AI Services · Strategy and Innovation
AI and Innovation:
Turning AI Potential into Measurable Revenue
87% of CMOs feel intense pressure to prove marketing ROI. AI is the most powerful tool available to close that gap — and the most consistently misdeployed. Most organizations buy AI tools and deploy them into existing workflows without asking the revenue question: which AI investments actually move pipeline? TPG's AI and Innovation practice answers that question first, then builds the roadmap, deploys the technology, and governs the adoption — so AI produces revenue outcomes, not productivity claims.
TPG's Proprietary AI Framework
R.A.I.N.: The Revenue Artificial Intelligence Network
The R.A.I.N. framework is how TPG structures AI deployment across the revenue marketing function. R stands for Revenue: every AI initiative tied to a pipeline metric. A stands for Artificial: AI and machine learning applied where they move buyers. I stands for Intelligence: data-driven insight that powers decisions. N stands for Network: the integrated ecosystem that delivers outcomes at scale. R.A.I.N. is the AI delivery layer of the RM6 Revenue Marketing Operating System.
Revenue
Every AI initiative is tied directly to a measurable revenue outcome. No deployment without a defined pipeline contribution metric and a measurement plan.
Artificial
AI and machine learning applied specifically where they move buyers through the Revenue Loop: scoring, prioritization, personalization, and attribution.
Intelligence
Data-driven decision intelligence that powers the entire framework — from predictive scoring to attribution models to AI buyer visibility through AXO.
Network
The integrated ecosystem of technology, people, and process that delivers revenue outcomes at scale — not isolated tools running in separate silos.
Full AI Services Portfolio
Every AI capability the revenue engine needs
TPG delivers AI services across strategy, systems, and intelligence — connected to revenue outcomes at every layer. As a member of HubSpot's AI Partner Advisory Board, TPG has early access to AI feature development across the HubSpot platform.
AI Roadmap Accelerator
90-day program producing a prioritized, executable AI roadmap using the R.A.I.N. framework.
Emerging Innovations
Forward-looking AI capability assessment: what technologies are ready to deploy now versus monitor.
AI Agents and Automation
AI agent design and deployment across marketing, sales, and RevOps workflows for autonomous revenue tasks.
Marketing Operations Automation
AI-driven automation of campaign execution, lead routing, scoring, and reporting workflows.
AI for Financial Services
Specialized AI agents for financial institution marketing teams operating under compliance constraints.
Predictive and Generative AI
Predictive lead scoring, propensity modeling, churn prediction, and generative AI for content at scale.
AI-Driven Personalization
Real-time, AI-powered personalization across email, web, and campaign channels at enterprise scale.
Data and Decision Intelligence
AI-powered analytics, attribution modeling, and the decision infrastructure that makes AI outputs actionable.
AXO: AI Buyer Visibility
Measuring and improving how your brand is represented in AI buyer research tools: ChatGPT, Perplexity, Gemini, Claude.
Section 01
Why AI Adoption Is a Revenue Operations Question, Not a Productivity Question
The framing that separates AI deployments that produce pipeline from AI deployments that produce activity metrics.
The productivity trap: why most B2B AI deployments fail to move pipeline
Most B2B marketing AI programs are measured against the wrong benchmark. The question asked at the start of the program is "how much time can we save?" rather than "which workflows, when augmented with AI, produce more pipeline?" These are different questions that lead to different investments. A content team that deploys AI to produce more blog posts faster has solved a productivity problem. If those blog posts are not structured for AI citation and buyer question coverage, the productivity gain produces more content that reaches fewer buyers. A demand generation team that deploys AI for email send-time optimization has improved one variable in a program that may be misconfigured at the strategic level. The AI capability is real; the workflow it was inserted into is not producing the outcome it was designed for. Under 1 in 5 B2B companies have achieved Revenue Marketing maturity with AI integrated into their revenue motion, which means the overwhelming majority of B2B AI deployments are improving productivity metrics without connecting to pipeline.
TPG frames AI adoption as a revenue operations question from the first conversation, because the answer changes everything about which AI capabilities to deploy first, in which workflows, with which data quality prerequisites, and with which governance model. The AI capabilities that produce the fastest pipeline impact when correctly deployed are: lead scoring and account prioritization models that improve MQL quality and sales conversion rates from marketing-sourced leads; content personalization engines that match message to buyer stage and persona at the point of engagement rather than through manual segmentation; attribution models that connect AI-assisted campaign interactions to opportunity creation; and AXO programs that ensure the brand is accurately represented in the AI tools buyers use for independent research before any sales conversation begins. The MAKE IT approach — Measurement, Analysis, Knowledge, Execution, Integration, Testing — is the operational framework that ensures each of these AI deployments is continuously optimized against revenue outcomes rather than activity metrics.
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Section 02
The R.A.I.N. Framework: AI Structured Around Revenue Outcomes
How TPG's Revenue Artificial Intelligence Network framework connects every AI capability to a specific revenue marketing dimension — preventing the isolated tool deployment that characterizes most failed AI programs.
Why the R.A.I.N. framework produces different outcomes than a point-solution AI deployment approach
Most B2B AI programs are assembled as collections of point solutions: a predictive lead scoring tool here, an AI content generator there, a send-time optimization feature enabled in the MAP. Each capability is real and valuable in isolation. The failure mode is not in the tools. It is in the absence of a unifying model that connects the tools to a revenue outcome, sequences their deployment against data quality prerequisites, and governs their adoption across the teams that need to trust and act on their outputs. A predictive lead scoring model that the sales team does not trust because they were not involved in its calibration produces scores that marketing acts on and sales ignores. An AI content generator deployed without a content strategy produces more content of unclear quality faster. Send-time optimization deployed on a nurture program that is not advancing buyers through the Revenue Loop optimizes the delivery of an ineffective program. The R.A.I.N. framework prevents each of these failure modes by anchoring every AI deployment to a specific revenue marketing dimension and requiring that each deployment be measured against the pipeline outcome it was designed to influence.
The four R.A.I.N. dimensions map directly to the RM6 control dimensions — which means the R.A.I.N. framework is not a separate AI methodology but an AI deployment layer within the RM6 operating system the client is already building or improving. This integration produces a critical benefit: the RM6 diagnostic that starts every TPG engagement identifies which of the six dimensions represents the current bottleneck to pipeline contribution, and the R.A.I.N. AI deployment sequence prioritizes the AI capabilities that address that bottleneck first. An organization whose primary constraint is Strategy (ICP and MQL definition) deploys AI for ICP refinement and closed-won analysis before deploying AI for content personalization. An organization whose primary constraint is Technology (MAP misconfiguration and attribution gaps) deploys AI for attribution modeling and MAP optimization before deploying AI for campaign creation. The sequence matters because AI deployed before the foundational capability is stable amplifies the problem rather than solving it.
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Section 03
The AI Roadmap Accelerator: 90-Day Transformation Program
How TPG's AI Roadmap Accelerator produces a prioritized, executable AI roadmap in 90 days — with use cases scored by revenue impact, an AI Council structure, and the change management model that produces adoption.
What the 90-day AI Roadmap Accelerator produces and how use cases are selected
The AI Roadmap Accelerator is designed to solve the two problems that consistently prevent B2B marketing organizations from converting AI potential into revenue: they do not know which AI capabilities to deploy first, and they do not have the governance structure to ensure that the capabilities they deploy are adopted and maintained. Most AI roadmaps are produced by technology vendors describing their feature roadmap, or by consulting firms describing a generic AI maturity model that does not reflect the client's specific RM6 maturity stage, data quality baseline, and existing technology stack. TPG's AI Roadmap Accelerator starts with an AI readiness assessment across five dimensions: AI skills and capability (which team members can use, configure, and govern AI tools?), strategic alignment (which AI use cases are connected to the organization's specific pipeline targets?), data quality and management (which AI capabilities require data foundation work before they can produce reliable outputs?), risk assessment (what governance requirements apply to AI use in this sector and organization?), and technology review (which AI capabilities are already available in the existing stack and not yet utilized?).
Use case selection from the AI Roadmap Accelerator is scored against seven criteria: scope of business impact, time to value, budget requirement, cultural effect, AI skills required, data readiness, and strategic alignment. The highest-scoring use cases are sequenced into a phased implementation plan: Phase 1 (months 1-3) implements the quick wins that build momentum and organizational trust in AI; Phase 2 (months 3-12) delivers ongoing implementation support, benchmarking against market developments, and a comprehensive implementation review. Organizations following this staged AI adoption approach see 40% faster time-to-value and 25% higher user adoption rates compared to organizations that deploy AI without a phased readiness model. The AI Roadmap Accelerator also designs the AI Council structure: the governance body with defined roles, decision-making framework, and meeting cadence that ensures AI deployment remains aligned with revenue objectives as capabilities evolve.
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Section 04
AI Agents and Marketing Automation: Autonomous Revenue Workflows
How TPG designs and deploys AI agents that operate autonomously across marketing, sales, and RevOps workflows — handling the repetitive, high-volume tasks that consume human capacity without producing proportional pipeline.
What AI agents do that traditional marketing automation cannot
Marketing automation executes predefined workflows: if this trigger, then this action, in this sequence. It is deterministic. AI agents operate differently: they assess context, make decisions within defined parameters, and take actions that vary based on the situation rather than a fixed rule set. An AI agent managing lead qualification does not just score leads against a static model — it continuously updates the model based on new conversion data, identifies anomalies in the scoring pattern that suggest the ICP has shifted, and flags those anomalies for human review rather than applying a stale model to current leads. An AI agent managing content personalization does not just select from a fixed set of content variants based on a segment assignment — it evaluates the specific buyer's behavioral history, maps it to their most likely Revenue Loop stage, and selects the content variant most likely to advance them to the next stage based on what has worked for similar buyers in the past. These capabilities require AI, not automation.
TPG designs AI agents for three primary revenue marketing applications: lead and account intelligence agents (continuously updating lead scores, account engagement scores, and propensity models based on new behavioral and firmographic data, and surfacing the accounts most likely to convert at any given moment to the sales team's CRM queue), content and campaign agents (generating, personalizing, and deploying campaign content at the intersection of buyer stage, persona, and engagement history, with A/B testing and performance optimization running continuously without manual intervention), and revenue operations agents (maintaining data quality governance, attribution model accuracy, and pipeline reporting integrity by identifying and resolving data discrepancies, flagging stale opportunity records, and ensuring the CRM reflects the actual state of the pipeline). All AI agent deployments include a human-in-the-loop governance design: defined escalation criteria, exception handling, and regular calibration reviews to ensure the agent's autonomous decisions remain aligned with the revenue objectives they were designed to advance.
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Section 05
Predictive and Generative AI for Revenue Marketing
How TPG deploys predictive models and generative AI capabilities that produce revenue outcomes rather than productivity improvements.
Predictive AI for pipeline: the specific models that connect predictions to sales actions
Predictive AI in revenue marketing encompasses four model types with direct pipeline impact. Lead propensity models score the probability that a specific contact will convert to an opportunity within a defined timeframe, based on behavioral signals, firmographic attributes, and historical conversion patterns — producing a prioritization signal for sales that is more accurate than a static scoring threshold and more actionable than an engagement score alone. Account health models predict churn risk, expansion readiness, and advocacy potential for existing customers, enabling the customer success and expansion teams to allocate their attention toward the accounts most likely to renew, expand, or refer rather than distributing attention uniformly across the customer base. Campaign response models predict which content offers, messaging variants, and delivery channels will produce the highest response rate for each audience segment at each stage of the buyer journey, enabling content investment to be directed toward the combinations that produce pipeline rather than the combinations that produce engagement. And revenue forecasting models apply machine learning to pipeline data, sales cycle patterns, and seasonal factors to produce forecast ranges that are more accurate than manager-submitted probability estimates.
Generative AI in the R.A.I.N. framework is deployed specifically to produce AEO-structured content that advances both the buyer journey and the brand's AI visibility simultaneously. TPG's generative AI content production system uses AI to produce the structural framework and first draft of AEO-compliant content (question-based headers, direct hero answer blocks, FAQ sections) while requiring human expert contribution for the proprietary data points, client-based evidence, and named-author authority signals that make content AI-citable. The human-AI collaboration model produces content at sprint scale (100+ pages per cluster) without sacrificing the quality signals that determine whether AI tools cite the content or ignore it.
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Section 06
AI-Driven Personalization at Scale
How TPG deploys AI personalization across email, web, and campaign channels to deliver relevant experiences to each buyer at each stage — without the manual segmentation overhead that limits scale.
The personalization gap between what B2B marketing claims and what AI-driven programs actually deliver
Most B2B marketing organizations describe their programs as personalized. In practice, personalization means segment-level content selection: all contacts in the "enterprise financial services" segment receive the financial services case study, all contacts at the "VP of Marketing" title receive the marketing leader messaging. This is better than no segmentation, but it is not personalization — it is categorization. True personalization matches the specific message, offer, and content to the specific buyer's behavioral history, stated preferences, and most likely current position in the buying journey. It requires knowing that this specific contact visited the pricing page three times last week, downloaded the competitive comparison guide, and has not responded to the last two nurture emails, and delivering a content experience that addresses those behavioral signals rather than their segment assignment from six months ago. AI makes this match possible at scale: rather than requiring a marketer to manually create and maintain personalization rules for hundreds of behavioral combinations, AI continuously assesses each contact's behavioral pattern, maps it to the most appropriate content variant, and delivers that variant in real time.
TPG deploys AI-driven personalization across three channels as part of AI and Innovation engagements: email personalization (dynamic content blocks that render based on behavioral history, lifecycle stage, and real-time intent signals rather than static list assignments, implemented in the client's existing MAP and connected to the lead scoring model), web personalization (real-time dynamic content on key landing pages, the homepage, and product pages that adapts to the visitor's behavioral history, company, and stage, implemented through the existing CMS or personalization platform), and campaign personalization (AI-driven content selection within campaign programs that continuously tests and optimizes the message-offer-channel combination for each audience segment, replacing the static A/B test with a continuous multi-variate optimization loop). All personalization deployments are measured against pipeline conversion metrics: does personalized email produce better lead-to-MQL conversion than static email? Does personalized web experience produce longer engagement and higher form conversion than generic experience?
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Section 07
AXO: AI Visibility in the Buyer Research Journey
How TPG's AI Experience Optimization framework measures and improves how a B2B brand is represented in AI buyer research tools — the new top-of-funnel where enterprise buying journeys increasingly begin.
Why AI buyer visibility is now a marketing operations requirement, not a content team experiment
Buyers using AI tools to build vendor shortlists are doing so before any human sales interaction. When a VP of Sales asks ChatGPT which revenue marketing agencies serve Fortune 1000 financial services companies, the answer shapes the consideration set. If a brand is not in that answer, they are not on the initial list — and often never recover that position in the deal. This is not a content strategy problem in the traditional sense. It is a marketing operations infrastructure problem: the content program was built for Google rankings, not AI citation, and the gap between what Google values (backlink authority, keyword density, click-through rate) and what AI tools value (direct answers to specific questions, specific data attributed to credible sources, comprehensive topical coverage) is large enough that being excellent at one does not make you visible in the other. TPG's AXO diagnostic measures this visibility gap across six dimensions and four AI platforms (ChatGPT, Perplexity, Gemini, Claude), producing a scored assessment and prioritized content roadmap.
AXO is positioned within TPG's AI and Innovation practice rather than the content team because it is a strategic AI infrastructure investment, not a content execution task. The AXO diagnostic is a measurement tool. The AEO content program that improves the AXO score is an ongoing content operation requiring platform expertise, structured content production at sprint scale, and schema markup implementation. The average AXO score across B2B companies tested by TPG is 28 out of 100. Companies that implement structured AEO programs and run the AXO diagnostic on a quarterly basis typically move from the Low band (sub-30) to the Developing band (50-60) within two to three quarters, and from Developing to Strong (61-80) over four to six quarters of sustained investment. The pipeline impact is measured through AI-referred traffic tracking, citation monitoring across the four primary AI tools, and buyer survey data that identifies AI tools as a research touchpoint in the buying journey.
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Section 08
Data and Decision Intelligence: The Foundation AI Requires
How TPG builds the data infrastructure, attribution architecture, and decision intelligence layer that AI models require to produce reliable, actionable outputs.
Why AI without data quality produces confident wrong answers
AI models are as reliable as the data they are trained or tuned on. A predictive lead scoring model trained on a database with a 40% duplicate rate will produce scores that reflect duplicate inflation rather than actual engagement patterns. An attribution model built on top of a MAP-CRM integration with sync failures will produce attribution numbers that are inconsistent and not credible to the CFO. An AI personalization engine drawing on lifecycle stage data that has not been governed — where contacts are stuck at their original stage indefinitely, with no automation maintaining consistent stage progression — will personalize for the wrong stage. Data quality is not a prerequisite that gets addressed before the AI program starts: it is a parallel workstream that must be maintained continuously as the AI program scales, because the AI models will continuously surface the data quality gaps that limit their accuracy.
TPG's data and decision intelligence practice covers the foundational layer that AI deployments require: data quality governance (deduplication standards, field completion rate monitoring, lifecycle stage automation, and the monthly hygiene operations that prevent database decay from degrading AI model outputs), attribution architecture (the MAP-CRM integration configuration that ensures marketing activity creates the campaign member records and deal touch records that AI attribution models require to produce accurate pipeline influence calculations), and decision intelligence infrastructure (the reporting frameworks, dashboard design, and data model documentation that translate AI model outputs into the executive-level business intelligence that CMOs and CFOs can act on). As a practical example: a predictive lead scoring AI model is only as useful as the sales team's ability to understand what the score means and trust that it reflects real buying intent. TPG's data and decision intelligence work includes the model explainability and stakeholder communication layer — making sure that when the AI says "route this account to enterprise sales immediately," the sales representative understands why and acts on it rather than ignoring the signal.
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Section 09
AI Governance and the AI Council Model
How TPG designs the AI governance structure that keeps AI deployments aligned with revenue objectives, compliant with risk requirements, and adopted by the teams responsible for acting on AI outputs.
Why AI programs without governance structures produce adoption failure, not just compliance risk
AI governance is discussed primarily in the context of risk: how do we prevent the AI from producing biased outputs, confidential data leaks, or regulatory violations? These are real concerns. But the more common governance failure in B2B marketing AI programs is adoption failure: the AI capability was deployed, the accuracy metrics were reasonable, and the sales and marketing teams stopped using it within six months because the outputs felt unreliable, the model was never explained to the people using it, and there was no defined process for raising concerns or requesting recalibrations. The AI Council model addresses this adoption failure directly by creating a governance body with the organizational authority to set AI deployment priorities, approve use cases, review model outputs quarterly, and adjudicate concerns from the teams whose workflows the AI is intended to augment. Without this governance structure, AI programs operate as technology deployments rather than organizational changes — and most technology deployments that do not receive sustained executive sponsorship and change management attention are quietly abandoned.
TPG's AI Council design as part of the AI Roadmap Accelerator includes: council composition (cross-functional representation from marketing, sales, IT, legal, and finance with defined roles and decision-making authority), meeting cadence and agenda structure (quarterly model reviews, monthly use case pipeline reviews, and ad-hoc escalation handling for model concerns), a use case governance framework (the scoring criteria that determine which AI initiatives are approved, how they are sequenced, and what success metrics are required before scaling), change management integration (the communication and training program that ensures the teams whose workflows are augmented by AI understand the outputs and trust the process), and risk monitoring standards (the specific data privacy, security, and bias monitoring requirements that apply to each AI deployment type). An AI Council that meets too infrequently becomes ceremonial. One that meets too frequently becomes a bottleneck. TPG's design defines the meeting structure based on the specific AI capabilities in the deployment roadmap rather than applying a generic governance calendar.
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Section 10
How to Engage TPG for AI Strategy and Innovation
The four AI engagement models TPG offers — and how the AI readiness assessment determines which is the right starting point.
The right starting point for AI engagement depends on where the organization is, not where it wants to be
The most expensive AI mistake is building AI capabilities on a foundation that cannot support them. Organizations that invest in AI lead scoring before their data quality is stable will build a scoring model that produces wrong outputs. Organizations that invest in AI content production before their AEO strategy is defined will produce more content that AI tools cannot cite. Organizations that deploy AI agents before their MAP-CRM integration is functioning correctly will build autonomous workflows that amplify the integration problems. The starting point for any TPG AI engagement is the AI readiness assessment, which scores the organization across five dimensions — AI skills, strategic alignment, data quality, risk posture, and technology utilization — and identifies which AI investments are ready to deploy now versus which require foundational work before AI deployment makes sense. This assessment is available as a standalone tool at pedowitzgroup.com/ai-assessment before any consulting engagement begins.
TPG offers four AI engagement models based on readiness assessment findings and organizational objectives: the AI Roadmap Accelerator (90-day program producing a prioritized roadmap and AI Council structure for organizations that are ready to invest but need strategic direction), AI capability implementation (point-solution deployment for organizations that have a defined AI use case and need platform expertise and change management to execute), AI managed services (ongoing AI program operation and optimization for organizations that have a roadmap and need execution support), and integrated AI within the RM6 transformation (AI deployment embedded within a full revenue marketing transformation engagement for organizations whose primary constraint is at the strategic or operational level rather than the AI capability level). As with every TPG engagement, the AI work is backed by the guarantee: if unsatisfactory, TPG does the work again at no charge; if still unsatisfied, the client does not pay.
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"We went from a very traditional marketing organization to a digital marketing team running an incredibly successful nine-touch nurture campaign. The immediate result was $1.1 billion in asset value contributed to the sales pipeline."Paige LubwaySr. Manager, Demand Generation, Charles Schwab
AI and Innovation: Frequently Asked Questions
Direct answers to the most common questions about TPG's AI services, the R.A.I.N. framework, the AI Roadmap Accelerator, and how AI connects to revenue outcomes.
What is the R.A.I.N. framework?
R.A.I.N. stands for Revenue Artificial Intelligence Network. It is TPG's proprietary framework for deploying AI, machine learning, and big data across the revenue marketing function to produce measurable pipeline and revenue outcomes.
R.A.I.N. stands for: Revenue (revenue-aligned goals that tie every AI investment to measurable business outcomes), Artificial (AI and machine learning applied across the revenue motion), Intelligence (data-driven decision intelligence that powers the entire framework), and Network (the integrated ecosystem of technology, people, and processes that delivers revenue outcomes at scale). It is the AI delivery layer of TPG's RM6 Revenue Marketing Operating System.
What is the AI Roadmap Accelerator and what does it deliver?
The AI Roadmap Accelerator is TPG's 90-day consulting program that produces a prioritized, executable AI roadmap. Phase 1 (months 1-3) covers AI readiness assessment across five dimensions, use case identification scored by revenue impact and feasibility, and quick win implementation. Phase 2 (months 3-12) delivers ongoing implementation support and comprehensive review.
The program uses the R.A.I.N. framework and includes AI Council design and change management. Organizations following this staged approach see 40% faster time-to-value and 25% higher user adoption rates compared to unsupported AI deployments.
Why is AI adoption a revenue operations question?
AI productivity focuses on automating existing workflows to reduce time or cost. AI adoption in the revenue marketing context is a revenue operations question: which AI deployments produce pipeline impact? A content team that deploys AI to produce more blog posts faster has solved a productivity problem, not a pipeline problem, unless those posts are structured for AI citation and buyer engagement.
Under 1 in 5 B2B companies have achieved Revenue Marketing maturity with AI integrated into their revenue motion. Most are improving productivity metrics without connecting to pipeline. TPG frames every AI engagement around the pipeline question first.
What AI services does TPG offer?
TPG's AI services span: AI strategy (R.A.I.N. framework, 90-day AI Roadmap Accelerator, AI Council design, emerging innovations), AI systems and automation (AI agents for marketing and sales, marketing operations automation, AI for financial services), AI intelligence and personalization (predictive and generative AI, AI-driven personalization at scale, data and decision intelligence), and AXO (AI buyer visibility diagnostic and AEO content programs).
As a member of HubSpot's AI Partner Advisory Board, TPG has early access to HubSpot AI feature development and connects platform-level AI capabilities to revenue marketing outcomes.
What is the MAKE IT approach?
The MAKE IT approach is TPG's implementation methodology that complements the R.A.I.N. framework. It facilitates data Measurement, Analysis, Knowledge, Execution, Integration, and Testing to drive comprehensive revenue optimization and continual performance tuning.
MAKE IT is the operational framework that ensures AI deployments are not just implemented but continuously optimized against revenue outcomes. The combination of R.A.I.N. (strategic framework) and MAKE IT (operational methodology) is what separates AI programs that sustain results from AI programs that degrade over time.
How does TPG connect AI to pipeline attribution?
Every AI capability TPG deploys is connected to a defined pipeline contribution metric before deployment. For predictive AI: does the lead scoring model improve MQL-to-opportunity conversion rates? For AI personalization: does personalized email produce better lead-to-MQL conversion than static email? For AXO: are AI-referred traffic visitors converting at higher rates than other channels?
TPG also builds the data and attribution infrastructure (MAP-CRM integration health, campaign influence configuration, AI-referred traffic tracking) that makes these pipeline connections measurable in the client's existing reporting environment.
What results have TPG clients achieved with AI and revenue marketing transformation?
Paige Lubway, Sr. Manager of Demand Generation at Charles Schwab, reported that TPG's engagement moved the organization from a traditional marketing model to a digital team running a nine-touch nurture campaign that contributed $1.1 billion in asset value to the sales pipeline. TPG has generated more than $25 billion in marketing-sourced revenue for 1,500+ clients since 2007.
The AI-specific outcome data includes: 40% faster time-to-value with staged AI adoption, 25% higher user adoption rates with phased implementation versus unsupported deployment, and 700% traffic increase in 4 weeks on TPG's own site from AEO program implementation.
Turn AI Potential into Revenue. Start with the Readiness Assessment.
87% of CMOs feel intense pressure to prove marketing ROI. AI is the highest-leverage investment available — when deployed against the right workflows, in the right sequence, with the right data foundation. The AI readiness assessment takes 15 minutes and shows you exactly where to start.