The book’s central lesson stayed with me: technology doesn’t manage itself. Someone has to own the architecture, costs, vendors, risk, and strategy. If no one does, the organization pays for it — in redundancy, in outages, in ballooning spend, in chaos.

It got me thinking. What if I applied the same lens to my home?

🏠 You already have an IT estate. You just haven’t named it.

Most households today run on a sprawling layer of technology — devices, subscriptions, services, networks, and security tools — that rivals the complexity of a small business. The difference is that a small business has someone accountable for it. Your home probably doesn’t.

When I started tracking what I owned and what I was paying for it, the number surprised me. Across compute, mobile, peripherals, cloud services, AI tools, networking, and security — my household runs a ~$32,000 technology estate. Here’s how it breaks down:

🔌 Core Infrastructure — $26,000 (81%): The hardware layer. MacBooks, iPhones, iPads, monitors, mesh network, mobile plans, and home security cameras. The one that sneaks up on you — purchases are spread over years and rarely reviewed together.

☁️ Cloud Services & Apps — $5,000 (16%): The subscription layer. Microsoft 365, Google Workspace, Notion, LinkedIn, Figma, Grammarly, Spotify, YouTube, and 20+ more. Each is individually defensible. Collectively, they compound every year.

🤖 AI & Governance — $1,000 (2.6%): ChatGPT Plus, Grok, AppleCare — plus zero-cost but high-effort governance routines: OS updates, backups, parental controls, subscription audits. The policy layer most households skip entirely.

🛡️ Security & Identity — $100 (0.4%): Bitdefender, VPN, Password Manager, Authenticator. Remarkably inexpensive for what it protects — but only effective if the governance layer above it actually runs.

📖 The CIO move: inventory before judgment

When Jim Barton takes over as CIO, his first instinct is to look competent — to pretend he understands the landscape. His mentor pushes back. The most important thing a new CIO can do, she tells him, is get an honest picture of what exists before making any decisions.

That’s the first move for the domestic CIO, too: inventory before judgment.

Once you have the picture, the book’s framework maps cleanly onto a household:

📋 Governance — who is accountable for technology decisions in your home? Who reviews the subscriptions? Who handles OS updates before a vulnerability becomes a breach? If the answer is “nobody really” — you have a governance gap.

💰 Cost management — do you know your total annual technology spend? Not your biggest bill — your total. Most people don’t. When I added it up, the number was clear. It forced me to ask which services truly earned their place and which had survived only by being forgettable.

⚡ Risk — what happens when the primary device fails? When the mesh router goes down during a school day? Enterprise IT builds redundancy into critical systems. Your home probably doesn’t. But you can build the mindset.

🤝 Vendor relationships — annual plans auto-renew. Family plans are routinely underused. Free tiers get you in the door — but once your photos, files, and habits are embedded in a platform, leaving becomes expensive, whether you pay to stay or pay the time cost of moving out. Understanding how vendors actually work is vendor literacy — and it pays.

🔍 The governance layer nobody builds at home

This is the only layer in my stack with no line-item cost. It's also the one that makes everything else work.

Password updates. OS and app updates. Backups. Parental controls. Subscription audits. These cost nothing in money — and they are the reason the $100 I spend on security tools actually works. Strip the governance layer, and the security layer stops mattering.

In enterprise IT, this is called the operating model. It’s not the software you buy — it’s the practices you maintain. Jim Barton’s biggest early mistake in the book is focusing on tools while underinvesting in process. Most households make the same error.

A domestic operating model doesn’t need to be elaborate:

• ✅ Monthly — cancel any subscription unused in the last 90 days

• ✅ Quarterly — rotate passwords on critical accounts

• ✅ Annually — assess hardware age; anything 5+ years approaching end-of-life?

• ✅ Weekly — verify your backups actually restore

Thirty minutes a month. The discipline is the product.

📊 Start with the audit. Here’s a tracker to help.

Forget the tools for now. Start with the inventory.

I’ve structured the tracker I use around the same four layers — so you can map your own stack the way I mapped mine. It’s free, it’s a Google Sheet, and you make a copy to use as your own.

→ Get the Home IT Tracker — free template

List every device. List every subscription. Categorize it. Calculate the annual total. Look at the number — it will tell you something useful.

🎯 This is not about cutting costs

The goal isn’t to spend less. The goal is to spend deliberately.

A household that spends $32,000 on technology and gets full value from that investment — productivity, security, connectivity, creative capability, AI leverage — is making a sound decision. A household that spends $12,000 with no visibility into what’s running, what’s expiring, or what’s vulnerable is not.

In 2026, understanding your domestic technology stack isn’t a hobby for the technically minded. It’s a household imperative — for your costs, your risks, and your family’s safety. The only question is whether you get ahead of it deliberately or get surprised by it later.

About this piece: Written from personal experience managing a domestic IT estate across four layers — infrastructure, cloud services, security, and AI. The referenced tracker uses the same taxonomy as here and is available as a free Google Sheets template.

Sources

1. Austin, Nolan & O’Donnell — Adventures of an IT Leader, Harvard Business Review Press

2. Home IT Tracker Template — Google Sheets

Remember the old way of adding a Custom Colors section to PowerPoint? It often involved a complicated process of hacking XML files, unzipping presentations, and manually editing code.

Fast forward to today, and AI 🤖 has completely changed the game.

I’ve been exploring a workflow using ChatGPT-5 that makes this once-tedious task incredibly simple.

Here’s how it works:

↳ You provide a list of your Hex color codes to generate the necessary XML code.
↳ You can then upload your presentation and have the AI insert the custom color theme directly into the file for you.

What used to take deep technical know-how now takes just two simple prompts. In the final file, your new palette appears perfectly in a dedicated “Custom Colors” section within the Shape Fill menu. This is a fantastic example of how AI is streamlining our workflows.

See the complete visual guide on Figma Slides and give it a try: https://hub.ontopraxis.ai/custom-colors-ppt

In a market fixated on models and apps—and headlines about GPUs and shortages—it’s easy to miss where production AI is decided: infrastructure. Chips matter, but they are only one component. The foundational force that powers the entire AI economy lives deeper in the stack.

The decisive layer is the operational substrate—servers, compute fabrics, interconnects, orchestration, and data pipelines—where reliability, governance, and unit economics are made real. When infrastructure becomes invisible, reliable, and scalable, enterprises build on it. That is where durable advantage accrues.

More AI is not possible without Core AI (a.k.a. AI Infra)

This brief reframes the AI-infrastructure conversation from feeds and speeds to business outcomes and platform leverage. It outlines the stack, draws the GTM implications, and offers guiding principles for making AI Factory solutions strategically essential.

The focus here is on vendors building the physical and hybrid backbone of enterprise AI—from servers and fabrics to orchestration and on-prem deployment platforms.

Here’s a high-level perspective for AI-infrastructure providers rethinking narrative and GTM.

The Stack Everyone Sees (and the One They Don’t)

Disclaimer: The Total Addressable Market (TAM) figures presented herein are high-level estimates intended to illustrate the potential scope of the market. They are based on specific assumptions and publicly available data, which are subject to change. This information should not be construed as a factual representation or a guarantee of future results. We strongly advise conducting independent due diligence before making any business decisions based on this data.

Let’s start with the obvious. The AI stack is crowded and rapidly expanding. By the end of 2025, it’s projected to hit a $576.5B total addressable market across four major layers:

• Applications: the visible layer where AI shows up—Copilot, Jasper, customer agents, and all the rest.

• Models: the brains that power those applications—GPT, Gemini, Claude, Mistral.

• Data: the fuel models need—platforms like Snowflake, Databricks, MongoDB.

• Infrastructure: the foundation—servers, chips, networks, orchestration, and all the plumbing that holds the whole thing up.

Together, these layers form a massive market, and yet, the lion’s share of value is still flowing into infrastructure ($307 billion)—the layer that gets the least attention but carries the most weight.

What people think AI Infra is

If you say “AI infrastructure” in a room full of execs, you’ll get polite nods—and then they’ll tune out. Why? Because to many, it still means racks of servers, cloud contracts, or a CapEx problem for someone else to worry about. It’s seen as plumbing. Necessary, yes—but not strategic.

Also, much of the newfound excitement around infrastructure has been chip-driven—focused on accelerators and memory specs. But raw compute doesn’t equal real-world deployment.

AI infrastructure is the platform layer. It’s what operationalizes those chips.

It determines:

• Speed → how fast you can train and infer

• Intelligence → what kind of models you can run

• Utility → whether your enterprise use cases even work

• Scale → how far and wide your AI programs can go

And most importantly, it defines where the value pools consolidate as adoption matures. Because chips don’t make AI deployable. Infrastructure does.

AI Infrastructure demand is surging

Enterprise adoption of AI is accelerating—but success hinges on the reliability, performance, and compliance of the infrastructure stack. 1 ^&2

Industry signals suggest three converging shifts that represent a significant market opportunity for infrastructure players:

• Scale of demand: As per IDC, the AI-centric infrastructure market is expected to grow from $55B in 2023 to over $229B in 2028, driven by AI model training, inference, and edge deployments.

💡 From Dell Technologies, Q1 FY26 Earnings:

• Architecture diversification: Enterprises are moving beyond cloud-first models. According to a BCG study, 30% of companies with high AI maturity are planning to repatriate workloads from the cloud to on-premises, citing cost, latency, and data sovereignty concerns.

• Stack complexity: Infrastructure decisions now span compute, networking, storage, orchestration, model integration, and responsible AI. Decision-makers seek integrated platforms over point solutions.

These dynamics create an opportunity for infrastructure providers, especially those offering secure, sovereign, and scalable platforms tailored to AI use cases.

Re-think your GTM strategy and positioning

What does this mean for AI infrastructure vendors, especially those building servers, networking, storage, cloud, or hybrid environments?

It means letting go of "feeds and speeds" and embracing a new identity: as strategic enablers of AI value.

Winning in this market isn’t about just shipping hardware. It’s about delivering outcomes. That’s why vendors need to evolve into growth enablers—powering not just AI workloads, but the business transformations those workloads unlock.

To get there, we propose a GTM model built around flexible deployment and usage-aligned growth—across CapEx and OpEx paths.

Foundational infrastructure layer

This is the engine room—the operational heart of AI. And it’s no longer just about raw compute or static capacity. Today’s AI infrastructure must be both provisioned and programmable, supporting enterprises that want to own their deployment (CapEx) and those that want to scale dynamically (OpEx).

That’s the opportunity: infrastructure becomes not just a platform, but a pricing and deployment strategy. By offering both traditional CapEx models and modern, usage-based options—backed by real-time telemetry and elastic controls—you meet customers where they are and grow with them as needs evolve.

CapEx gives control. OpEx gives agility. Your platform offers both.

Key tenets of this layer:

• Go beyond hardware: integrate orchestration, AI-ready services, and hybrid deployment support

• Offer deployment optionality: on-prem CapEx, hybrid, or cloud OpEx

• Embed usage-awareness even in CapEx deployments (e.g., for visibility and optimization)

• Wrap it all in Responsible AI guardrails: security, compliance, and auditability

• Build trust by aligning spend with value—whether paid upfront or scaled over time

AI Builder layer

This is where innovation happens—from first fine-tune to full-scale deployment. Builders today want freedom: to build quickly, scale predictably, and avoid vendor lock-in. Whether they’re operating on a cloud budget or building on dedicated infrastructure, your GTM needs to empower both modes.

With OpEx, that means usage dashboards, credit-based onboarding, and real-time spend tracking. With CapEx, it means performance tuning tools, optimized configs, and APIs that make private deployments feel cloud-native.

Key tenets of this layer:

• Equip builders with SDKs, APIs, and model deployment frameworks

• Support usage-aware workflows even in fixed-cost environments

• Let teams optimize performance and cost across both billing models

• Remove friction: builders should be focused on innovation, not infrastructure procurement

GTM execution layer

This is where you align infrastructure with real business outcomes. And just like your customers’ workloads, your GTM model should scale across commercial contexts—whether that's a CFO signing a multi-year infrastructure investment or a business unit testing use cases on variable pricing.

Your GTM motion should speak to both finance and dev—because buying happens across both. In one motion, you might land a CapEx-heavy deal with a sovereign AI deployment. In another, you may guide a FinOps team toward usage-based scale tied to enterprise OKRs.

Key tenets of this layer:

• Position your GTM around customer choice: pay up front, or grow as you go

• Use telemetry and workload insights to expand intelligently—across both models

• Co-sell with partners, but retain visibility into usage and outcomes

• Track and measure value through adoption, not just bookings

CapEx or OpEx – Consumption powers both

Too often, “consumption” is framed as just an OpEx or cloud play. But in this GTM model, consumption is a unifier—a feedback mechanism that connects all layers and pricing models.

• CapEx customers still benefit from usage visibility, optimization, and workload observability

• OpEx customers scale naturally, with pricing that matches impact

• GTM teams use the same telemetry to drive upsell, adoption, and retention—regardless of the commercial model

It’s not either/or. It’s the power of And.

CapEx control. OpEx agility. Your infra stack delivers both—with intelligence built in.

If you lead GTM at an AI Infrastructure vendor

The opportunity ahead

This moment isn’t about faster chips or bigger clusters. It’s about repositioning infrastructure—from a cost center to a visible lever for AI delivery and enterprise value.

Smart infrastructure vendors don’t need to outscale the hyperscalers.

Instead, they’ll differentiate where enterprise buyers feel the most pain: deployment flexibility, cost predictability, data governance, and usage-aligned growth.

Because the next phase of AI isn’t powered by hype. It’s powered by usage.

And usage starts with infrastructure that flexes with the workload, the buyer, and the business.

Jack Dobson: Well, as mentioned, this is DevCon, one of one. To kick us off, why now? Why have we started DevCon at this point, and what’s the journey that led us to today?

Shyam Sankar: I'm so excited to kick off our first DevCon here. It’s really the culmination of a lot of work and focus on the product side over the last two years to make this a developer platform. If you think about Palantir, we hold sacred the primacy of winning — thinking backwards from first principles to manifest outcomes that matter, not just inheriting market assumptions.

There’s a legitimacy crisis in the world today — doors falling off airplanes, sclerotic institutions, entropy in the system. Palantir’s journey has been about counteracting that.

Over the last two to three years, we’ve seen that to honor this primacy of winning, we need to extend our culture and tool chain to developers like you, our customers, to build these solutions. For example, 700 uniformed service members from the 101st Airborne built a search-and-rescue operating picture for Hurricane Helene in 24 hours. These aren’t trained computer scientists, but builders who saved lives with code — something unimaginable a decade ago. Now, we’re externalizing our tool chain, tradecraft, and culture, making you all forward-deployed engineers.

Jack Dobson: Incredible. We’ve seen the products develop over time, but it’s not just a product story. There’s a mindset shift in approaching problem-solving and building solutions. Have you seen our customers develop that mindset and intuition over time?

Shyam Sankar: Yes, ideas we earned inductively in the field that were once heterodox are now truisms. Ten years ago, it was radical to say that separate systems for insight and operations were a bug, not a feature. People were comfortable in their data science silos, disconnected from the factory floor, screwing up the OODA loop. Now, it’s accepted that data isn’t the new oil — it’s the new snake oil if you can’t act on it. Customers are now receptive to absorbing the trade craft and approach behind our software, making the timing perfect for DevCon.

Jack Dobson: On that note, we’ve seen this evolution across customers in commercial and government sectors. Is there a step-by-step process to get to that mindset, or do you just have to break through?

Shyam Sankar: There’s no process — only the content. You have to work backwards from the problems and roll up your sleeves. The AI revolution is experiential; those experimenting and iterating from prototype to production are learning the fastest and getting the best results. Hand-wringing, commissioning studies, or debating use cases leaves you in the dust. Look at Europe versus the U.S. — the performance gap reflects this difference in approach.

Jack Dobson: Speaking of AI, the model landscape is evening out with incremental progress, but deployment still has leaps and bounds to go. How is Palantir thinking about AI at this stage?

Shyam Sankar: Borrowing from Stephen Cohen, one of Palantir’s co-founders, there’s AI supply and AI demand. On the supply side, models are improving but converging —closed and open-source models are getting more similar, and the rate of improvement is leveling off. The real gap is on the demand side: how do you use these models to achieve economic value?

We’re in the early innings of building the necessary tool chain. It’s like the shift from analog to digital computing — we’ve abstracted zeros and ones over analog waveforms with error correction and hardware/software ecosystems. With LLMs, we’re in an indeterminate phase, wrestling with stochastic outputs in a historically deterministic tool chain. We’re ahead in providing developers the tools to get from prototype to production fast. Look at self-driving cars: the 2005 DARPA Grand Challenge was a great prototype, but it took 20 years to handle city driving. We need to focus on getting to production quickly as a community.

AI as Labor for Automation

Beyond copilots, AI’s value is in automation and enterprise autonomy, decomposing workflows into human, expert, and software roles.

Jack Dobson: We announced AIP in April last year (2023), approaching its first full year. How has our approach to use cases evolved, especially as we move beyond chat?

Shyam Sankar: The last few months have solidified my conviction that the value lies in automation and enterprise autonomy. Chat is a dead end — charismatic but limited. A copilot might boost productivity by 50%, but the real economic value is in treating AI as a type of labor. Decompose problems into what humans, subject matter experts, workflows, and traditional software can do, then string them together. The day-two problems— telemetry, agent behavior, refining edge cases —become critical. Automation is limited by edge cases, not the happy path. Our tool chain helps developers capture feedback and handle those edge cases fast, promoting users to managers of AI agents.

Jack Dobson: Here at DevCon, people will be building over the next couple of days. Historically, some of the strongest workflows I’ve seen at Palantir were built by customers— experts empowered by the tooling. What examples have you seen of customers evolving from users to agent governors? Are there leaders or laggers across commercial and government sectors?

Shyam Sankar: The use cases I’m most excited about are where humans aren’t perfect and time is the enemy. On the government side, machine-assisted disclosure of intelligence sharing stands out. Sharing intelligence with foreign partners used to take three days — humans reading guides and raw data. In three days, the missile’s already landed.

Working Backwards from Outcomes

Shyam emphasizes solving real problems over following rigid processes, a mindset shift critical for impactful development.

Now, LLMs rewrite intelligence, cite the guide, and flag where humans should QC (Quality Control), cutting it to three minutes. Humans were imperfect even with three days, so this high-pressure use case drives pragmatic adoption. Beyond copilots, which boost the median worker, AI levers up the best humans — core drivers of productivity — institutionalizing their impact across workflows.

Jack Dobson: As people leave DevCon and return to their organizations, there’s chaos in the creativity required to deliver this. How do you see organizations changing as they adopt AI — will they seek protocols, or should they brace for more fluctuation?

Shyam Sankar: Brace for creative chaos. Organizational boundaries from the pre-AI era won’t hold. The winners will reimagine how they’re organized, pushing technical expertise into operations. Take Hurricane Helene: 700 service members tailored software like an Ironman suit to meet the moment — not formal developers, but empowered builders. AI liberates developers to move into the business, focusing on high-impact outcomes, not boilerplate code.

Creative Chaos as Opportunity

Organizations must embrace disruption, reimagining structures and pushing technical expertise into operations to succeed in an AI-driven world.

Jack Dobson: Looking inward, how do you see Palantir’s culture changing with forward-deployed engineers and customers as builders?

Shyam Sankar: I tell our forward-deployed engineers: developers are now your customers, just like embedding in Detroit or Djibouti. Metabolize pain and excrete product. I want DevCon attendees to be perpetually dissatisfied — your complaints signal what the platform needs. Internally, AI requires reinventing tradecraft from first principles.

Clinging to old workflows gets marginal gains; maximalist reimagination creates opportunities — like three-month hires leading big projects over three-year veterans.

Jack Dobson: We’re rolling into 2025 soon. What are you most excited about next year?

Shyam Sankar: Automation — Project Autopilot internally. It mirrors our government-side Target Workbench, automating high-stakes targeting processes. We’re bringing that trade craft into a tool chain for developers to build automations, govern edge cases, and create feedback loops, instantiating armies of AI agents.

Jack Dobson: With our last few minutes, what should builders and technical leaders at DevCon bear in mind over the next two days?

Shyam Sankar: Focus on what makes you different and special — what creates alpha in your institutions. AWS deprecated infrastructure; our tool chain can deprecate backend development. That’s hard, undifferentiated work. The advantage lies in applications that run your business. Our declarative approach lets you compete on speed and focus on what sets you apart. Let your ambition run unbridled.

Disclaimer: The views expressed by the speakers are their own and do not represent ontopraxis. All insights are attributed to the speakers; any inaccuracies are my responsibility.

The podcast captures key highlights:

↳ enterprises embracing tailored AI models with a focus on governance, 

↳ the persistent challenge of moving AI past proof-of-concept due to data silos, and

↳ the intriguing value-shift pyramid—where infrastructure dominates today, but applications will lead tomorrow. 

↳ It also spotlights Databricks’ unique edge with its unified, multi-cloud platform.

Listen to how the AI hosts break it down - offering a fresh take on an already compelling discussion.

We conducted an Opportunity Assessment and Strategic Alignment initiative for an OT client, laying the foundation for a scalable and sustainable channel model in their industrial services business.

📖 Client Overview

A Fortune 500 operational technology company focused on the energy management and automation industry.

💰 Annual revenue: ~$39B

🎯 Project domain: Channel Development

🌎 Geo scope: South Pacific

🔬 Situation

The client aimed to capitalize on a significant growth opportunity in the services channel across the Pacific Zone. Despite a strong belief in the channel’s potential, gaps in the operating model, varied expectations, and internal challenges hindered the realization of a cohesive and scalable services strategy. Key challenges included:

• Low attach rates for concurrent services, with significant untapped potential in non-concurrent services.

• Limited enablement and motivation for channel partners to sell services effectively.

• Fear of product cannibalization and internal misalignment on channel strategy.

The objective was to build a multi-year services channel plan, starting with a comprehensive opportunity assessment and extending to strategy development and deployment.

✍🏼 Our Approach

• Opportunity Assessment

  • Conducted a detailed SWOT analysis to identify strengths, weaknesses, opportunities, and threats within the channel operating model.

  • Analyzed attach rates, service ratios, and RTM (route-to-market) mix to uncover growth levers for services across the lifecycle.

• Strategic Alignment and Planning

  • Designed guiding principles for a services channel model, balancing immediate revenue growth with long-term strategic investments.

  • Created a multi-stage roadmap, including defining the partner play, value proposition, and incentive structures.

📈 Impact

• Identified a pathway to build a €10M annual services channel run rate within two years.

• Unlocked new revenue streams by enabling partners to sell non-concurrent services, increasing attach rates across RTMs.

• Achieved greater alignment among internal stakeholders, ensuring buy-in across business units.

To know more, reach out to us at curious@ontopraxis.ai

We collaborated with a leading hyperscaler and empowered their partners with the tools, strategies, and support needed to succeed in the competitive public cloud market. This engagement addressed immediate marketing challenges and laid the foundation for sustained partner-driven growth.

📖 Client Overview

Part of a Fortune 10 tech company offering a new way to the cloud.

💰 Annual revenue: ~$33B

🎯 Project domain: Partner Marketing

🌎 Geo scope: Asia Pacific & Japan

🔬 Situation

The client faced challenges in enabling its partners to capitalize on the growing public cloud market. Research revealed key constraints hampering marketing execution: a lack of intent, talent, and content. To address these gaps, the client sought a strategic initiative to empower its partners and accelerate time to value.

✍🏼 Our Approach

• Strategic Alignment

  • Collaborated with the client to design and implement a partner demand generation engine, aligning it with the organization’s business and sales priorities.

  • Identified and addressed the unique challenges of partner marketers, focusing on intent-building and content optimization.

• Customized Marketing Support

  • Developed personalized marketing plans for partners, optimizing existing strategies to deliver higher ROI.

  • Provided hands-on guidance to improve demand generation efforts, including recommending suitable marketing packages and agencies.

• Comprehensive Execution Assistance

  • Supported campaign execution by facilitating agency selection, brief creation, and implementation oversight.

  • Ensured smooth collaboration by acting as an escalation point for agency-related issues.

• Capability Building

  • Enhanced partner marketing skills through the sharing of best practices and knowledge.

  • Curated impactful content and resources to support effective marketing campaigns.

📈 Impact

• Enhanced Marketing Outcomes

  • Partners praised the high-quality content, immersive experiences, and tailored support, leading to improved marketing execution.

• Optimized Marketing Fund Utilization

  • Enabled better and more effective use of marketing funds by aligning investments with targeted strategies and high-impact campaigns.

• Partner Testimony

  • Partners consistently highlighted our role in simplifying campaign execution, improving marketing strategy, and enhancing team coordination.

To know more, reach out to us at curious@ontopraxis.ai

We partnered with a leading technology provider to design and deploy a sales acceleration plan for one of their multi-billion-dollar global business units.

📖 Client Overview

A Fortune 500 technology company operating in the hybrid cloud world.

💰 Annual revenue: ~$25B

🎯 Project domain: Enterprise and Channel Sales

🌎 Geo scope: Global

🔬 Situation

The client faced stagnant growth in one of their core businesses, compounded by a salesforce with fragmented priorities. Revitalizing both direct and indirect sales efforts was crucial to reigniting growth.

✍🏼 Our Approach

• Developed a comprehensive understanding of the customer value proposition and existing Go-to-Market (GTM) structure.

• Designed guiding principles to enable a seamless and frictionless sales experience.

• Identified key growth levers and implemented targeted programs to activate them.

📈 Impact

• Helped surpass annual sales targets, driving year-over-year growth.

• Improved alignment and engagement across field sales teams.

• Increased mindshare and influence within the partner ecosystem.

To know more, reach out to us at curious@ontopraxis.ai

We worked with an IT Infrastructure and Services client to build and implement a channel plan for scaling their managed
services offering through their global partner ecosystem.

📖 Client Overview

A Fortune 500 technology company with the purpose to advance the way people live and work.

💰 Annual revenue: ~$27B

🎯 Project domain: Channel Sales

🌎 Geo scope: Global

🔬 Situation

The IT marketplace is shifting towards a service-led model, and our client, recognizing this trend, aimed to establish a thriving managed services partner ecosystem. To achieve this, they needed a comprehensive plan to attract and nurture partners, ultimately expanding their market reach through indirect channels.

✍🏼 Our Approach

Our approach centered on achieving full operating model alignment, going beyond just sales alignment, and encompassed the following:

• Market analysis: A deep dive into market dynamics to identify opportunities and challenges.

• Strategic partner positioning: Defining clear roles and value propositions for each partner type.

• Collaborative workshops: Joint sessions with partners to foster alignment and build relationships.

• Sales enablement: Equipping partners with the tools and knowledge for success.

• Demand generation support: Providing resources to drive leads and stimulate market demand.

📈 Impact

A well-documented plan facilitated leadership alignment, secured sponsorship, and garnered investment to support this multi-year initiative.

We also established an incubation program, offering hands-on guidance to partners dedicated to collaboratively developing their managed services practice with the client.

To know more, reach out to us at curious@ontopraxis.ai

We partnered with a leading technology company to design and execute a content strategy for their annual flagship marketing event, empowering partner marketers to become future-ready.

📖 Client Overview

A Fortune Top 50 technology company with the purpose to create technologies that drive human progress.

💰 Annual revenue: ~$94B

🎯 Project domain: Partner Marketing

🌎 Geo scope: Asia Pacific & Japan

🔬 Situation

The client sought innovative ideas and a content strategy to drive strategic alignment and empower their partner marketers.

✍🏼 Our Approach

Focusing on the target personas, we crafted a balanced approach combining Outside-in and Inside-out perspectives to foster seamless marketing and sales collaboration. Our plan featured insightful, inspirational, and actionable content on achieving commercial excellence.

📈 Impact

• Engaged over 500 participants during a two-day virtual event.

• Partners praised the high-quality content, interactive engagement, and immersive experience.

• Enabled the client to transition from articulating value to creating value.

To know more, reach out to us at curious@ontopraxis.ai

Banks are projected to spend $6 BILLION on GenAI in 2024, skyrocketing to $85 BILLION by 2030! 📈

💡 Why the massive investment? Generative AI holds the potential to:

• Boost productivity across critical banking functions

• Enhance customer satisfaction and employee experiences

• Strengthen risk management capabilities

This could unlock $200-$340 BILLION in value for the industry over the next 3 to 5 years! 🤑

The future of retail? It's not on its way—it's already in our carts!

Here's the story of how @ontopraxis found its voice and visual identity ↴

↳ https://hub.ontopraxis.ai/my-travel-map

Check it out:
↳ http://hub.ontopraxis.ai/my-reading-list

My favorite ❤️ bookstore ➝ Must visit if you are in Singapore:
↳ Books Kinokuniya @Takashimaya Shopping Centre

[originally posted on Sept 9, 2024]

As I complete my first 100 days in America, I’m filled with gratitude and reflection on the journey so far. Moving across the world from Bangalore 🇮🇳 to Singapore 🇸🇬 and now America 🇺🇸, I'm reminded that growth isn't just about building businesses; it's about transforming ourselves.

⛺️ Basecamp [Bangalore] was where it all started — the foundation of my career. It was the learning phase, establishing a foothold and laying the groundwork for what would come. A place where I embraced the basics of business building, partnerships, and strategy.

🧗🏼‍♂️ Bootcamp [Singapore] was where the intensity ramped up. It was a transformative phase marked by accelerated learning, overcoming challenges, and refining my approach. Singapore sharpened my mindset with global exposure and strategic thinking, pushing me to evolve and adapt.

👨🏼‍🎨 Buildcamp [America] is where I stand today, weaving together the threads from Basecamp and Bootcamp to create something meaningful. America has provided the canvas for the next level of innovation, collaboration, and purpose-driven impact.

Steve Jobs once said: “Don’t be trapped by the dogma of a career. Follow your passion and calling.”

These past 100 days have underscored the importance of that message.

Here’s to the next phase of this journey: building with purpose and embracing the power of calling over a career.

For further reading on Steve's message: https://lnkd.in/gn6N6V9t

In the fast-paced world of technology, partnerships aren't just strategic...they're existential. With estimates suggesting that 70-80% of business flows through partners, the importance of efficient channel management cannot be overstated. Salesforce research underscores this, with 84% of sales professionals acknowledging that partner collaborations are driving more significant revenue impacts than in the past. But managing those partnerships? That's where things get complex.

Key aspects of this complexity include:

• Multiple vendors: Partners often deal with numerous vendors, each with their own portfolio of products, sales strategies, marketing materials, operational requirements, and enablement programs.

• Diverse customers: Partners must cater to various customer segments, including IT decision-makers, finance departments, procurement teams, and end-users, each with unique needs and expectations.

• Internal organizational complexity: Within their organizations, partners juggle roles and responsibilities across various departments, including sales, marketing, operations, and enablement, adding another layer of complexity to their operations.

The traditional playbook is outdated. We're drowning in data, struggling to meet partner needs, and losing valuable time to operational friction.

This is where PartnerGrok comes in. It's not just another tool; it's a catalyst. An AI-powered copilot designed to transform your channel into an intelligent, synchronized, revenue-generating machine.

The Power of Synthesis AI

PartnerGrok leverages the power of Synthesis AI, a new wave of generative AI applications focused on decision advantage. By converging information from various sources, PartnerGrok provides actionable insights and automates tasks, enabling channel chiefs to make informed decisions and optimize their partner strategies.

Here’s how PartnerGrok can help drive productivity gains:

• Personalized partner journeys: It can tailor interactions for each partner and their specific personas, anticipating their needs and providing relevant content and training.

• Real-time support and insights: It can offer immediate assistance with inquiries, deal registration, troubleshooting, and more, using data analysis to identify potential roadblocks and opportunities.

• Enhanced collaboration: It will facilitate seamless communication between your company and partners, streamlining processes like MDF management, co-marketing campaigns, and lead sharing.

• Predictive analytics: It will analyze partner data and market trends to predict potential churn, identify high-potential partners, and suggest personalized growth strategies.

• Ontology-powered Intelligence: It will leverage ontology to understand the context of partner interactions, make accurate predictions and recommendations, and streamline workflows.

The Renaissance of Partnership powered by AI

The B2B sales landscape is undergoing a seismic shift. Heightened competition, evolving buyer expectations, and economic uncertainty are forcing organizations to re-evaluate their go-to-market strategies. 

The channel ecosystem has grown in scale and complexity, yet remains a critical avenue for driving growth and achieving cost-effective sales.

As Channel Chiefs, you are the architects of your organization’s partner ecosystem, responsible for navigating the complexities of channel relationships, driving growth, and ensuring alignment between partners and customers. 

The age of intelligence is here, and it's time to seize the opportunity.

UPDATE: Nov 26, 2024: Announcing AWS Partner Assistant, a generative AI-powered virtual assistant for AWS Partners1

Embarking on the journey

The path forward demands clarity and strategic vision.

• Define your north star: Establish clear objectives and identify the guiding principle to shape your channel strategy.

• Prioritize use cases: Focus on the areas where Synthesis AI can deliver the most immediate and impactful value.

• Start small, scale fast: Initiate proof-of-concepts to validate the technology's potential. With each success, expand its capabilities and empower your channel team.

A new future of channel management is here. Embrace it with Synthesis AI.

Download the deck