GTM AI: A New Approach for Getting Rid of GTM Bloat

Rising customer acquisition costs, decreasing customer lifetime value, lowered forecasts, down rounds, depressed valuations. The last couple of years for go-to-market teams have been nothing short of brutal. 

And something’s gotta give – these trends pose an existential threat.

Employees, leadership, and investors are desperate for a better way. The old mantra of “what got us here won’t get us there” has never been more true. 

So what did get us here? And how will GTM leaders change the game and control their destiny in 2024 and beyond? 

What is GTM AI?

GTM AI, or Go-to-Market Artificial Intelligence, is a strategy that infuses AI across the entire go-to-market engine to streamline processes, gain valuable insights, and empower sales, marketing, and customer success teams.

Traditionally, go-to-market teams have become increasingly siloed and disconnected, leading to inconsistencies in messaging, targeting, and execution. GTM AI aims to break down these silos by connecting all the pieces of the go-to-market puzzle. With the right GTM AI platform that can ingest and analyze data from various sources, these solutions enable teams to gain real-time insights into target accounts, personalize outreach at scale, and codify best practices into repeatable workflows.

The role of every go-to-market function is important in implementing a successful GTM AI strategy. As the connective tissue between sales, marketing, and customer success, rev ops can identify opportunities to leverage AI for productivity gains, such as automating account research or generating customized content.

By demonstrating the time savings and efficiency improvements of AI-powered processes, go-to-market leaders can secure buy-in from executives and ensure a cohesive approach to AI adoption across the organization.

GTM Bloat: The Biggest Problem, Hiding in Plain Sight 

GTM Bloat is the accumulation of technology, resources, and cumbersome processes across the go-to-market engine. 

Leaders can see it in their metrics and tech spend. They can feel it when they try to get things done. Their GTM is characterized by inefficiency, sprawl, and disjointedness.

The pressure is assuredly on.

“Do more with less” is a common directive, without a roadmap. It’s clear that the status quo needs disruption, that cracking the whip harder won’t yield better results.

This is the promise of AI. We’ll explore that promise in a moment. But first, let’s unpack the magnitude of GTM Bloat.

The Inefficiency of App Sprawl

Over the years, companies have poured wild amounts of dollars into adopting new GTM tools and building processes around those tools.

While the intentions were good, the promised value of many of those tools was never fully realized. Tools that were supposed to be generating huge ROI ended up collecting dust or sitting in siloes. 

Point solutions got adopted by individuals or teams with little thought of enterprise-wide integration.

These siloed tools don't seamlessly share data, and create disjointed workflows and competing sources of truth. Then teams waste countless hours passing data and leads between platforms, arguing about whose version of the truth is the right one.

This fragmented architecture frequently causes breakdowns in processes when data gaps appear which means legacy tech investments are now technical debt, creating drag on productivity.

Years of siloed tech decisions have led to a situation where the tech stack lacks cohesion and agility.

Too often, the tools dictate suboptimal workflows instead of enabling streamlined operations.

Bloated spend, bloated processes. 

The Unintended Consequences of Overspecialization

In an effort to produce clear swimlanes and clear R&R, go-to-market teams have become hyper-specialized and, in many cases, hyper-siloed.

Further, this specialization was more affordable in a zero interest rate environment. My, how the times have changed.

While some division of labor is necessary, excessive specialization creates friction. The baton gets passed too frequently between hyper-focused specialists in the race to revenue. 

With each handoff, momentum slows. Context gets lost. Follow-ups fall through the cracks. Meanwhile, customers get frustrated by the fragmented experience.

The traditional go-to-market model depends on these specializations working seamlessly together. But in reality, misalignment and lack of transparency between silos creates slow, disjointed processes.

By breaking down rigid silos, GTM teams can tap into their full potential.

Aligning around core priorities rather than functional domains allows them to operate more dynamically. With artificial intelligence (AI) taking care of the busy work, they can focus on big-picture strategy and execution. Operating more horizontally while still maintaining depth of expertise.

Solving GTM Bloat requires increased agility, transparency, and cross-functional collaboration.

As specializations blur, end-to-end ownership of the customer experience becomes possible. Streamlining those handoffs ultimately translates to shorter sales cycles, higher conversion rates, and accelerated revenue growth.

When GTM Bloat Gets Baked into Strategy

Despite technological advancements, most companies still operate with a high level of inefficiency intentionally built into their go-to-market strategies.

This status quo of tolerated inefficiency manifests in several key ways:

• Sales quotas often have 20%+ buffers over company revenue plans to account for expected underperformance. Rather than optimizing processes to enable reps to achieve quota regularly, underperformance is budgeted for.
• Studies show sales reps spend only a third of their time selling. The rest is wasted manually updating CRM records, researching leads, scheduling meetings, and other non-selling tasks.
• Marketing and sales teams accept the handoff of leads as a slow, inefficient process. Lead response times stretch to days or weeks instead of minutes or hours. Follow-ups fall through the cracks. Marketing teams plan for a 1-2% conversion rate of lead to opportunity, relying on sellers to wade through a sea of unqualified leads.
• Companies budget for high customer acquisition costs and low marketing ROI without questioning the underlying processes. The excessive headcount, complex tech stacks, and layers of management to support inefficient GTM systems are considered normal.

In short, inefficiency gets designed into the system.

Companies accept and plan for a high level of friction, delay, and waste. They work around problems instead of solving them at the root.

But this tolerance of inefficiency as the status quo is no longer sustainable or competitive.

The High Cost of GTM Bloat

Companies are pouring more and more resources into their go-to-market strategies, yet seeing diminishing returns. Customer acquisition costs continue to rise exponentially while customer lifetime value is on the decline.

The traditional go-to-market playbook is no longer effective.

Over the past decade, sales and marketing teams have scaled up headcount, budget, and tech stacks. But the results haven't kept pace with the exponential increase in spending. It now takes more money than ever to acquire each new customer.

At the same time, new customers aren't sticking around as long or spending as much over their lifetime.

The go-to-market model is broken, yet companies continue doubling down on the same old tactics.

More content, more cold calls, more trade show booths, more PPC ads. All it does is drive customer acquisition costs even higher. Lifetime value keeps dropping lower.

Companies are trapped in an endless cycle of diminishing returns. The only way to reverse the trend is to completely rethink their approach to go-to-market. They can't just invest more into a broken model. It requires a fundamental shift, not incremental improvements.

In other words, the status quo is no longer an option.

From GTM Bloat, To GTM Velocity 

The rise of AI technology enables companies to put an end to GTM Bloat and achieve real, sustainable GTM Velocity. GTM velocity is an environment where GTM members can act with speed, confidence, and creativity.

Friction is significantly reduced or outright eliminated, and lean teams run with agility to drive results and business value.

AI unlocks efficiencies in other redundant tasks like data entry, meeting scheduling, and reporting. As these routine processes get automated, teams spend less time on busywork and more time on high-impact activities.

But collaboration improves when AI removes friction from workflows. With AI eliminating bottlenecks, there is greater transparency into deal progress and team performance. Data is centralized instead of living in silos, providing full visibility across the revenue engine.

This means that leaders have access to real-time reporting for smarter decision-making, a key value proposition of AI technology.

When redundancy is removed and resources are optimized, go-to-market teams reach their full potential.

AI allows companies to reimagine what's possible.

Let’s take the seemingly simple example of lead scoring, processing, and qualification. Often, salespeople don’t put any stock in the lead scores provided to them, having learned to distrust the black box judgment of whatever system provides the scoring. 

That changes with AI, as the rationale for the lead score can be stored on the lead record and surfaced to the reps. Operations teams qualitatively define their lead scoring or tiering criteria, and the AI does the lead and account research to assess the fit. 

This means that only the best leads will achieve a high score, sellers will understand and trust the lead score rationale, the right leads will get prioritized and worked. AI does the screening instead of wasting precious seller time.

And by reducing the grunt work in lead qualification and assignment, AI frees up reps to have higher-quality conversations with prospects. Reps can focus more on guiding leads down the funnel rather than just processing them.

Conversations become more strategic and consultative, leveraging AI to surface relevant information.

Accelerating Revenue Growth with GTM Velocity

GTM Velocity, enabled by AI, tools accelerates revenue growth through streamlined processes, increased productivity, and higher conversion rates.

With redundant tasks automated, teams are freed from administrative bottlenecks to focus on high-impact initiatives. Lead response times drop dramatically when manual handoffs are eliminated, and personalized engagement powered by data insights helps connect prospects with the right solutions faster.

The improvements cascade down the funnel, compounding revenue growth.

When you leverage AI for your go-to-market strategy, the efficiencies gained create a flywheel effect on revenue growth. Companies not only see an immediate lift but also compounding benefits over time as velocities increase.

The Path to GTM Velocity

Achieving GTM Velocity requires methodically optimizing processes. Companies and GTM professionals should take the following steps:

1. Assess Current Tech Stack and Processes

The first step is auditing your current tech stack and go-to-market processes.

Look for redundant tools, fragmented data, and steps that add little value. Identify constraints and bottlenecks that slow things down.

Document workflows and inventory pain points reported by team members. This assessment illuminates opportunities for improvement.

2. Identify Optimization Opportunities

With a clear view of the current state, start brainstorming ways to enhance processes.

Look for ways to eliminate redundant steps, automate manual tasks, consolidate apps, improve handoffs, and streamline workflows.

Focus on changes that will have an outsized impact by saving significant time or resources. Think about how to move prospect interactions along faster.

3. Implement Your GTM AI Solution

One high-impact way to optimize go-to-market is implementing an AI solution purpose-built for GTM.

Look for a platform that merges siloed data, predicts optimal interactions, and automates repetitive tasks.

And importantly, make sure that your AI solution supports the entire GTM team and not just an individual or small team. Adopting a litany of AI-powered point solutions over time may cause the same GTM Bloat that exists today. 

4. Continually Refine Strategy

GTM optimization is an ongoing pursuit. Monitor results and fine-tune strategies to maximize outcomes.

Look to expand the use of AI over time to amplify its impact. Set key metrics and track them.

Keep an eye out for new tech tools that could add value. Optimization is a continuous process of incremental improvements that compound gains.

With GTM AI setting the pace, marketing and sales reach new heights of productivity, fully harnessing the capabilities of generative AI, machine learning, and tools offered by leading AI companies.

What Makes Copy.ai the Best GTM AI Platform?

Copy.ai Workflows eliminates GTM Bloat by unifying disparate pieces of the sales process into seamless, end-to-end experiences.

Unlike the fragmented tech stacks that dominate the industry, Copy.ai is a comprehensive platform that bridges the gaps between sales, marketing, customer success, finance, and operations, ensuring that GTM teams can execute strategies with enhanced cooperation and clarity.

Time-Saving Automation at Scale

Copy.ai goes beyond the capabilities of typical AI chat tools with its ability to automate multi-step processes at scale, freeing GTM teams from the quagmire of grunt work.

Redefining efficiency, it powers every aspect of your go-to-market, without complex integrations, technical modifications, or cumbersome change management.

Personalization and Flexibility

The intuitive process of building with Copy.ai—simply by describing the desired process in plain English—launches GTM teams into a world of unmatched personalization and flexibility.

Campaigns and follow-ups can be scaled and tailored, ensuring that every lead feels individually catered to, thus shortening sales cycles and enhancing conversion rates, a key measure of GTM velocity.

Data-Driven Decision Making

With its text-based analytical prowess, Copy.ai Workflows compiles and crunches data across all stages of the sales funnel, offering actionable insights and predictive analytics that drive strategic decision-making.

Teams can now pivot from making hunch-based decisions to leveraging data-driven insights that maximize ROI.

Eliminating Technical Debt

Copy.ai addresses the legacy of technical debt accumulated with piecemeal solutions.

Its ability to run multiple tasks simultaneously and natively means that go-to-market teams can avoid the common pitfalls of non-integrated tools and sidestep the productivity drag caused by legacy investments.

Alignment with GTM Objectives

The platform also excels in aligning sales insights with marketing objectives.

It can directly feed into marketing copy for landing pages, ensuring that marketing efforts are consistently optimized to lead quality—a synchronous dance between lead generation and content marketing that few platforms can facilitate with such finesse.

It then ensures that the right leads, context, and content are passed to the sales team. This creates a consistent experience for the customer and optimizes your funnel conversion rates.

Streamlined Customer Journeys

Finally, Copy.ai enables a smoother customer journey.

From first contact to post-sale engagement, the AI-driven workflows ensure that communications are personalized and timely, drastically improving the customer experience and fostering loyalty.

Adopting Copy.ai is more than just embracing a new platform; it's a strategic shift towards an integrated, data-driven, and customer-centric GTM approach.

It's not incremental; it's transformative.

This is a resource where you can come and learn all the different ways Copy.ai  helps you realize GTM Velocity.