion.

The growth trajectory is strong. 
The utilization rate is not.

McKinsey found not one of 50+ senior marketing leaders could clearly articulate their martech ROI. The problem is not the technology. It is how we buy and deploy it.

Three insights that separate winners from laggards:

→ First-party data readiness is now a competitive moat. Those without unified customer data are scrambling as third-party cookies disappear.

→ AI integration is table stakes, not differentiation. The real advantage comes from AI working across all four pillars, not in isolated point solutions.

→ Fewer tools with deeper integration beats more tools with shallow adoption. Organizations with composable architectures deploy new capabilities 80% faster.

The winners in 2026 are not the ones with the biggest martech budgets. They are the ones who can actually measure what that budget delivers.

Full analysis with 2025 market data and strategic recommendations below:

TLDR The Core Four martech stack (CDP, DAM, MAP, CXM) now exceeds $50 billion in combined market value, with CXM and CDP showing the fastest growth th

Open article: The Core Four martech stack in 2025: A $50B+ ecosystem reshaping customer engagement by Fernando Academia Jr, EMBA, CDMP, PCM • 10 min read

The Core Four martech stack in 2025: A $50B+ ecosystem reshaping customer engagement

Enterprise tools do not fail because of bad technology. Hear me out…

I’ve seen dozens of enterprise tool implementations fail or fall way behind schedule.

Same pattern every time.

Marketing buys a CDP. IT refuses to integrate it. Analytics team never gets access. Finace never brought in to understand brand & longterm ROI.

Years later, the platform sits unused while everyone blames each other.

The real problem was simpler.

Nobody wanted to collaborate.

#Digitaltransformations require co-ownership. The tools do not care about your org chart.

They require integrated data flows, unified governance, and cross-functional decision-making.

When you purchase an enterprise tool, you are not buying software. You are buying an org-wide commitment to work differently.

That means:

#Marketing must explain business outcomes in technical terms IT understands.

IT must translate infrastructure constraints into business impact.

Analytics must make data actionable for both teams.

Finance must understand ROI beyond immediate cost reduction.

Every successful implementation I have seen had one thing in common. Teams who treated the tool as a shared asset requiring shared accountability.

Every failure had people who wanted results without collaboration.

You cannot #lead transformation initiatives alone. Not because you lack the skills. But because enterprise tools touch multiple systems, multiple teams, and multiple workflows.

The question is not whether you can drive the project. The question is whether you can get everyone to own the outcome together.

#Stakeholdermanagement is not politics.

It is the actual work of making technology deliver business value.

No alternative text description for this image

The expectations of executive leadership are shifting. In #digitaltransformation, the C-suite can no longer lead through decks, dashboards, and delegation alone.

I saw this firsthand during a week-long executive program at UCD Smurfit in Dublin. We analyzed why GE Digital failed while Airbnb succeeded. We visited Pinterest, HubSpot, and Google.

One pattern emerged across every case: the gap between technology capability and organizational capability. That gap closes only when executives get close to the work.

The Magazine Luiza transformation in Brazil shows what this looks like.

When Frederico Trajano became CEO in 2016, he faced a choice. Follow the Silicon Valley playbook of burning cash for growth, or find another way.

He chose neither separation nor delegation. He personally incubated a technology unit of 55 engineers, eventually growing to 850. He housed them in a secluded corner of headquarters with one instruction: build digital tools for both online and offline businesses with total focus on the end user.

The result was a 7,900% stock price increase over four years. The company transformed from a traditional retailer into a tech company.

The Thinkers50 Transformation Playbook captures the key insight. If a technology unit is left to fend for itself inside a larger organization, it will get rejected.

It needs to be separated and incubated by the CSuite, who should give it opportunities to get initial credibility through small wins.

This is why executive sponsorship is the connective tissue in digital transformation.

Harvard Business School research calls this the “use of self.” Leaders model future behaviors early in the process. They visibly tackle new challenges outside their comfort zone. This serves as a template for others to follow.

In #MarTech, the same principle applies. You cannot lead a CDP implementation from a quarterly business review. You cannot transform customer experience through filtered reports.

HubSpot  CEO Yamini Rangan  recently shared what works instead:

Do gemba walks. Sit with your team. Watch how the work happens. See where the friction is.

Triangulate feedback. Listen directly to customers, partners, and employees.

Experiment with #AI yourself. Write prompts. Share what you learn. You cannot #lead transformation if you are not learning the tools shaping it.

Communicate relentlessly. Share what is known, what is unknown, and how decisions are made.

As Rangan puts it, the best C-suite leaders today lead by doing. They provide clear direction and then protect the space for teams to execute.

Sources: 
- Thales Teixeira, Leandro Guissoni, and Frederico Trajano, An Unlikely Case of Successful Digital Transformation, The Transformation Playbook, Brightline Initiative/Thinkers50.
- Ryan L. Raffaelli, Leading Organizational Change, Harvard Business School Core Curriculum, 2017.
- UCD Michael Smurfit Graduate Business School Digital Transformation Executive Program, Dublin, 2025.

A candid, documentary-style photograph from the side of a conductor's podium. A single hand, cufflink visible on a suit sleeve, holds a baton steady in mid-air, commanding attention. The conductor’s body is partially visible but anonymous. The background orchestra is a wash of indistinct shapes and warm stage lights, representing diverse, fragmented elements waiting to be aligned. Shallow depth of field. The mood is authoritative and singular.

I’ve watched the same pattern destroy implementations across fintech, government, ecommerce, healthtech, and SMEs—and it’s not what you think.

Over the past 15 years working with enterprise clients on martech implementations, member management systems, and #digitaltransformation projects, I’ve reviewed my notes and found one consistent thread:

80% of #martech implementations fail to deliver their promised value—and the failure point is almost never the technology.

𝐈𝐭’𝐬 𝐭𝐡𝐞 𝐚𝐛𝐬𝐞𝐧𝐜𝐞 𝐨𝐟 𝐬𝐮𝐬𝐭𝐚𝐢𝐧𝐞𝐝 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐦𝐞𝐧𝐭 𝐚𝐟𝐭𝐞𝐫 𝐭𝐡𝐞 𝐩𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐨𝐫𝐝𝐞𝐫 𝐢𝐬 𝐬𝐢𝐠𝐧𝐞𝐝.

The data backs this up:
• 97% of mid-sized enterprises reported a martech failure that negatively affected customer relationships in the last 12 months .
• 72% fail from lack of post-launch monitoring .

Across every sector I’ve worked in—from government agencies managing constituent data to healthtech companies orchestrating patient journeys—the pattern is identical.

The Core Four stack (CDP, DAM, MAP, CXM) gets approved at the executive level, then delegated to mid-level leaders who lack the authority to make it work.

Here’s what I’ve observed goes missing:
→ No one mandates data sharing between Sales and #Marketing.
→ No one resolves the turf war between Acquisition and Retention teams.
→ No one reallocates headcount from legacy system maintenance.
→ No one absorbs the political cost of standardizing processes across business units.
→ No one owns the compliance risk of unified customer data.
→ No one protects the 18-36 month transformation timeline against quarterly pressure.

𝐓𝐡𝐞𝐬𝐞 𝐚𝐫𝐞𝐧’𝐭 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬—𝐭𝐡𝐞𝐲’𝐫𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐝𝐞𝐬𝐢𝐠𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞-𝐥𝐞𝐯𝐞𝐥 𝐚𝐮𝐭𝐡𝐨𝐫𝐢𝐭𝐲.

When Gartner surveyed marketing leaders, they found that successful implementations have one thing in common: active C-suite involvement in vendor selection, change management, and continuous oversight.

This isn’t about blame.

It’s about recognizing that the Core Four requires a different kind of leadership engagement than traditional software purchases.

Read on below to understand what real executive sponsorship requires—and why the gap between leaders and laggards widens every quarter you wait.

The Core Four martech stack is not a technology decision. It is an organizational design decision. And that is why it keeps failing without executive 

Open article: The Core Four: Why your Martech Stack is an Organizational Problem by Fernando Academia Jr, EMBA, CDMP, PCM • 4 min read

The Core Four: Why your Martech Stack is an Organizational Problem

QIMA

As the global Head of Data for QIMA, I oversee the company's artificial intelligence (AI), business intelligence (BI) and data engineering teams with a focus on generating actionable, data-driven insights for the business.  

QIMA is one of the world's leading supply chain analytics companies with labs and offices in 100+ countries around the world.

Head of AI and Data

Kolea Consulting LLC

📊 Designed, built, and implemented client solutions related to data strategy & data project prioritization, predictive analytics, and data visualization for clients in North America.

🤝 Worked closely with the Vice President of Analytics at CGI to create the data strategy for one of Canada’s largest provincial governments. The project had a 5-year budget of $40 million to develop a Data Management Office for the province. Data Quality, Data Literacy, Cybersecurity, and Organizational Change Management were my primary focus areas. 

🖥️ Co-led an Agile analytics team with CGI to design, build, and implement an analytics system using R Shiny web applications for one of British Columbia’s public health departments.

Director Data Science

upGrad

🎓Designed and delivered live teaching sessions for students enrolled in upGrad's professional data science and business analytics certificate program in partnership with the University of Maryland Robert H. Smith School of Business.

📊 Topics taught included Python, Deep Learning, Convolutional Neural Networks, NLP, Text Analytics, Exploratory Data Analysis, and Statistics.

🗣️ Feedback from students:
     "Courtlin was excellent in his presentation and shared his experience well."
     "Loved the session. One of the best sessions so far."

UpGrad Expert - Advanced Machine Learning, Data Visualization and Statistics Instructor

Philip Morris International

📈 Led the data-driven decision-making transformation as head of the Enterprise Analytics department (reporting directly to the Finance Director for Indochina) while promoting the use of self-service analytics.

🧑‍🤝‍🧑 Recruited and supervised a team of junior, senior, and manager-level analysts while guiding their professional development.

📋 Developed comprehensive standard operating procedures for the analytics division to improve the consistency of operations and assist with new hire onboarding.

🛠️ Managed the project team that built the company’s business intelligence system (covering its sales, distribution, and operational functions) using SQL, Python, R, and Microsoft Power BI. Created the data model design and overall data architecture for the BI system.

🤝 Operated as the primary analytics business partner to support internal stakeholders with comprehensive analyses of issues, including the performance of new product launches, geospatial analysis for distribution routes, customer segmentation, and customer profitability (ROI).

🎯 Developed an improved sales forecasting methodology using Python that was more accurate than the company’s preexisting, manually derived sales forecasts. (Forecast error was reduced by approximately 67%)

⏱️ Reduced the time spent by analysts in the Sales Development, Distribution, and Marketing departments gathering data and preparing routine management reports by 10-20 hours per month per department by measuring report usage, eliminating unnecessary reports, and leveraging Power BI automation. The time saved was used to conduct deeper, more insightful analysis of the underlying data.

👥 Led the development of a B2B customer segmentation model using unsupervised clustering algorithms that challenged several long-held company beliefs regarding which customer segments were growing and where they were located. The results were used to generate various B2B customer personas to improve trade promotion program design.

Director of Enterprise Analytics Vietnam

Data Scientist

En Pointe Management Corporation

💼 Provided internal business plan consulting, financial modeling, business process optimization, competitive marketing research & business intelligence to multiple subsidiaries within our group of companies in Vietnam.

🏆Accomplishments 

🔍 Designing, overseeing, and analyzing the competitor, market, and consumer intelligence data collected by the department’s research analysts to generate actionable insights and strategic recommendations for the Board of Directors.
💹 Creating financial forecast models for potential acquisitions.
📊 Developing KPIs, dashboards, and business scorecards for subsidiaries to align their strategic and operational priorities.
📑 Conducting business feasibility studies, developing business plans, and designing sales forecasts for potential new ventures.
🤝 Structuring and negotiating partnership agreements with counterparts from the US, EU, and South East Asia region.
📅 Assisting subsidiary companies with the creation of annual strategic plans and budgets.

* Skills Required
Business intelligence  data analysis, financial analysis, financial modeling, strategic planning

En Pointe provides core business functions including Human Resources, Digital & Information Technology, Finance & Accounting and Business Development to enable each company in our group to excel at what it does best. Our Group companies include:

1) Purpose Media 
2) Novel Production
3) Amberstone Media
4) Billboard Vietnam
5) INQ
6) Renaissance Collection
7) Embassy Education
8) AMPA Education
9) Soul Music & Performing Arts Academy - SMPAA

Director Of Business Intelligence

Kolea Consulting Group manages a portfolio of machine learning and AI-powered businesses serving customers in North America and Southeast Asia.

Director Consulting Services

Diamond Head Financial Advisors

Managed the firm's value-oriented investment strategy by conducting primary research on investment ideas, selecting securities, executing trades and managing portfolio risk.

Created the firm's summer intern equity investment analyst training program. Many of graduates from the training program went on to secure investment banking job offers.

Developed strategic partnerships with 3rd party financial services providers.     

Software: 
Bloomberg Professional, Factset, TD Ameritrade Institutional ThinkPipes, TD Ameritrade ThinkOrSwim, AssetBook, Morningstar

Portfolio Manager & Vice President of Business Development

Analyzed and pitched long/short equity investment ideas to the firm's Chief Investment Officer. Built financial models and valued companies on the basis of DCF, relative value and LBO methodologies. Covered companies in the technology, healthcare and industrials sectors. Executed trades and rebalanced client portfolios on behalf of the CIO.

Registered Investment Advisor (RIA)

Investment Research Analyst

Gigaisland LLC

Created an in-house firewall Global Management System; reduced service delivery costs by 87%.
Developed and implemented the firm’s cybersecurity assessment product offering.
Refined and documented the firm’s core operational procedures into a series of SOP manuals.
Remotely managed several hundred client PC and Mac workstations and several dozen Windows servers.
Conducted cybersecurity assessments for corporate clients, identified vulnerabilities and recommended improvements.
Designed hardware and software solutions to protect clients from malware, ransomware and spyware.

IT Systems & Cyber Security Analyst

State of Hawaii Employees' Retirement System

Served as the State of Hawaii public pension fund's Investment Specialist throughout the Great Recession of 2008-2011. Assisted the Chief Investment Officer in the management of the State's $10.5 billion employee pension plan. Monitored the monthly performance and operations of the Plan's 30+ outside investment managers. Conducted reviews of several hundred prospective third-party investment managers. Created a comprehensive cash flow model for the pension fund to model various investment return scenarios. Rebalanced the Plan's asset allocation on a quarterly basis. Analyzed RFP responses for the Plan's private equity, venture capital and large-cap equity mandates.

Investment Specialist

As the Head of Data for QIMA, I lead the design, development, and implementation of data-driven solutions for the company and its clients. I have over 10 years of experience in data analytics, business intelligence, and machine learning, as well as various Microsoft certifications in data analysis and data science. 

My mission is to help organizations leverage data to optimize their operations, enhance their performance, and achieve their goals. I have successfully delivered data strategy and BI projects for one of Canada's largest provincial governments, one of British Columbia's public health departments, and Philip Morris International, where I headed the Enterprise Analytics department for two years. I also have a solid background in finance, risk management, and consulting from my previous roles. I enjoy teaching and sharing my knowledge on advanced machine learning, data visualization, and statistics as an instructor for upGrad.

Courtlin Holt-Nguyen

“Nobody in their right mind would use LLMs of the type that we have today" within 3-5 years, he said at MIT last month. (Yann LeCun)

His alternative: world models. Systems that learn from visual data and build causal understanding, like a child learning physics by dropping things thousands of times.

Global Head of AI and Data @ QIMA | AI Strategy & Innovation Leader | Driving Business Value through AI/ML & Data | ex-Fortune 500 Analytics Head, AI Engineer, Hands-on developer

Founder & CEO, Curiouser.AI | Berkeley Instructor | Building Values-Based, Human-Centered AI | LinkedIn Top Voice in AI

Yann LeCun just left Meta.
Let that sink in.

One of three people who invented modern deep learning. Turing Award winner. 

Chief AI Scientist at a company spending billions on AI.

Gone.

Not to a competitor. Not to retirement. 
To a startup building technology 
Meta apparently doesn't prioritize.

This matters more than most people realize

LeCun has spent 40 years being early and right. 

In the 1980s, he couldn't find a PhD adviser because machine learning was "a dead end." 

Three decades later, that dead end became the foundation of modern AI.

Now he's saying large language models, the technology behind ChatGPT, Gemini, and Meta's Llama, are another dead end.

"Nobody in their right mind would use LLMs of the type that we have today" within 3-5 years, he said at MIT last month.

His alternative: world models. Systems that learn from visual data and build causal understanding, like a child learning physics by dropping things thousands of times.

Current LLMs? 
They predict the next word. 
They don't understand gravity. 
They don't know that objects exist when you're not looking at them.

LeCun compares their intelligence to a cat's. He thinks the cat is smarter.

Here's what troubles me:

Meta is spending countless billions on technology that its own Chief AI Scientist publicly called a dead end. 

Either LeCun is wrong (possible, but his track record suggests otherwise), or Meta is chasing market narratives over technical reality.

His departure suggests he's betting on the latter.

The implications for current AI investments are significant. If LeCun's 3-5 year timeline holds, companies built entirely on LLM scaling may face architectural obsolescence.

That's not a guarantee. Even geniuses miss sometimes.

But LeCun has never been wrong about something this big before
********************************************************************************
The trick with technology is to avoid spreading darkness at the speed of light.
Stephen Klein
Founder & CEO, Curiouser.AI
Instructor, UC Berkeley Haas School of Business
WeFunder (link in comments)

Sources:

Wall Street Journal - LeCun departure and startup plans (November 2024)
MIT Symposium - LeCun remarks on LLM limitations (October 2024)
Meta FAIR Research Publications - World model architectures
Turing Award Archives - LeCun, Bengio, Hinton recognition (2018)

During the last 8 years, I have worked in Vietnam managing global teams for several MNCs. I’ve become convinced that regardless of whether a company operates in Asia, North America or the EU, more often than not, corporate “standard” operating procedures resemble a steaming bowl of pho noodle soup - i.e. a tangled mess of half-submerged surprises that is dangerous to dive into too quickly. This is workable for human-based operational processes but it becomes a serious problem when you’re trying to design an AI system to automate a complex process.

“Garbage in garbage out” doesn’t just apply to the data but also to the business processes that an AI system is supposed to replicate. 

I remember advising one CIO that the company should first hire business process engineers to sort out the SOP chaos rather than hire more AI engineers and data scientists. He agreed, but in that particular case, the company had a mandate to “use AI everywhere ASAP”, so they hired more AI engineers and threw them at the project…6 months later, the agentic POC failed because the company’s SOPs were deemed “too complicated“ and “required too much human judgement to automate.” In reality, the SOPs were either incorrect or incomplete so a human was required to resolve most cases; sadly, the LLM never stood a chance. 

If you want to use agentic AI in your business, spend the time to clarify and document what actually happens in your operational workflows BEFORE trying to automate it with AI…that way you can save the pho for lunch ;)  

Daliana Liu - from Vietnam 🇻🇳

Global Head of AI and Data | Driving Strategic Business Value through AI/ML & Data Solutions | ex-Fortune 500 Analytics Head | Speaker | Author

Impressive how much can be done now with minimal pre-trained CV tools.

🤖 Generative AI Lead @ AWS ☁️ (150k+) | Startup Advisor | Public Speaker | AI Outsider | Founder Thinkfluencer AI

AI Engineer builds a real-time potato counter using just ONE training image.

Most people assume you need huge datasets and large models to build anything useful with Al. Well, this project proves otherwise.

A senior Al engineer created a real-time potato counter for a conveyor belt using a tiny setup.
No custom dataset. No heavy model. Just one annotated frame.

He used Ultralytics ObjectCounter with a YOLO11 nano model, labeled a single image with SAM 2, and trained it.

That one frame was enough for the system to track every potato in the entire video.

This is a solid reminder of what real industrial Al looks like. Simple. Efficient. Built for one clear job.

These focused systems save time, cut errors, and work without big infrastructure. In other words, this is the future!

#ai #physicalai

There is a strange comfort in the chaos of AI projects.

You walk in with a plan, a roadmap, a stack of best practices. You think you know how the story will go. But then the data is messier than you expected. The business priorities shift. The model that looked so promising in the lab suddenly falls apart in production, like a sandcastle at high tide.

And you realize, again, that the real work is not in the code or the math. It is in the conversations. The late-night Slack threads with product managers who are quietly panicking. The whiteboard sessions where you try to explain why the model needs more data, not just more compute. The moments when you have to say, "I don't know yet, but we'll figure it out."

This is the part nobody puts in the case studies. The uncertainty. The improvisation. The way a team learns to trust each other, not because everything goes right, but because they survive the things that go wrong.

Truth is, the best AI teams are not the ones with the fanciest algorithms. They're the ones who can adapt, who can laugh at the absurdity, who can keep moving when the ground shifts under their feet.

If you want to build real value with AI, start by building that kind of team. The rest is just details.

There is a myth that AI teams are made up of lone geniuses.

The reality is much messier. The best teams I have worked with are a patchwork of personalities, backgrounds, and skill sets. Some are obsessed with the math. Others are relentless about the business case. A few just want to build cool stuff and see what happens.

The magic happens in the friction. The arguments over feature selection. The debates about what "success" really means. The moments when someone asks a question that no one else thought to ask.

You cannot fake this kind of collaboration. You have to create space for it. Psychological safety is not a buzzword, it is the foundation.

If people are afraid to be wrong, you will never get the right answers.

So yes, hire for skill. But more importantly, build for trust. That is how you get teams that do more than just ship models. They move the business forward.

And honestly, that is the only metric that matters.

This is a key piece of the explanation why so many studies (MIT, McKinsey) are reporting little financial impact from genAI. 

When the complexity of a coding project exceeds the POC stage, continuing to rely on a flaky AI coding assistant that refuses to follow instructions becomes a serious problem. The 5-sentence prompt that vibe coded the POC is not going to cut it. 

Really interesting Alex Turnbull , thanks for sharing.

Bootstrapped Groove from $0–$5M ARR solo. Now rolling it into a holding co. for CX SaaS. Launching Helply, InstantDocs & ZeroTo10M to scale $0–$10M ARR w/ 50%+ margins. Sharing it all at ZeroTo10M.com.

AI coding tools traffic collapsed 76% in 12 weeks.
10,000 startups got prototypes working, then 57% hit the complexity wall. That's a $400M-$4B rebuild market nobody's talking about.

Base44 spiked 950% in May, then crashed to 95% by October.
Lovable went from +207% to -37%.
Cursor dropped from +62% to -19%.

The pattern was identical across every AI coding platform.

Massive adoption surge, then 60-80% user decay within three months.

These tools made it easy to build prototypes fast.
But integration, governance, security, and reliability stayed hard.

The average user tried for 12 weeks, hit the wall, and churned.

Most founders who went all-in on "vibe coding" is now paying the rebuild tax.

They got an MVP up.
Found traction, raised a pre-seed, or onboarded early customers.
Then everything broke when real users showed up.

Now they're spending $200-300K hiring senior engineers 
to re-architect everything.

Multiply that across thousands of teams.
Hundreds of millions in wasted spend.

Everyone could suddenly build something.
Now everyone's learning what it costs to make it actually work.

This is a market correction. 

You can't outsource architecture to autocomplete.

System design
Error handling
Deployment, compliance.

All the unsexy stuff that keeps products alive 
multiplies the need for engineers. 

The gold rush now is rescuing companies that got stuck 
with working demos they can't scale.

If 10,000 startups experimented
say 8,000 will need help getting unstuck.

With budgets between $50K and $500K each.

That's the real market AI coding created
The money's in the valley of this chart.

Your BI program is burning millions of dollars.

But not on the technology.

On the invisible costs nobody talks about.

I've watched Fortune 500 companies spend 60-80% of their analytics budget on something that doesn't appear in any dashboard or ROI calculation:

Data reconciliation.

Here's what's actually happening:

Your sales team trusts the CRM numbers. Finance believes the ERP data. Marketing swears by their attribution platform.

They're all looking at the same customers. But seeing different revenue figures.

The real cost?

Your analysts spend their days playing data detective instead of delivering insights. Reports arrive weeks late because someone needs to "validate the numbers" first.

Executive meetings turn into debates about whose data is correct rather than what decisions to make. Or worse, every meeting generates follow-up meetings to validate the data.  

The brutal truth about BI failure:

It's not a technology problem. It's an alignment problem.

You bought the best analytics platform money can buy. But your data still lives in silos. Your metrics still lack clear definitions. Your insights still don't connect to strategic goals.

I've seen this time and again with the companies I've worked for; world-class tools, talented teams, substantial budgets but the same result. 

Our BI program struggled until we stopped asking "what data do we have?" and started asking "what decisions do we need to make?"

The shift?

Define 3-5 strategic outcomes first. Then build the data infrastructure to support those specific decisions.

Not the other way around.

When you have 20 minutes, watch this on 1.5x speed. It’s a fascinating video summary of the book “If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All”. 

This isn’t doom and gloom AI where Terminator-style robots come for humanity (see Musk’s plan to have “helper” police bots following each of us around all day to stop crimes before they happen for that scenario.) 

Instead, it’s a logical, step-by-step analysis of how super intelligence could escape from the AI research lab and then continue to obtain the resources it needs to improve itself beyond human control. The use of social media influencers (some human, some AI-generated) to manipulate public opinion, encoding messages to its future self inside its own made-up mathematical language, creating bioweapons to weaken humanity’s response while simultaneously positioning itself as our savior with DNA-based vaccines, the intentional sabotaging of rival AIs (and even hobbling smarter future versions of itself so they don’t deceive it the same way it would have deceived us) will give you plenty to ponder this weekend. 

Link to the YouTube and original book are in the comments.

Flashy investments may grab the headlines, but the boring, mundane ones often have the best economics.

CEO at Novaro Capital • $9bn+ of Transaction Experience • Investing in Niche Real Estate Opportunities

Everyone's fighting over gigawatt data centers. The real AI infrastructure play is sitting empty in your city.

That old warehouse with 5 MW of power? It's worth 10x what it was three years ago. And nobody's paying attention yet.

-The Hidden AI Gold Rush-

While Meta builds $29B facilities for training models, the actual AI revolution needs something different: distributed compute for real-world applications.

Self-driving cars can't wait for data to travel to Virginia and back. Drones need sub-10ms response times. Manufacturing robots require local processing to avoid catastrophic delays.

These applications don't need 500 MW campuses. They need 1-10 MW facilities within 50 miles of where they operate.

-Why These Sites Are Suddenly Valuable-

Power availability in urban areas is the constraint. New utility connections take 2-4 years. But existing sites? Already connected.

An old warehouse with 5 MW can become an edge data center in 9 months. Try getting that power allocation new - you'll wait until 2029.

The economics:
- Acquisition: $5-15M
- Conversion: $10-20M  
- Value post-conversion: $50-80M
- Timeline: 9-12 months vs 3-4 years

-Who's Already Moving-

Vapor IO is dropping micro data centers at cell towers - supporting autonomous vehicle corridors. Partnering with Hangar for drone operations.

EdgeConneX raised $1.9B to convert suburban facilities. They're buying existing industrial sites with power in place.

DataBank is turning dead office buildings into edge facilities. 2-10 MW conversions where nobody's looking.

Locus Robotics needs edge compute for warehouse robots. Every fulfillment center running AMRs needs local processing.

-The Use Cases Driving Demand-

Autonomous Vehicles: Waymo and Cruise need compute every 10-15 miles. Can't process in the cloud at 45mph.

Drone Delivery: Amazon Prime Air, Zipline require local processing for collision avoidance.

Smart Manufacturing: Boston Dynamics robots need sub-5ms latency. Compute must be within 10 miles.

Healthcare: Surgical robots can't risk network delays. Hospitals are building their own edge facilities.

-What Makes a Site Valuable-

Power: 1-10 MW available capacity
Location: Within 50 miles of population centers
Fiber: Proximity to major routes
Zoning: Industrial/flex allowing data center use
Loading: Existing docks for equipment delivery

-The Window Is Closing-

We're seeing 3-5x appreciation on these sites. A client bought a 3 MW Dallas building for $8M in 2022. Current offers exceed $25M.

This arbitrage won't last. Once institutional capital realizes edge is where AI deployment happens - not training - these sites disappear.

Smart money is quietly accumulating these properties. Not for industrial use. For the infrastructure that makes autonomous systems work.

The hyperscalers are building AI brains. These edge sites? They're the nervous system making AI useful.

Who else is tracking this shift from centralized to distributed AI infrastructure?

Before joining a new company, you should take a look at its corporate values to make sure they are aligned with your values, right?
Maybe, but not for the reasons you might think. According to an intriguing study from MIT of 1.2 million employees at 500 large companies:

"The analysis reveals that there is no correlation between the cultural values a company emphasizes in its published statements and how well the company lives up to those values in the eyes of employees. All of the correlations between official and actual values were very weak, and four of the nine — collaboration, customer orientation, execution, and diversity — were negatively correlated." 

The negative correlations are fascinating. If a company claims to be customer-focused, diverse, collaborative or results-oriented, it is LESS LIKELY to actually exhibit those values according to its own staff. 

This argument makes sense if we consider the perspective that corporate values are aspirational rather than descriptive of the current environment. In other words, companies seek what they don't currently have (or do).

Link to the full article is in the comments.

New HBR piece from Vipin Gopal, Tom Davenport, and Randy Bean makes an argument I've been watching play out across enterprise AI implementations:
The proliferation of C-level tech roles (CDO, CDAO, CAI Officer) is creating fragmentation exactly when organizations need unified execution.

Their proposed solution: The CDAIO (Chief Data, Analytics, and AI Officer).

What caught my attention:
-- 53% of companies have either appointed a Chief AI Officer, believe one is needed or are expanding existing data roles to include AI. That's a lot of organizational restructuring happening simultaneously.

-- Only a small fraction report positive P&L impact from gen AI so far. The authors argue this underscores the need for business-first AI leadership, not technology-first.

-- 42% of leading organizations now have AI/data leadership reporting to business or transformation leaders rather than IT. JPMorgan made their CDAIO part of their 14-member operating committee.

The uncomfortable truth buried in here: Many early CDO roles failed because expectations were unclear and ROI was hard to demonstrate when the mission focused solely on foundational data investments. 

The authors state "The failure of data initiatives to create commensurate business value has likely contributed to the short tenures of data leaders (to say nothing of doubts about the future of the role entirely). CDAIOs need to focus on business value from day one."

AI provides the channel to finally show the value of data investments. But it requires someone who can be both evangelist AND realist, inspiring the organization while quickly terminating projects that don't deliver returns.

The authors propose a 7-part mandate for CDAIOs:
1) Owning the AI strategy 
2) Preparing for a new class of risks 
3) Developing the AI technology stack for the company 
4) Ensuring the company’s data is ready for AI 
5) Creating an AI-ready culture 
6) Developing internal talent and external partner ecosystems 
7) Generating significant ROI for the company

The seven-part mandate they outline is ambitious. But after watching coordination problems kill promising AI initiatives, I think the consolidation argument has merit.

No offense to my Chief AI Officer friends out there, but my coauthors Vipin Gopal and Randy Bean and I think the job is best combined with a Chief Data and Analytics Officer role. Read this new article for our rationale and what we think the job should involve.
https://lnkd.in/esT4snwz

Open article: Why Your Company Needs a Chief Data, Analytics, and AI Officer by hbr.org

Why Your Company Needs a Chief Data, Analytics, and AI Officer

Sometimes I wonder if we overcomplicate AI strategy on purpose.

We draw elaborate frameworks, invent new acronyms, debate the merits of this platform versus that one. We talk about MLOps pipelines and model governance and responsible AI, and somewhere along the way, we forget what we were trying to solve in the first place.

I've seen teams spend months perfecting their infrastructure, only to realize the business problem was never clearly defined. Or worse, that nobody actually cared about the answer the model was supposed to provide.

Here's a radical thought: what if we started with the problem, not the solution? What if we spent more time with the people who actually use the output, and less time arguing about which cloud provider has the best GPU pricing?

The best AI projects I've seen are the ones where the tech fades into the background. Where the focus is on impact, not architecture diagrams.

Maybe it's time to get back to basics. Less noise, more signal.

What do you think?

This was true when I posted it 2 years ago and it still rings true today ;)

Chief Data, Analytics, AI Officers—it's not your fault; you were set up to fail

⚠️That’s the conclusion of a recent HBR article titled, Why Chief Data and AI Officers Are Set Up to Fail (June 2023), which everyone in a data leadership position should take a few minutes to read. 

💰Despite massive investments over the past decade in people + technology, and assurances to shareholders that “digital transformation” and “data-driven decision-making” initiatives were underway, senior corporate executives admit that only: 

1) 23.9% have a data-driven organization 
2) 20.6% have an established data culture 

🚫The other 80% of companies are beginning to blame their CDAIO for the lack of “insightful insights” and analytics-driven transformation. This is one reason for the high turnover in CDO / CAO / CAI roles I wrote about previously https://lnkd.in/gKYhQNws 

As the HBR article mentions:
“The CDAIO has been left on the hook for major projects that require huge investment and reach into all corners of the company, but often fail to deliver measurable benefit.

Even when they’ve done exactly what was asked of them, it can be hard to argue they’ve succeeded.

We believe that two factors largely led to this situation: the wrong focus and a lack of trust.”

📝 The continuously expanding scope of the CDO role has also contributed to the problem.
CDO Version 1 = primary focus was on data risk governance and regulatory compliance 
CDO Version 2 = CDO V1 responsibilities + data monetization expectations  
CDO Version 3 = CDO V2 + generative AI adoption 

❓What can CDAIOs do to fix the situation? 
The article suggests the following actions:

1) Make data everyone’s business - a sole CDAIO cannot be responsible for an entire organization’s data practices (governance + literacy).

2) Make business leaders champions for data projects - CDAIOs need to find a handful of leaders within the company who are ready and willing to invest in data & analysis with realistic expectations of what is possible. 

3) Review all data and AI investments to make sure funds are well spent - CDAIOs should concentrate their attention & budgets on must-have data projects with near-term business value instead of nice-to-have POC projects. 

4) Shift to an ecosystem mindset - CDAIOs need to cultivate relationships with vendors, universities and outside partners to build a successful analytics program and talent pipeline. 

5) Proceed with caution - be careful with new technologies like generative AI (ChatGPT) that bring new data-related risks to an organization. 

📍Here’s the link to the full HBR article: 
https://lnkd.in/gcU5wekH

Open article: Why Chief Data and AI Officers Are Set Up to Fail by social.koleaconsulting.com

Why Chief Data and AI Officers Are Set Up to Fail

I’ve lost count of how many times I’ve seen AI projects stall at the same place. 

The proof of concept works. The demo is slick. Everyone’s excited. 

Then reality hits. Integrating with legacy systems. Data that’s messier than anyone admitted. Stakeholders who suddenly have a hundred new requirements. 

It’s like running a marathon and tripping on the last mile. 

Here’s the thing nobody tells you: operationalizing AI is 80% negotiation, 20% code. 

You need translators, not just engineers. People who can bridge the gap between what the model can do and what the business actually needs. 

I’ve started to think of these folks as the unsung heroes of AI. The ones who keep the project alive when everyone else is ready to give up. 

If you’re building an AI team, don’t just hire for technical skills. Look for people who can navigate ambiguity, build consensus, and keep moving when the path isn’t clear. 

That’s how you get models out of the lab and into the real world.

A 55% smaller model. Same performance. Zero censorship. Here's how quantum physics made it possible and why it matters for your AI infrastructure costs.

Multiverse Computing just demonstrated something enterprise AI teams should pay attention to:

They took DeepSeek R1, one of the most capable open-source reasoning models, and compressed it by 55% using tensor networks borrowed from quantum physics.

The result performs nearly as well as the original while requiring significantly less compute.

Here's why this matters:
⚡ THE EFFICIENCY ANGLE
Most LLMs are bloated. Multiverse's approach identifies and removes redundancy with mathematical precision. That translates directly to lower GPU costs and energy savings.

🎯 THE CUSTOMIZATION ANGLE
The same technique that removed Chinese censorship can selectively add or remove other behaviors. Domain expertise. Safety guardrails. Brand-specific alignment.

🧭 THE STRATEGIC ANGLE
If you can compress and modify open-source models at the weight level, you're no longer dependent on API providers or original model creators for customization.

This is where AI infrastructure is heading: not just fine-tuning, but surgical model editing.

For enterprise teams running open-source models in production, the question becomes: are you optimizing what you deploy, or just accepting default weights?

Open article: Quantum physicists have shrunk and “de-censored” DeepSeek R1 by technologyreview.com

Quantum physicists have shrunk and “de-censored” DeepSeek R1

Most MLOps conversations are missing the point.

We talk about pipelines, versioning, monitoring, all the technical scaffolding. But the real challenge is not getting the model into production. It is keeping it useful once it is there.

Models drift. Data changes. Business needs evolve. The world does not stand still, and neither should your AI systems.

I have seen teams celebrate a successful deployment, only to watch the model degrade quietly over months. No one notices until the complaints start rolling in. By then, the trust is gone.

MLOps is not a checkbox. It is a mindset. A commitment to continuous learning, not just continuous integration.

If you want your AI to deliver real value, you need to treat it like a living product, not a one-off project.

That means investing in feedback loops, retraining, and honest conversations about what is working and what is not.

Otherwise, you are just automating yesterday's decisions.

LLMs out of the box continue to be “often wrong but never in doubt.”

WRONG 60% OF THE TIME:
ChatGPT. Gemini. Grok. Perplexity. Copilot. DeepSeek 

Premium models, $20 to $40 per month, performed WORSE than free versions.⁵ Higher cost. Higher confidence. Higher error rates.

Columbia University's Tow Center tested eight AI search engines on the simplest possible task: Given a direct excerpt from a news article, identify the headline, publisher, date, and URL.¹

Collectively, they gave incorrect answers more than 60% of the time.²

Not on complex reasoning.
Not on nuanced analysis.
Not on predicting the future.

On citing their sources.

A basic Google search gets this right instantly.

The individual results:

Grok 3: 94% wrong
Gemini: 1 correct answer out of 200
ChatGPT: 67% wrong
Perplexity: 37% wrong (best performer)

But the error rate isn't the real problem.

The confidence is

ChatGPT gave incorrect information 134 times. It expressed uncertainty just 15 times.³ It never once said "I don't know."

Never. Once.

Grok 3 didn't just get sources wrong, it invented them. 
Out of 200 queries, 154 citations led to fabricated URLs.⁴ 
Pages that don't exist. 
Evidence that was never there.
They're not making mistakes.
They're manufacturing proof.
And here's where it gets absurd:

Now think about where these tools are being deployed.

Writing code. Summarizing contracts. Informing investment decisions. Drafting legal briefs. Generating medical information.

If they can't accurately cite a news article, the most basic intellectual honesty task, what else are they confidently fabricating?

The researchers didn't mince words: "Most of the tools we tested presented inaccurate answers with alarming confidence."⁶

This isn't a bug to be patched.

This is the architecture working as designed.

Large language models don't know things. They predict what sounds plausible. 

They've learned that confidence gets rewarded, accuracy be damned.

We're building critical infrastructure on foundations that fail 60-94% of the time
********************************************************************************
The trick with technology is to avoid spreading darkness at the speed of light.
Stephen Klein
Founder & CEO, Curiouser.AI
Instructor, UC Berkeley Haas School of Business
WeFunder (link in comments)

Sources:
¹ Columbia Journalism Review, Tow Center for Digital Journalism, "AI Search Has A Citation Problem," March 6, 2025 - Study analyzed 1,600 queries across 8 AI search tools
² Ibid. - "Collectively, they provided incorrect answers to more than 60 percent of queries"
³ Ibid. - ChatGPT "signaled a lack of confidence just fifteen times out of its two hundred responses, and never declined to provide an answer"
⁴ Ibid. - "Out of the 200 prompts we tested for Grok 3, 154 citations led to error pages"
⁵ Ibid. - Premium models "demonstrated higher error rates... tendency to provide definitive, but wrong, answers"
⁶ Ibid. - Direct quote from study authors

Your million-dollar AI project just failed.

The Product Manager said "we just need better predictions."

Let me tell you where this goes wrong.

I've led AI initiatives at various global companies and the the pattern is always the same:

Non-technical product managers create systematic failures through misaligned expectations, unrealistic timelines and poor data strategy.

Not because they're incompetent or unskilled.

Because they lack the technical vocabulary to bridge the divide.

Here's what actually happens:

Your business stakeholder says: "We need better customer recommendations."

Your PM nods and adds it to the roadmap.

Your data scientist asks: "Better how? What's the success metric?"

Silence.

So the AI engineer builds something. Ships it. 

And six months later, everyone's confused why it didn't deliver business value.

The real problem?

Your PM couldn't translate "better" into measurable technical requirements.

They approved the project without understanding:
- Data availability constraints
- Model performance trade-offs  
- Computational costs
- Ongoing maintenance needs

I've watched teams waste entire quarters this way.

Here's what changes the game:

Product managers who understand the full AI/ML lifecycle don't need to be data scientists.

But they do need technical literacy.

They need to know when a request is feasible versus impossible.

They need to set success metrics that align model optimization with business outcomes.

They need to communicate in both languages.

Because that translation layer?

That's where AI projects either create value or burn cash.

From my experience building and mentoring cross-functional AI teams: the organizations that succeed invest in technical foundations for their product managers.

The ones that fail keep treating AI like traditional software.

It's not.

And your product managers need to understand why.

Watson AI was a nice demo but it was very difficult to work with. This is an interesting post-mortem about the platform. 

The part about Watson Health’s failure is a good cautionary tale for organizations:

“Watson didn't fail alone.
It forced organisations to confront their own complexity too.
Hospitals discovered through Watson that:
- clinical notes are inconsistent
- workflows are fragmented
- ontology alignment is non-trivial
- "ground truth" in medicine is often contested

Watson revealed that organisations were nowhere near ready for the level of Al they claimed to want.
The data wasn't ready.
The workflows weren't ready.
The governance wasn't ready.”
The expectations certainly weren't ready.”

25+ years in AI | 39k+ Followers | MBA (AI) - BSc (Stats) - BCom (Finance) | Follow for real-world insights on AI, data & automation

IBM’s Watson Health wasn’t just one of the biggest AI failures - it was a $4 billion reminder that ambition without alignment is indistinguishable from delusion.

People keep flexing “I worked on Watson” like it’s a badge of honour.
It shouldn’t be.
Watson wasn’t early. Watson was wrong.

It was a great Jeopardy demo that collapsed the moment it touched the real world.

And it didn’t collapse quietly. We just stopped mentioning it loudly.

The trail it left behind was brutal:
- MD Anderson spent $62m and got nothing deployable
- Australian hospitals abandoned it after unsafe recommendations
- IBM sold off Watson Health in 2022 after years of losses

That isn’t innovation.
That’s a pattern.

People still parade Watson as proof of their AI credibility.
It should be the opposite.

Watson didn’t move the field forward.
The field simply moved past it.

While modern AI was built on deep learning, attention, transformers, self-supervision, scaling laws, RLHF & foundation models, Watson stayed anchored to symbolic scoring pipelines, brittle ontologies & offline indexing.

None of the breakthroughs that define today’s stack came from Watson.
None reference it.
None stand on its shoulders.

Watson didn’t evolve into the modern AI ecosystem.
It became a dead branch on the tree.
A shiny lab demo that never adapted to real-world conditions.

The story is not just a technical failure.
It’s also a story of what the field learned by watching the failure unfold.

And Watson certainly taught us something important.
It taught us what not to do.

It clarified the ingredients modern AI actually needs:
- end-to-end learning
- self-supervision
- scale
- probabilistic reasoning
- flexible representations
- uncertainty estimation
- RLHF-style alignment with human judgment

Watson failed at these, but in failing, it made them visible.
Its collapse did more to define the boundaries of the field than its success ever could.

Watson didn’t fail alone.
It forced organisations to confront their own complexity too.

Hospitals discovered through Watson that:
- clinical notes are inconsistent
- workflows are fragmented
- ontology alignment is non-trivial
- “ground truth” in medicine is often contested

Watson revealed that organisations were nowhere near ready for the level of AI they claimed to want.
The data wasn’t ready.
The workflows weren’t ready.
The governance wasn’t ready.
The expectations certainly weren’t ready.

So yes, Watson misled the market.
It overpromised.
It oversold.
It pushed the wrong paradigm.

But it also revealed the hidden complexity that everyone had been pretending was simple.

Watson is a reminder of what happens when:
- vendors oversell
- executives under-examine
- models overreach
- complexity is ignored
- narrative replaces reality

So the next time someone cites Watson as proof of their AI credentials, ask a simple question:
“What did it achieve in production?”

The silence is the real legacy of Watson.

Not the demo.
Not the hype.
The silence.

There’s a weird paradox in AI strategy right now. 

Everyone wants to talk about the next big thing. GenAI, autonomous agents and self-improving models. 

But when I look at the projects that actually deliver business value, they’re almost always... boring. 

Fraud detection. Churn prediction. Inventory optimization. 

The stuff that doesn’t make headlines but quietly saves millions. 

I get it, the hype is fun. But I’ve seen too many teams chase the shiny object, only to realize six months later that the ROI just isn’t there. 

The best AI leaders I know? They’re obsessed with the unsexy problems. They ask, "Where are we bleeding money? Where are we wasting time?" Then they build solutions that actually move the needle. 

It’s not glamorous. But it works. 

So here’s my question: Are we too distracted by the promise of AI to focus on the problems that matter?

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