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Content, Engagement and the Chaos In Between: A Case for Supply Chain Management as a Strategy to Manufacture Opportunity

Content, Engagement and the Chaos In Between: A Case for Supply Chain Management as a Strategy to Manufacture Opportunity

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What if you could manufacture opportunity?

This is not to ask what if you could manufacture campaigns. Instead, this is to ask if we could think about manufacturing opportunity the same way we might think about manufacturing a jar of pickles? Simply: Yes. If the ask is to manufacture opportunity for the purpose of profitable growth, a supply chain dedicated to the creation and optimization of content that acts as the fuel for engagement can and should be established.

Today, the complex web of functions, talent, tools and processes attached to content creation has left organizations with nothing less than chaos. This web is a bottleneck that brings our system to manufacture opportunity to a screeching halt.

What is Supply Chain Management, and How Does it Apply to Content?

Let’s talk about pickles. To meet market demand, you sell 100 jars each month so, you order enough cucumbers to make 100 jars for the month of June. But the lid manufacturer can only supply 90 lids for June. Without visibility and automation in the supply chain, you are still ordering ingredients, making pickles, and filling 100 jars, wasting the 10 with no lids. Making this worse, you already took orders for all 100 jars and now 10 customers are out of luck. No pickles for them.

A supply chain strategy is a plan to manage how goods and services are made and delivered. This encompasses everything from raw materials to final delivery, detailing the people, platforms and processes that are required. Supply Chain Management (SCM) typically breaks down into coordination, planning, execution, delivery and monitoring. These phases commonly involve key actions in sourcing, procurement, logistics management, quality assurance and delivery as SCM technologies and workflows aim to optimize efficiencies across the entire chain measuring efficiency, productivity and opportunity for continuous improvement.

Demand signals, past performance, supplier availability of each component to the finished delivered product all play a part in getting your 100 bottles into the hands of your loyal customers. SCM intentionally predicts, orchestrates and optimizes each stage of production, execution and delivery to ensure that every element is rightsized and in lock step with all other stages.

Now: close your eyes and imagine that instead of pickles, we were talking about content. Instead of talking about profit from selling pickles, we were talking about opportunity that is driven from a customer’s journey and their engagement with content.

What Should a Content Supply Chain Really Address?

The purpose of a content supply chain is NOT to make more content faster. It is to generate the right content for the right audience or customer in the right moment. In the earlier example of our pickle business, the end goal of an optimally managed supply chain is not to make more pickles faster. Instead, it works to get the right jar of pickles into the right customer hands to meet an immediate craving and need in the right moment. Miss that window, you risk missing that sale.

Through this same lens, if we are working to make content, there needs to be an end business goal that answers the question of WHY are we doing this to begin with. We produce content so that we can manufacture opportunity and growth thanks to engagement.

If we only focus on automating the process of content operations, we only get more of the same content, produced faster. When we add Artificial Intelligence (specifically generative AI) to the mix, we get more versions of content, produced faster. However, if we focus on how we use content to manufacture opportunity, our processes, our outcomes and the technology we leverage is set to a different purpose. Instead of churning assets quickly, our gaze turns to prediction, context and timing of content production and delivery orchestration.

This is not to say that there is not intense pressure on marketing teams to create more forms of more content. This pressure has pushed the boundaries of the content development team to deputize functions and personas well outside the formal engagement center of marketing, conscripting them into this content development and deployment team.

These informal engagement hubs have often turned to rogue, self-selected tools that may produce content or assets quickly, but fail to connect to centralized common brand assets or templates. These teams are asked to be part of the machine that is manufacturing opportunity, but they are not included in the tools or platforms that could allow for brand secure creation.

The content supply chain requires specific shared services that include common data, common asset repositories and visibility across what exists in the creative world of possible. With guidelines and guardrails set across the organization, the supply chain allows for creative centralization that is built from collaborative briefs and a collective understanding of the brand and the customer.

Visibility, accountability and analytics are supported by truly digitally connected systems and tools. The data and analytics that flow across this content supply chain should not just curate and elevate insights and recommendations for optimized actions. An AI-powered content supply chain should have the capacity to reason and take action based on known factors from brand standards, allowable use guidelines, market conditions, and even current events. Predictability and scalability across the supply chain is not thanks to the speed of automating creativity, but rather thanks to the speed of identifying outliers and predicting needs to perfectly pace production, resources and collaboration. The beauty of a supply chain strategy is in the dichotomy of it all: a content supply chain should be structured to empower flexibility.

Just like with pickles, tastes for content will change. The content supply chain will allow for change. Spicy pickles could be all the rage today but fall out of favor tomorrow as sweet pickles take over. Similarly, image personalization are table steaks in today’s engagement strategies, but just like that video could be the only game in town.

Is your organization ready to manufacture opportunity?

If the answer is yes, start by mapping the stages and phases that touch. Work backwards to identify where along that process content can and should be created, individualized and contextualized to deliver a more personalized engagement with the customer. Identify where signal, telemetry and data can be curated to fuel predictive outcomes. Then, do this exercise all over again, but bring in different entry points for the customer’s journey that extend across the organization. Sales, service, finance, commerce, in-store, and even events should be thought of as part of this process to manufacture opportunity…and in turn should be accounted for across the content supply chain.

The content supply chain will continue to evolve as a strategy just like the technology stack will evolve as technologies evolve. While there won’t be a single “content supply chain stack” there will be core tools and core platforms that will connect into a larger engagement and opportunity ecosystem. Think of solutions like Adobe GenStudio and Adobe Express with all of their generative AI and content creation power connecting into larger CRM and Service solutions like ServiceNow or Cloud platforms like AWS or Microsoft to bring all that interaction and engagement together with the customer and with all those content attributes into a trusted single truth.

The content supply chain will integrate and connect far beyond Marketing and creative teams. It will account for and deliver right-sized tools across the organization and connect everyone into this overarching supply chain that manufactures durable, profitable growth, even for a pickle manufacturer that only sells 100 jars a month.

 

Image is AI generated using Adobe Firefly. No pandas were asked to pack pickles for this image.

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9 Google Cloud customers on AI implementations, best practices

9 Google Cloud customers on AI implementations, best practices

There was no shortage of customers at Google Cloud Next 2025 as the company is hitting its enterprise stride with a focus on industries, use cases and integrating AI agents.

Here’s a look at customers at Google Cloud Next their takeaways and best practices picked up along the way.

Google Cloud Next coverage:

Volkswagen

Volkswagen's Steve Lancaster, Director of Connected Car Development, Operations and Connected AI for the America region, is responsible for apps, IoT platform and AI efforts. Volkswagen has been a Google Cloud customer for about 16 months.

Lancaster said the data streaming from cars and apps is valuable feedback. The problem was Volkswagen didn't have the visibility into the content and data to improve customer experiences.

"There's a lot of feedback and direct contact with the customer through this application, and we pay a lot of attention to what they're saying," said Lancaster. The requirements for Volkswagen revolved around search across multiple data types and documents and models that can learn and answer customer questions.

"We're using Gemini to really understand customer intent and Vertex AI to really break data down into its proper form and return answers. We know the most about these vehicles and we need to make that information readily available," explained Lancaster.

Best practices:

  • Follow the playbook and Google Cloud roadmap. Lancaster said it helps to learn the basics blocking and tackling on the platform to improve data and AI core competencies.
  • Focus on business results. Lancaster said he wouldn't talk about ROI savings, but said there are fewer calls to customer service. The feedback from the app provides signals as does sentiment on social media. "Customer satisfaction is measurable and tangible, and we're watching them closely. We get all kinds of feedback from the app, from the thumbs up, thumbs down to very specific comments about how it's interacting, or what they're thinking," said Lancaster. "At the end of the day we're selling cars. If we can impact the owner experience and brand loyalty that's a big measure."
  • Know your own user stories before you engage. You know your business better than anyone else and have the data before engaging.
  • Clean up your data so it's AI ready ahead of the AI project.
  • Beware of the "laptop AI guy" who shows what was possible with a model. Lancaster said AI is an IT project that needs security, compliance and everything you need in the enterprise.

Best Buy

Ashley Daniels, Vice President of Product Management at Best Buy, said the retailer is in a broader customer care transformation and moving beyond stitching together experiences beyond the call center.

Daniels said genAI is enabling agents to lead with empathy since AI can take the notes. "We're now in the IVR and chat bot transformation," said Daniels, who said Best Buy has been transforming its customer engagement and experience for the last 18 months.

Best practices:

  • If you have contact center use cases, there are plenty of ROI measures. Daniels said the previous IVR system would route to an agent when a customer tried to cancel a membership. The problem was an inconsistent experience. Now Best buy can pull data on savings and coverage for devices and increase the save rate. "The day it went live the contain rate went form 9% to 50%," she said.
  • Continually optimize. Daniels said Best Buy continuously changes the system to leverage AI and tweak the experience.
  • Create human experiences using AI. Don't lose track of the human element.
  • Best Buy using AI agents going forward will improve customer satisfaction and drive revenue, said Daniels.

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Geotab

Neil Cawse, CEO of Geotab, has a business that manages vehicle fleets. Geotab leverages Gemini in its products and in analytics as well as Workspace.

Cawse said Geotab is focused on data residency and where it resides. "We can't tolerate data being in the wrong places," said Cawse.

Best practices:

  • Focus on adoption. Cawse said the company is following the usage data. By tracking usage, Cawse was able to discover that HR didn't use Gemini nearly as much as marketing.
  • Notch small wins. Geotab uses town halls to highlight examples of good AI usage internally.
  • Find new ways of working. Processes and workflows change with LLMs.
  • "We have the opportunity to leverage AI to take away the laborious work and enable AI agents to automate tasks," he said. "Humans will still run the show, but you'll have agents running behind the scenes doing the work."

Gamuda Berhad

John Lim Ji Xiong, Chief Digital Officer at Gamuda Berhad, said his Malaysia-based construction firm is focusing on AI via Google Cloud and "trying to insert technology for all of our property and construction business."

Xiong said Gamuda Berhad has 164 agents in production. He said:

"We're at 164 across all sorts of different areas, whether it's contract analysis, whether it's looking at the design documents, whether it's looking at tender documents, being able to speed up our time to put in proposals, being able to also then get information across the organization, whether it's Australia to Malaysia. We're seeing a 20% increase in our funnel."

"These are customers that would not have necessarily come through our sales gallery, but they are interacting with us already."

Best practices:

  • Transform your industry. Gamuda Berhad is now starting a tech services unit to branch out from construction.
  • Gamuda Berhad has just completed the second phase of its transformation by consolidating data in BigQuery. Now the plan is to leverage data to "create a crystal ball for construction," said Xiong.
  • Upskill. Xiong said his company has created an AI academy to get people from all walks of life to learn AI. "We use a full-stack syllabus," he said. "We can get people up to speed in three months." The company has graduated two classes of 50 and is expanding the class to the rest of the country.
  • Build a talent base. Xiong said his company is hiring some of those graduates.

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WPP

Stephan Pretorius, CTO WPP, said the advertising firm started out with Google Cloud focused on workflows and when genAI landed the company started using it for content and ideation.

"As the models have become more capable, the latest unlocks for us have been video and we saw an explosion of video usage inside the business," he said. "Going in we saw a new pattern of behavior."

WPP also has 2,800 agents in production for contract review, CRM automation and other use cases.

Best practices:

  • Pretorius said enterprises need to focus on adoption, change processes and the way they think. "Adoption is a huge metric. We track, time spent and tasks," he said.
  • AI can drive new ideas quickly and you pretest concepts for media campaigns as well as returns for customers. But unit economics are a challenge. WPP used to charge for creative by new sizes and markets. Now that revenue stream is gone.
  • "For most of our clients, AI is enabling them to keep their spend and get same reward or spend more to get more," he said.
  • Embrace the business changes and adapt. WPP's business model has changed dramatically.
  • Focus on ecosystems. Pretorius said it makes sense to pick vendors that work across systems and data stores. Open ecosystems matter so you don't get boxed in architecturally.

Palo Alto Networks

Palo Alto Networks' Rajesh Bhagwat, Vice President of Engineering at Palo Alto Networks, is responsible for data platform and AI. Palo Alto Networks has been a Google Cloud customer since 2020.

He said Palo Alto Networks cloud transformation aligned with the overall product strategy, which now revolves around its Cortex platform today. Bhagwat said Google Cloud was able to offer real time data analysis via BigQuery.

Best practices:

  • Focus on tangible business returns. Bhagwat said uptime and customer satisfaction. Palo Alto Networks was able to reduce its mean time to resolution for customer issues. "When a case comes in, the support personnel is using AI assist for a quick respond back. The AI assistant knows the customer context, knows all the data is using the underlying models bring out all the reasoning, all the analysis," he said.
  • Know whether you should build or buy. Bhagwat said enterprises need to know their core competencies. It was clear that Palo Alto Networks wouldn't train its own models and run massive data centers at scale.
  • Prepare for scale. Bhagwat said scale matters. Plan ahead for it. Palo Alto Networks manages 5.5 petabytes of data each day.
  • Address skill gaps via learning but balance between broad and deep knowledge of AI. Look at new hires through the lens of AI readiness.

AI21

Pankaj Dugar, Senior Vice President and GM at AI21 Labs, said the company which develops models is using Google Cloud for infrastructure.

"As a company we do two main things. We build highly optimized foundation models. And we recently launched an AI planning and orchestration system," said Dugar.

Best practices:

  • Leverage hackathons to find talent and upskill as new technologies emerge. Dugar said AI21 connects his engineers to Google Cloud experts to learn how to use new features and services.
  • Be an early adopter. By adopting Google Cloud services early, AI21 creates a feedback loop to improve products on both sides.

Citigroup

Citigroup CTO David Griffiths said the banking giant is leveraging Google Cloud as part of its multi-year transformation. "Anywhere we work with digital transformation, AI can help. We are embracing AI as a universal enabler," said Griffiths.

Griffiths outlined the strategy and best practices:

  • "We've been guided by a couple of simple principles, with taking a very deliberate approach. We build a simple, scalable, secure, multi-model platform that has centralized controls and observability, so we can keep everyone safe, and we can learn and observe across the breadth of all of our AI interactions."
  • "We think about the impact of AI in two dimensions: General, horizontal, assistive AI tools that have very wide applicability. These may only give you 1% to 3% of productivity back, but you scale that across the company, this really adds up. And you have to complement that with deeper AI verticals, specialized capabilities for the specialists within your workforce."
  • "A scale footprint allows us to maximize the impact as this technology advances. Google is at the frontier of AI development, and we want to have a mini lag between AI innovation and AI impact."

Griffiths added that Citigroup had about 1,000 use cases in 2024 at various stages. Those use cases were horizontal and could benefit the entire organization. In 2025, Citigroup is focused on scale and depth and "industrializing our AI verticals" for customer servicing, fraud detection, finance and sales and marketing.

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Citigroup’s journey

Pearson

Pearson CTO Dave Treat said the education and learning company is transforming to "use AI to help educators and students transform learning across all stages of life." And the company is betting on agentic AI.

"We've realized it's time for us to think outside the book, and we're really just at the beginning now of creating super effective, personalized learning experiences using agents," said Treat. "We're envisioning a team of agents working together on behalf of educators and students using natural language interfaces integrating all of the tools and resources that they need guided and shaped by our learning science and trusted content."

Treat noted that agentic AI will change its engineering and software development lifecycle. Pearson is using specialized agents for code, documentations, and testing scripts.

According to Treat, Agentspace will be the control plane for multiple agents including ones from Salesforce and ServiceNow. Treat's take highlights how hyperscalers may be best suited to orchestrate AI agents across systems. "Just like humans, there's going to be the right agent for the right job," said Treat.

 

 

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Canva launches Visual Suite 2.0, adds Canva Sheets, Canva Code

Canva launches Visual Suite 2.0, adds Canva Sheets, Canva Code

Canva launched its Visual Suite 2.0 in what it calls the biggest product launch since it was founded in 2012. Canva aims to make spreadsheets visual with Canva Sheets and create interactive designs with simple prompts with Canva Code.

The company announced Visual Suite 2.0 at its Canva Create event in Los Angeles. Canva's Visual Suite overhaul is designed to build on its 230 million monthly active users, cater to designers as well as attract new users.

Here's what Visual Suite 2.0 includes:

  • One user interface and design. Whether it's a document, spreadsheet or photo editing, Canva's design carries over in Visual Suite 2.0. The unified design is a bid to consolidate separate tools for design.
  • Canva Sheets is an effort to reimagine the spreadsheet. Sheets is built on Canva's Magic Studio and features Magic Insights, which scans data for patterns and takeaways. Canva also added connectors to import data from HubSpot, Statistica and Google Analytics.

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  • Magic Charts takes data and makes it interactive with visuals ranging from visualizations to infographics.
  • Magic Studio at Scale has bulk content creation with personalization.
  • Canva AI generates and edits designs, text and images.
  • Canva Code creates designs from natural language prompts and applies to presentations, landing pages, classroom resources and interactive designs.
  • Photo editing is also available as a standalone or integrated tool.

Melanie Perkins, CEO of Canva, said the goal with Visual Studio 2.0 was to meld creative and productivity workflows with one design.

Canva continues to gain traction with a model that offers free services and upsells to prosumers as well as enterprises. Canva has more than $3 billion in annual revenue, up 30%. Canva counts T-Mobile, Salesforce, FedEx and DocuSign as customers.

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ShortList Spotlight: Kodem Security

ShortList Spotlight: Kodem Security

There's a seismic shift happening in how we develop and secure #software. 🔐 Developers are no longer just writing code - they're navigating a complex ecosystem of #AI-generated #applications. 

Modern apps are interconnected with multiple systems and rely on numerous open-source packages. In these dynamic, unpredictable environments, attackers hunt for interaction vulnerabilities - a single weak link that can compromise an entire system. 🔎 

Enter Kodem Security 🔒 Kodem was named to the Constellation ShortList as a solution leader in Application Security Testing. Constellation analyst Chirag Mehta explains Kodem's approach to...

🔍 Scan code statically and dynamically
🔬 Track runtime vulnerabilities
🌐 Understand complex application interactions

Watch this ShortList Spotlight to learn more about Kodem 👇 and start reimaging application #security in the AI era. 

View the full Constellation ShortList here: https://lnkd.in/gh6wqXqv

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Google Cloud CEO Kurian on agentic AI, DeepSeek, solving big problems

Google Cloud CEO Kurian on agentic AI, DeepSeek, solving big problems

Google Cloud CEO Thomas Kurian said AI agent interoperability is critical to link process and workflows, outlined the sovereign AI strategy and said the company is building an agentic AI ecosystem.

Speaking at an ask me anything session with analysts, Kurian said the following at the end of day 1 at Google Cloud Next.

Scaling the organization while looking around corners. "Our strength as an organization has already come from one understanding deeply what customer problems we're trying to solve," said Kurian. "We are very careful, given the number of problems out there, to focus on a very specific set of problems. We focus on a few important ones."

Building the go-to-market ground game. Historically, Google has been a company that has had great technology, but difficult to buy and deploy. That vibe has changed as Google Cloud has built an ecosystem. Kurian said Google Cloud can appeal to multiple CxOs. Kurian said: "If you look at AI, much of the buy decision is not in IT. It's outside it. How you engage not just in IT, but outside, and how you can build a solution portfolio that our team will sort of take market is constantly evolving."

Process flow and accuracy. Kurian was asked about the success of agents given hallucinations and how enterprises may be reluctant to hand off processes to AI. "We've taught people to decompose. Take a process and break it into a set of sub steps. We need people to go through each step and not design one agent. You need to have that ability to view each step and prepare the accuracy of that step," said Kurian. "Enterprises want predictability."

Starting with agentic AI. Kurian said companies just starting out need to start with the business problem to solve and measure the result. Data quality feeding that AI agent is also critical. There are also industry considerations. Workflows and security are also issues. Kurian said: "There's a lot of specific guidance whenever we talk to customers. The best proof point is always look at others in the industry and what they may have done so that you can actually face the timescale you think you need to change the organizational change management."

Why Kurian seems happier? First, Chappell Roan called him back (even if she isn't performing at Next). That call was noted during the keynote. "I've always been proud of our team. Many people thought we would not succeed in it. Many people thought that we would never be successful outside of the consumer domain. I'm proud of the team and the resilience we've shown."

Solving big showcase problems. Kurian said "The Wizard of Oz" project is an example of how Google Cloud likes to participate in big projects, but let the customers tell the story. "I don't think people realize Dorothy shoes only show up in eight frames so tuning a model takes a lot of work to improve the model. We chose to work with that because if you can do this here, you can do pretty much anything else," said Kurian. Google Cloud is also working on similar tough projects with hedge funds, healthcare, life sciences and manufacturing. "We like to change the way those industries work," he said.

DeepSeek. Kurian: "The industry tends to rotate every few months because a bunch of articles that were written that may not be accurate. They've done some really good things. If you look at the cost of our models, I think people misinterpret why we didn't say anything. We're very confident that the actual training cost for a model from us is comparative to anybody out there."

"We always say there's a lot of debate on these topics. And if you actually sit down with the DeepMind guys who are working on it, I think you see the reality of some of the numbers. That's why, when we don't say something, it's not that we are surprised and it's obvious what they did. Credit to them for doing it."

 

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Walmart's tech bets improve resilience as it reaffirms Q1 outlook

Walmart's tech bets improve resilience as it reaffirms Q1 outlook

Walmart reaffirmed its first quarter sales growth outlook; said its e-commerce business is on track to be profitable and highlighted technology initiatives that have made the company more resilient in a volatile economy.

At Walmart's investor meeting, Walmart CEO Doug McMillon said the company's investments in customer experience, technologies and a business model that has added higher margins have enabled the company to push its advantages. "While in the short term we are not immune to some of the effects businesses face in today’s operating environment, we are uniquely positioned to play offense," said McMillon.

Walmart said it expects its first quarter sales to be in line with its 3% to 4% outlook. In a presentation to analysts, Walmart executives noted that Walmart is technology powered and is leveraging AI on multiple fronts to be more efficient.

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Some examples of how technology investments have paid off include:

  • Walmart has integrated physical and digital experiences to gain share and reach consumers via multiple channels.
  • Digital businesses such as Walmart's advertising and data units have improved margins.

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  • CFO John David Rainey said the company has leveraged data and automation in its supply chain to manage inventory better and cut costs. In the US, half of e-commerce fulfillment center volume flows through next-gen fulfillment centers and more than half of stores receive freight from automation.
  • Digital commerce best practices from the US are being deployed internationally and vice versa.
  • Sam’s Club U.S. President and CEO Chris Nicholas said 40% of total transactions are digital including scan & go and online sales.

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An (Alternative) Method to the Tariff Madness

An (Alternative) Method to the Tariff Madness

The new tariff regime instituted by the United States over the past couple of weeks is creating uncertainty in the executive world.  Questions on how it affects an industry abound and answers are not plentiful.

Two camps have clearly emerged, with little room in between for indecision: pro tariffs and against.  My friend and colleague Ray Wang wrote a convincing case for the tariffs and the downstream effects they could have for the American economy.  Only time can tell if this method he proposes will play out, and I cannot say with certainty it will not. 

I do have an alternative model, one that continues what has worked well in the past 65+ years.  There are four reasons why continuing the existing model is a better bet:

  1.  Globalization has not failed (including the WTO).  It is not a movement intended to level the playing field for everyone involved, it leverages what every country does best and promotes and rewards those that focus on that. 

No single country in the world can ever do everything great: a rising middle class does not want to sit in a hot, dirty, dangerous coal mine or manufacturing facility to earn the same or less they could make in a service job.  They prefer to code a social-media app while sipping matcha smoothies and playing foosball. 

This is an intrinsic part of the evolution of the United States in the past few decades.  Trade imbalances are not only acceptable but also expected for the American economy.  Countries that can manufacture better, cheaper, faster than us, should – and we will buy their output because our time is better spent creating better profits via service solutions.

  1. The United States is no longer a manufacturing economy. One hundred 100 years ago, manufacturing was the key to the American power in the world.  Starting in the early 1900s, our innovations in manufacturing led the world to better and more precise tools and processes, ending with the hegemony we displayed during WW2 with almost impossible production of war machines and supplies.  Post WW2, manufacturing led the growth of the American economy and the creation of a middle class that remains to this day a model for developing countries to follow. 

In the 1980s, this middle class shifted towards consumerism, at the same time the manufacturing class was beginning to decay in quality and abilities.  New manufacturing prowess emerged (mostly) in Asian countries, with Taiwan and Japan leading the way.  We conceded that role of leading the world in manufacturing to shift towards a service economy that produces more than 100 times more revenue, and 50 times better margins.

This is our new reality, and the reason we are seen as the leader in technology, software, services, and even distribution of American non-durable goods and services around the world.  Our economic power today is predicated in outsourcing the low-level, thin-margins, exhausting and un-interesting jobs to places better suited for them (developing countries that are where we were 100 years ago) in exchange for a focus in better jobs with better revenues. 

  1. Consumer sentiment is the basis for the new economy.  While I admit to a nostalgic bend in my life, it is not the foregone days when we could manufacture cars and refrigerators better than anyone. The interesting part of the economic shift over the last 40-50 years is not unique to the United States.  Consumerism is a global trend, and one that newly emerging middle classes in places like Nigeria, India, China, and other up-and-coming countries quickly embraced as they grown.  I am very happy, as are most Americans, with the ability to buy a new, bigger, better TV for a low-price every few years.  This shift to consumerism must be fueled by cheaper manufacturing.

Any economist will tell you that consumer sentiment, and the accompanying spend into a consumerist model, is what powers economies today. A service economy produces better margins, more disposable income, and better living conditions that in turn require an environment where those gains can be spent forwards a better life.  That Is the power of the middle class.

  1. History has proven tariffs to not work.  The US tariff regime of the 1930s, implemented to help pull the us out of the grand depression, took almost 80 years to fully unravel.  It also did not have the intended effect: while it worked for a while, long-term growth was driven by a service economy and outsourcing of manufacturing, not the return of manufacturing to replace imports.

Economic models shifted dramatically in a world where globalization drives economic growth, and people out of poverty, around the world.   The pandemic left us without a “normal” model of operation, but a return to nostalgic models that were proven wrong before is not the answer.  The trade imbalance in durable goods manufacturing is replaced by a trade surplus in services that is magnitudes bigger than any potential increase in revenues from manufacturing.   With better margins.

Trying to recreate the infrastructure we need to become a manufacturing nation again is prohibitive in cost, time, and resources.  We won’t find the workers willing to take the jobs at the prevailing wages.  We won’t find the machinery in time to replace what is intended to being replaced.  We won’t find the know-how. We can do it, but it is not the way.  Not following a model that has proven to not work before.

As I said, only time will tell which model is proven right.  I don’t believe tariffs are the way, nor do most economists and experts. 

Using tariffs to bring trading partners to the negotiating tables? Well, it seems to have worked – but as always, there are better methods grounded in diplomacy and communication over exerting muscle.

No?

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The AI Race, CRM Composability, AI Security, Google Cloud Next | ConstellationTV Episode 102

The AI Race, CRM Composability, AI Security, Google Cloud Next | ConstellationTV Episode 102

ConstellationTV Episode 102 just dropped! 📺 Co-hosts Larry Dignan and Martin Schneider kick off with #tech news, including the AI race 🏎? between Salesforce and ServiceNow and economic uncertainty around #tech spending. 

Next, Martin intros his latest #research on AI and composability in #CRMs, covering the benefits of composable apps and how they enable businesses to make cost-effective decisions and reduce overall costs. 📈 

Constellation analyst Chirag Mehta then unpacks the challenges of securing #AI workloads and understanding AI attack vectors.🔒 His new report on AI security covers mandatory compliance, frameworks for securing AI systems, and the role of marketplaces in supporting AI solutions. 

Larry concludes with his top 5?? takeaways from Google Cloud Next 2025, including Google #Cloud's AI hypercomputer, hashtag#agenticAI developments and horizontal agent integration.

00:00 - Meet the Hosts
00:20 - Enterprise Technology News
09:33 - AI and Composability in CRM
16:43 - AI Security and Marketplace Shifts
21:07 - Google Cloud Next 2025 Highlights

Tune in for a new ConstellationTV episode every two weeks! Get the latest news, research updates, and case study interviews during the fastest 30 minutes in enterprise technology!

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Google Cloud CTO Grannis on the confluence of scale, multimodal AI, agents

Google Cloud CTO Grannis on the confluence of scale, multimodal AI, agents

Google Cloud CTO Will Grannis said multimodal models including ones learning how to smell, AI agents at scale that will break down enterprise silos and continually optimized compute will usher in new experiences.

Grannis, speaking at an analyst summit at Google Cloud Next, said the company is pacing disruption and transformation based on a confluence of technologies cloud, generative and agentic AI, and multimodal model advances with reasoning

"A year ago, customers talked about getting started with generative AI. This year is about scale and agents," said Grannis. "The work we do is an early signal on where technology is headed. We don't do demos, we do engineering proofs. We are seeing a confluence of events."

More from Google Cloud Next:

Google, through Google Cloud, Google DeepMind and Google Labs, is leaving breadcrumbs on where the enterprise is headed through its research. Here's a look at where Google Cloud is placing its bets and why enterprises should pay attention as they cook up new experiences and use cases.

Multimodality. Grannis said models will continue to improve and they are quickly becoming "able to understand and control tings in the physical world." "Enterprises are dreaming up new scenarios and customer experiences that two to four years ago were impossible," said Grannis, who noted Google is focusing on bleeding-edge multimodal models including Veo 2, Lyria and Chirp.

Scale. Grannis said Google Cloud "thinks about scale up front and tries to bake it in at the beginning." By engineering well in the beginning, new use cases for something like AI agents can emerge faster because you won't have to retrofit an infrastructure in case something scales.

"Agents are in early stages right now and starting to be proven out," said Grannis. "MCP works well but AI agents need to scale out with authentication, stability, reliability."

Google Cloud's Agent2Agent protocol is a step in scaling agents. "Interoperability is super important. We're doing what we did with Kubernetes and TensorFlow--drive the industry forward with partners so a single agent can turn into clouds of connected agents," said Grannis.

What does agentic AI at scale look like? "Think of a procurement agent that can go and negotiate contracts for a customer and do it well if game theory is baked in," said Grannis. Today, that procurement agent will require instrumentation and multiple parties. "Bots are actually customers and partners trying to interact," explained Grannis. "Rethink how to create digital experiences."

Optimization. Grannis said Google Cloud over time is continually refining and improving. For instance, Google is on its 7th version of TPUs in eight years. "We're still getting exponential increases every year," he said.

Grannis said his most underrated announcement at Google Cloud Next 2025 is Cloud WAN, which is a service that makes Google's high-speed, low-latency network available to enterprises. "Cloud WAN is a big deal because it allows you to transit over the high performance network of Google and its consistent over geographies. It's one of the hardest optimizations to run," said Grannis, noting that Google Distributed Cloud with Google AI and Nvidia baked in is critical too.

Silo busting. Grannis said the horizontal approach to AI agents is critical for breaking down silos--data and work streams. "Agentspace is profound because it can create agent AI for everyone. Employees are getting a boost in creativity and usage," said Grannis, who noted that there is a lot of creativity that is pent up and can be freed as agents break down corporate silos.

Already, Google Cloud cited multiple Agentspace integrations and customer use cases. Culturally, Grannis said this silo busting may be the biggest disruption.

 

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Lloyds Banking Group bets on Google Cloud for AI-driven transformation

Lloyds Banking Group bets on Google Cloud for AI-driven transformation

Lloyds Banking Group is building its next-generation machine learning platform on Google Cloud's Vertex AI in a move that will replace legacy systems.

Ranil Boteju, Chief Data and Analytics Officer at Lloyds Banking Group, said the company's previous machine learning and data science platform was on-premise and pushing a decade of use. "We realized we needed to modernize and wanted to move to the public cloud and Vertex AI," said Boteju. Lloyds completed the migration a year ago.

Boteju said the bank's hundreds of data scientists are building models at scale. According to Boteju, the move to Vertex AI is "quite a significant step up in capability," but is also enabling Lloyds to "pursue our agentic AI aspirations as well."

More from Google Cloud Next 2025

According to Boteju, Lloyds is leveraging Vertex AI to accomplish the following:

  • "Enable the whole bank with AI," he said.
  • Build the bank's GenAI Workbench on Vertex AI.
  • Use multiple models via Google Cloud's Model Garden including Gemini and open source models based on ability to handle use cases. "We can select the right LLM for the right task," he said.

Ultimately, Boteju said the plan is to leverage agentic AI. "For the last nine months, we've had a heavy focus on agentic AI capabilities," said Boteju. "There are many use cases in financial services and we think there are at least 50. What we're trying to build is a robust agentic AI architecture that we can deploy against multiple use cases from customer advice to software engineering to claims or underwriting."

Lloyds AI plans part of broader transformation

Lloyds's use of Vertex AI is part of a broader transformation that's focused on driving efficiencies and experiences throughout the company.

Speaking at a Morgan Stanley conference, Lloyds CEO Charlie Nunn said the company hit its targets and was feeling good about its position in the UK. Nunn said the company committed to driving market share and growth, operating leverage and change via technology.

"The most important part is what we call change, so grow-focus-change. And the change is really about building new capabilities, bringing in new technologies, and then driving underlying business and market share growth. We did that across every single part of the bank, and that's the stuff that gets me excited," said Nunn.

Lloyds said it started the first phase of its strategic transformation in 2022 with the aim of returning to growth and adding capabilities. Nunn said the bank focused on people, technology and data and specifically hired engineering talent.

"Our technology strategy over the next two years will have two distinct elements. Ongoing modernization and rationalization of our estate will continue to deliver savings and improve efficiency, providing the capacity for investment in new technologies," said Nunn, who added that the bank had more than 800 AI models live at the end of 2024 and "already launched a significant number of genAI use cases across the group."

From concept to production

Boteju said Lloyds Bank was viewing agentic AI as a concept that was just emerging as recently as last summer. "We went through a process with the Google team on a 12 week sprint to have an MVP (minimum viable product)," he said. "We started with a first step toward exposing an intelligent agent directly to customers to provide financial tips and guidance."

That use case was chosen due to less risk. Lloyds Bank refined the agent with more advice elements that can be used for other use cases, added Boteju.

Boteju said the advice assistant MVP was complete at the end of 2024 and the plan is to have a production agent in place by August or September.

Now that Lloyds Bank engineers have seen what's possible agentic AI capabilities are being used elsewhere.

"For example, we have automated our process to build data products. There's a lot of automation and our engineers have started building on top of that with an agentic approach to make the process more intuitive and easy to use," said Boteju. "Agentic AI has gone from literally PowerPoint slides 12 months ago to MVPs to engineers building agent systems."

Consistency matters

Lloyds Bank's experience with developing agent systems highlights how consistency matters.

Boteju said "it's really important that people use and deploy agents in a consistent way." He laid out the strategy to ensure consistency:

  • Lloyds Bank built a workbench that is consistent across the bank.
  • Give everyone the same set of capabilities and leverage Google Cloud advances to accelerate processes.
  • Deploy and operate guardrails centrally across the enterprise.
  • Create a center of excellence.
  • Invest in skilled users and AI literacy. "We've invested very heavily in AI literacy and data culture targeted at both technicians and business leaders," said Boteju. "On the technician side there's a lot of upskilling about how to use these capabilities. On the business side, the whole point is to reimagine your business."

The workbench needs to facilitate multiple models that are exposed to teams across the bank.

"We focused everyone on building things that scale to ensure reuse and that we focus on the highest value use cases," said Boteju. "We're excited about the next 12- to 18-months as these technologies really start to mature and add value across our bank."

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