Is Your Go-to-Market Built for Today or Tomorrow?

Bain & Company recently posed a question that should give mid-market PE investors and CEOs pause:

“Is your go-to-market strategy built for future opportunities—or is it still calibrated to yesterday’s market?” Bain

For mid-market Technology companies, the answer determines whether growth compounds or stalls. Buyer behavior has rewired: AI is now board-level spend, cybersecurity is non-negotiable, and B2B buyers increasingly prefer to buy without ever speaking to a sales rep.

Here’s what a future-ready GTM looks like and how PE leaders can reshape portfolio companies to capture growth in this next cycle.

The Market has moved: Three Forces Reshaping GTM

1. Digital-first buying. Gartner reports 75% of B2B buyers prefer a rep-free experience, and when they do engage, just 5–6% of buying time is with a rep. Winning is less about the perfect pitch and more about building digital journeys that allow buyers to self-decide in your favor.
   
   
2. AI as budget gravity. IDC forecasts $307B in AI spending in 2025. Buyers expect AI embedded in workflows now, not promised for later releases.
   
   
3. Cybersecurity at the core. With worldwide spend hitting ~$213B in 2025, security has shifted from a compliance checkbox to a purchase driver. Deals stall or accelerate based on security readiness.

Bottom line: yesterday’s GTM optimized coverage and MQL volume; tomorrow’s GTM optimizes the full self-serve-to-human continuum, quantifies risk/ROI in the language of AI, security, and compliance, and treats post-sale outcomes as growth fuel. These shifts are the new imperatives for winning future opportunities.

Building a Future-Ready GTM

1. Redesign the sales journey

• Build proof, not promises, using free trials, ROI calculators, and sandboxes.
• Create outcome-first content tied to board themes: AI efficiency, cyber resiliency, regulatory readiness.
• Use signal-based intervention: let sales engage when buyer intent spikes.

Better KPIs: time-to-first-value, % of opportunities from self-serve, regret-rate (post-purchase escalation).

2. Make AI tangible

• Deliver one or two high-leverage copilots (1) with measurable productivity or risk reduction.
• Govern by design: publish model cards, (2) data boundaries, and fallbacks to reduce InfoSec review times.
• Price to adoption: implement usage-based pricing that scales with value realized. 

Better KPIs: AI attach rates, $ impact per account, time-to-security approval.

3. Lead with security and compliance

• Map features to frameworks like SOC2, HITRUST, and FedRAMP.
• Provide pre-completed security questionnaires and API-level evidence dashboards.
• Monetize assurance with “compliance accelerator” bundles that shorten procurement cycles.

Better KPIs: time-to-green on reviews, % of deals requiring no additional discovery, assurance upsell rates.

4. Specialize your routes to market

• Pick three micro-verticals where your proof is strongest.
• Go ecosystem-first: embed in AWS, Azure, and ServiceNow marketplaces.
• Build partner-attached plays that speed trust and procurement.

Better KPIs: revenue mix by vertical, marketplace-sourced pipeline, deal velocity vs. direct.

5. Make Customer Success the growth engine

• Commit to 3–5 outcomes per segment (e.g., cut MTTR by 40%).
• Use telemetry to surface expansion signals. Examples include leadership changes, new investments, etc.
• Codify wins into peer advocacy, since buyers increasingly trust references over sales. 

Better KPIs: Net revenue retention (NRR), time-to-value, verified, quantified customer outcomes.

The PE-Grade Modernization Plan

Phase 1 (0–90 days): Diagnose & de-risk

• Analyze the last 10 deals won/lost for journey gaps and veto points. Gartner
• Initiate sprints on the top 3 security gaps to resolve.
• Refresh content with ROI calculators, reference architectures, proof assets.

Phase 2 (Quarter 2–3): Rewire around buyer reality

• Stand up a digital revenue team for product-qualified pipeline.
• Build 3 complete micro-vertical plays.
• Package assurance add-ons to accelerate approvals.

Phase 3 (Quarter 4+): Compound & differentiate

• Productionize AI copilots with measurable outcomes.
• Execute co-sell programs with hyperscalers, such as resellers and companies offering complementary products/services.
• Pilot value-indexed pricing aligned to customer impact. Monevate

What PE Investors Should Ask

• How well are buyers able to evaluate the product without a salesperson?
• What’s the median security review cycle time?
• Where is AI producing measurable outcomes (not just features)?
• Are acquisition costs lower if field-assisted ?
• How directly does the product tie to growth budgets like AI and security?

What “Good” Looks Like at Exit

• 40%+ of pipeline from product-qualified or marketplace sources.
• Security review cycles <20 days.
• AI feature attach >50% in core segments with case studies.
• NRR >115–120%, expansion tied to outcomes not discounting.

The Call to Action

The era of easy growth is over. Technology companies can no longer rely on market momentum alone. Winning requires a go-to-market engine that mirrors how buyers actually buy—digital-first, AI-enabled, security-proofed, and outcome-driven.

For PE investors, that’s not optional; it’s the difference between average returns and premium exit multiples.

About the Author

John Auer is a Managing Partner with Veritac Group. Having worked with over 300 companies, John brings deep expertise in B2B revenue design. A former PE Managing Director and CRO, he quickly identifies Go-To-Market gaps and designs solutions that drive measurable enterprise value. John has helped firms such as Symphony Technology Group, HIG Growth, and Riverside. 

About Veritac Group: We are a mid-market, B2B, GTM consulting firm with deep experience in technology and healthcare. We create scalable, effective processes and advise clients on a wide variety of GTM solutions (designing for the future being one element). John Auer is a Partner at Veritac Group with over 25 years of sales, consulting, and Private Equity experience.

Footnotes
(1)An AI-powered assistant or augmentation tool that works alongside a human user to make them faster, more effective, and more accurate.

(2) A model card is a structured “report card” for an AI model that explains what it does, how it performs, where it works well, and where it doesn’t — to promote responsible, transparent use.