I spent two days at HumanX 2026 in San Francisco covering 27 sessions across AI infrastructure, enterprise adoption, agentic systems, security, creative, retail, and finance.

I heard about AI in marketing. AI in brand strategy. AI in customer service. AI in software development. AI in creative production. AI in financial infrastructure. AI in cybersecurity. AI in retail design. AI in financial crime. AI in compliance.

I did not hear a single session about the supply chain.

Not one.

For someone who came up through operations and supply chain, who has personally sat with demand forecasting spreadsheets that were wrong more often than right, navigated supplier disruptions without early warning, and made inventory decisions that were really just educated guesses dressed up in numbers. For an industry that sits at the core of the global economy, the silence was striking.

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What we mean when we say “supply chain.”

To be precise, this is not about last-mile logistics or route optimization—the areas that do get occasional attention. The Supply Chain word gets used loosely, usually.

This is about upstream decision-making: demand forecasting, supplier risk management, inventory positioning, and procurement intelligence.

Demand forecasting: predicting what you’ll need to sell or produce before you’ve sold or produced it.

Supplier risk management: identifying which suppliers are fragile before they fail you.

Inventory positioning: deciding how much of what to hold where, across a network that doesn’t sit still.

Procurement intelligence: understanding pricing patterns, lead time trends, and supplier behaviour across hundreds of relationships simultaneously.

These are decisions that get made every week in every company that makes or moves physical goods. They are made by people working with incomplete information, under time pressure, using tools that were not designed for the complexity of the problem. And they are decisions where being systematically better — not perfect, just better — compounds into enormous financial advantage over time.

Yet at a conference dedicated to AI’s transformation of business, they were absent. AI should be solving the industry’s problem right now.

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What I did hear

Here is what I did hear, to be fair.

P&G’s CIO Seth Cohen spent time on supply chain automation — specifically, unattended manufacturing scaled across nine locations, and molecular discovery work that cut development timelines from years to months. For a company like P&G’s scale, those results are genuinely significant. But they are the results of a decade-long data infrastructure investment that most companies, including most large companies, have not made.

Walmart’s Daniel Danker gave one supply chain example: a remote Canadian store used an internal tool called Code Puppy to combine weather data and ferry schedules for inventory planning. It’s a good story — a frontline associate solving a local operations problem with a tool headquarters gave them. But it’s a point solution, not a framework, and it was mentioned in passing in a session primarily about AI democratization.

That was essentially it. Two examples, mentioned briefly, in sessions about something else. For an industry representing somewhere between $15 and $20 trillion in global economic activity, that’s a remarkably thin presence.

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Why the supply chain is the most AI-ready industry nobody is talking about

What makes this gap surprising is that the supply chain is, on paper, one of the most AI-ready industries in existence.

It runs on data. Sensor data, transaction data, demand signals, supplier data, logistics data, and financial data. The data infrastructure in large supply chains is often more mature than in the marketing and creative functions that dominated the HumanX agenda.

The problems are structured. Unlike brand strategy or creative judgment, supply chain decisions are historically modelled, mathematically defined, and directly measurable. A demand forecast is either accurate or it isn’t. A stockout either happened or it didn’t. An inventory position either matched demand, or it left cash sitting on a shelf or a gap in a customer’s order. The feedback loops are tight, and the outcomes are concrete.

The stakes are direct. Having spent time inside these decisions, I can tell you that even small improvements in forecast accuracy at a meaningful scale translate into real money — in working capital, in margin, in customer retention. A 2% improvement in forecast accuracy at a company running substantial inventory isn’t a rounding error. It’s a financial event.

So why wasn’t it on stage?

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Three reasons for the gap

The first is audience composition. HumanX skews toward technology leaders, marketing executives, founders, and investors. Supply chain and operations leadership tends not to show up at general AI conferences. They attend industry-specific events — operational forums, ERP user conferences, sector trade shows. The gap in the room creates a gap in the agenda.

The second is vendor invisibility. The companies building AI for demand sensing, supplier risk, and logistics optimization are largely invisible outside their industry. They’re specialized, often enterprise-only, and they sell through procurement channels rather than developer communities. They don’t generate the kind of press coverage that gets sessions programmed at a conference like HumanX.

The third — and this is the one that matters most — is that the enterprise supply chain is genuinely hard to disrupt quickly. The data is siloed across ERP systems, warehouse management platforms, and procurement tools built over decades. Integration alone is a multi-year project. The risk tolerance for AI-driven decisions in the supply chain is low because the consequences of errors are operational and financial. You can course-correct a bad marketing campaign. You cannot easily recover from a production halt caused by a procurement decision that an AI made without the right context.

This is exactly why AI hasn’t disrupted the supply chain at the pace it’s disrupted other functions. And it’s exactly why, when it does, the value creation will be disproportionately large.

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What the conference did tell me

The sessions I attended gave me frameworks that apply directly to this gap, even though none of them were about the supply chain.

Databricks CEO Ali Ghodsi said something that stayed with me: current models are sufficiently capable, but they fail in enterprises because they lack context. The bottleneck isn’t the AI. It’s the organizational and data infrastructure around it. He expects enterprise AI adoption to take five to ten years.

That timeline maps almost exactly to where supply chain AI is in its adoption curve. The context problem is especially acute here. A demand forecasting model trained on historical sales data without context about upcoming promotions, competitor pricing moves, macroeconomic shifts, or supplier lead time changes will produce outputs that experienced planners will correctly distrust — because they know what the model doesn’t know. The data exists. The integration and context layer doesn’t.

The building of a trustworthy agentic AI session reinforced this from a different angle. Panellists from Dataiku made a distinction that matters enormously for supply chain: back-office decision agents — the ones affecting clinical outcomes, credit decisions, or supply choices — require far stronger testing and explainability than personal productivity tools. A demand forecasting agent that informs procurement isn’t a chatbot. It needs to be auditable, explainable, and designed to fail gracefully. Most current AI deployments are not built to that standard.

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What finance figured out that supply chain hasn’t yet

What I found most instructive at HumanX wasn’t what was said about supply chain. It was watching what happened to industries that got forced into AI governance before they were ready — and what that forced them to build.

The finance sessions were the clearest example. Multiple panels addressed AI compliance frameworks in regulated financial services — what one session called the “compliance flywheel.” The argument was that embedding compliance, risk, and governance early in AI product development actually accelerates innovation rather than slowing it. Shared semantic definitions, data lineage, and auditability become infrastructure that compounds over time. Organizations that treat compliance as an early-stage design constraint end up with more durable systems than those that bolt it on later.

The financial crime session added a sharper edge to this. Jonathan Levin of Chainalysis described how generative AI has dramatically lowered the barrier to entry for financial fraud — enabling impersonation, automation, and scale that wasn’t previously possible for lower-skill actors. The response from defenders has been to build proactive threat-hunting systems, intelligence-sharing networks, and AI agents that can process evidence and flag suspicious patterns faster than any human analyst.

I kept thinking about procurement fraud while sitting in those sessions. Supplier impersonation. Fake invoices. Bid manipulation. Ghost vendors. These are supply chain problems that have existed for decades and are about to get significantly harder to detect as AI makes fraudulent activity more convincing and more automated. The financial services industry is building defensive infrastructure right now because regulation and litigation forced the conversation. Supply chain hasn’t been forced there yet. But the same pressures — fraud escalation, operational failure, regulatory scrutiny, and eventually litigation — are coming.

The compliance frameworks being built in regulated finance are the template for what supply chain AI governance will eventually need to look like. The difference is in the timeline. Finance is building it now under pressure. Supply chain will build it later, under more pressure, starting from further behind.

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The investment angle

The publicly traded companies most exposed to the supply chain AI wave are not the model providers. They are the enterprise software platforms that own the data: the ERPs, the warehouse management systems, the supply chain visibility platforms, the procurement analytics tools. Every one of those companies is currently navigating the same question: do they build AI natively into their platforms, partner with AI providers, or get disrupted from below by AI-native startups that don’t carry decades of integration debt?

That question is not answered. The window where it remains unanswered is the window where the investment opportunity is most interesting — both in the incumbents navigating the transition and in the new entrants who might make the integration question irrelevant.

I track this closely because I sit at the intersection of it. I understand the operational problem from having lived inside it. I understand the AI capability layer from building tools on top of it. Those two lenses together are what make the gap visible.

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I came back from HumanX with a lot of material about where AI is moving and what serious operators and investors think. Most of it confirmed what I already believed about infrastructure, reliability, and the gap between demo performance and production reality.

The most valuable thing I came back with was silence.

Nobody talked about the supply chain. Not because it isn’t ready. Not because the problem isn’t large enough. Because the people who understand the problem and the people building the tools are not yet in the same room.

Finance got there first because it was forced. Supply chain will get there when it is forced to.

The question worth asking now — before the forcing event — is which companies are building the governance, the data infrastructure, and the AI capability to be ready when that moment arrives. Because the ones who are will look obvious in hindsight. They always do.

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Jinlu Wang has a background in supply chain, ERP implementation, and enterprise operations. She now builds automated trading systems and web applications and covers AI infrastructure, fintech, and enterprise technology for Trade with Harp, a paid investment research and trading community.

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Many job seekers think cover letters are outdated, but recruiters disagree

By Nick Kossovan

Nowadays, landing a job requires doing what others don’t. That’s why the ongoing debate about whether to include a cover letter with an application is perplexing.

As a job seeker, you should want to do everything possible to differentiate yourself from the competition. The common argument against writing a cover letter is that recruiters and hiring managers won’t read it, leading many job seekers to believe it’s not worth the effort.

Nobody knows exactly what percentage of recruiters and hiring managers read cover letters or how much they influence hiring decisions. Most insights on this topic are anecdotal or based on limited survey data.

Some job seekers look for the easiest route—putting in minimal effort. But nothing worth having comes easy. Given today’s competitive job market, it’s essential to increase your chances wherever possible. A cover letter provides a competitive advantage by making it easier for hiring managers to connect your qualifications and experience to the role.

Not including a cover letter is a missed opportunity to:

1. Pitch why you’re the right candidate for the job.
2. Show that you’ve taken the time to understand the role and its requirements. Many job seekers apply indiscriminately, so those who demonstrate they’ve read and understood the posting stand out.
3. Provide additional evidence to support your candidacy.
4. Demonstrate your writing skills.

This isn’t a debate. The level of effort you put in reflects how much you want the job. Sending a cover letter—or a thank-you email (another topic for another day)—demonstrates your seriousness and strengthens your application. No hiring manager rejects a qualified candidate for including a cover letter. However, some hiring managers consider omitting one unprofessional.

Not including a cover letter is lazy. And hiring managers don’t hire lazy.

Recruiters and employers favour applicants with cover letters, if only because it shows passion and investment in the role.

A common question is whether to include your cover letter as an attachment or in the body of an email. I recommend the latter, as it makes an immediate impression when the email is opened. Keep it short (75 to 150 words) and concise. This highlights two essential career skills: written communication and the ability to articulate the tangible value you bring to a company.

Your cover letter has one job: get the recipient to read your resume. With attention spans shrinking, getting to the point is critical. Writing with brevity will serve you well throughout your career. Keep it short and simple.

[Date]
[Recipient’s first name],

I’m writing to apply for the IT Project Manager position advertised on LinkedIn. Having led Global X’s development team (12 IT professionals) for seven years, overseeing key projects from conception to delivery, I am an ideal candidate for this role.

Some of my career highlights:

• Directed a $5.8-million digital transformation project for an air transport company, reducing lost/damaged cargo incidents from five percent to 0.8 per cent.
• Reduced costs for a clothing manufacturer by $2 million by conducting a gap analysis of its supply chain process.
• Implemented a cloud migration strategy for over 200 legacy applications, achieving zero downtime and cutting operational costs by 35 percent.

Call me at (XXX) XXX-XXX to schedule a mutually convenient time to discuss how I can contribute to [Company].

Sincerely,
Name
Attached: Resume

That’s it. No long-winded claims about being a “team player,” “detail-oriented,” or a “fast learner.” Employers hire based on results, not self-assessments. The key is to provide three achievements not found in your resume or LinkedIn profile.

Ask yourself: What results did I achieve in past jobs, projects, or tasks?

If writing a concise cover letter that could improve your chances of landing an interview seems like too much effort, you might need to question how badly you want the job.

Nick Kossovan, a well-seasoned veteran of the corporate landscape, offers advice on searching for a job.

© Troy Media

The views, opinions and positions expressed by all Troy Media columnists and contributors are the author’s alone. They do not inherently or expressly reflect the views, opinions and/or positions of Troy Media, TV Punjab and Ubiq Broadcasting Corp.

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There are a lot of tourist places here with natural beauty to bright beauty, which attracts a large number of people every year to see and visit.

Canada is an attractive tourist destination, where many major and beautiful tourist destinations are seen. So let’s know in detail about some of the best tourist places present in Canada:

National Park of Canada

Canada’s National Park is famous all over the world for its beauty. This national park in Canada is surrounded by beautiful hills. This place is so beautiful that it is considered no less than heaven on this earth. It has also been declared a World Heritage Site by UNESCO.

Niagara Falls

Niagara Falls is one of the most beautiful tourist destinations present in Canada. It’s a kind of waterfall. Here the waterfalls of water falling from the high hills enhance the beauty of this place even more and that place is also known as the “Honeymoon Capital of the World”.

There is also a very good arrangement for food and drink at this place.

Capilano Suspension Bridge

This bridge is one of the most beautiful place present in Canada. This bridge was built in 1889 and it was built over the Capilano River in Canada. The length of this bridge is about 137 meters. The more beautiful this bridge is, the more scary it looks. Therefore, people who are afraid of heights should not go here. This bridge was also shown in many TV shows and films.

Lucy Lake

Lucy Lake is also one of the famous and major tourist destinations present in Canada. It is a very beautiful lake. The water of this lake is blue in color, which looks very clean and beautiful.

This lake is present in Banff National Park located in Canada. The length of this lake is 2.5 kilometers and the depth is 90 meters. Skating is also done on this lake during the winter season.

Toronto

The Toronto is Canada’s most populous city. This city is known as the mutual culture of Canada. It has very beautiful and attractive lakes.

Apart from this, Toronto is also quite famous for its culture and tradition. Theater, music, museums, gigantic buildings, delicious food, etc. are also attract tourists.

Victoria

One of the very beautiful tourist destinations present in Canada is the city of Victoria. This place is famous all over the world due to the fragrant climate, natural beauty, things of ancient times and history. Due to the history and ancient things here, this place is a favorite place for tourists in Canada.

Quakers

Quakers City is a major city in North America, helping to retain old European charms. Queeback City is one of the places to see in Canada. The things like culture, art, natural beauty, religion, luxury, adventure activities, architecture, Montmorency waterfalls, religious places, etc. are seen. That’s why if you have gone to visit Canada, then you must visit Quake City.

Montreal

The city of Montreal is one of the second largest cities in Canada. It is one of the most beautiful places in Canada. The city is known for its culture and history. Architecture, festivities, old-fashioned classical architecture, modern skyline, night lifestyle, fusion restaurant, market, amazing museum and delicious food are also found in this place. The city of Montreal is also known as the metropolis of Canada.

Vancouver

Vancouver is one of the very beautiful and famous tourist destinations present in Canada. Vancouver is famous all over the world for its natural and attractive beauty and diversity. The city is surrounded by mountains and large buildings. The beauty and attractive views of the big buildings and mountains present here help to make this place even more beautiful. Music is also organized here, which makes the nightlife there even more entertaining.

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Last month, 153,000 people joined the labour force and the unemployment rate remained steady at 5 per cent. Earlier, unemployment had registered a steep decline of 4.9 per cent. There has been an increase in employment in almost every sector and the biggest benefit of this has been wholesale and retail.

During this period, an increase of 4.5 percent was recorded in allowances on a year-on-year basis.

Experts say that the increase in interest rates will slow down the pace of the economy, but the way the situation is visible, the situation is not so bad.

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With this, these interest rates will reach 4.5 per cent and it will be the highest since 2007.

The Bank of Canada has raised interest rates seven times since March to curb inflation that has crossed all limits. Economists believe that these interest rates will not be increased further by the bank after the increase to be made today.

How these interest rate hikes will affect Canadians’ spending habits will also be known over time. But the Bank of Canada is hoping that the economy will get back on track in the coming months and the situation will also be closely monitored by the bank.

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LeBlanc said strengthening the health care system by compromising with provinces is a key priority for the Liberal government. LeBlanc also confirmed that the federal government is willing to sign an agreement to increase the funding to be given to provinces, which canada health transfer (CHT), as well as to put federal funds in key priority areas over the coming ten years.

In an interview during the ongoing Liberal Cabinet Retreat in Hamilton, Ontario, LeBlanc said he did not expect a deal in the coming two weeks, but hoped to reach an agreement that would benefit Canadians before the federal budget.

He said the issue is also likely to be discussed in more detail with the provinces in the coming weeks. LeBlanc said he thinks the premiers are right if they think the five-year deal isn’t enough to address some of the key health care challenges. He attributed the growing population of Canada to the ageing population for this step.

Ensuring access to family health teams, reducing the surgical backlog and improving staffing are some of the key things that the federal government is looking at at right now, he said. He said this was also the main topic of discussion in the Federal Liberal Cabinet Retreat.

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Health care professionals must currently be registered with one of ontario’s health regulatory colleges to start working in the province. But Ontario is willing to relax these rules to eliminate a variety of bureaucratic delays.

The Ford government said changes to the law will be made in February.

Making the announcement in Windsor, Ontario on Thursday, Ford said that for nurses, doctors and health care workers working in any province and territory across Canada, he wants to say that if they are thinking about making Ontario their new home, there is now a golden opportunity to do so.

Meanwhile, in a news release released on Thursday evening, the Canadian Medical Association welcomed Ford’s approach and said it was the best step towards improving health care.

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The youth was on his way home earlier this month when the accident occurred on Fraser Highway in Langley, British Columbia. Victim has been identified as Taren Lal.

The accident on January 7 was so horrific that the fence was uprooted and a tree fell. Lal’s mother Sarbjeet Kaur said she had spoken to her son shortly before the accident.

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It is said that he felt chest pain and was admitted to local hospital but could not be saved.

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SQ spokesman Alois Cossette said police could not reach the scene during the day due to safety reasons. The police launched an investigation when a team of 50 firefighters brought the fire under control.

Cossette said the investigation is likely to go on overnight but there could be some difficulty due to heavy snowfall.

During the day the SQ did not say that one person was missing. But the number of missing persons reached three on Thursday evening.

According to the information received, this number may also increase. The explosion occurred at 11:17 a.m. Thursday at a propane facility in St-Roch-de-l’Achigan, 50 kilometers north of Montreal.

It was also informed that the buildings within a radius of one km of the building were evacuated at the earliest.

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