Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I am a sales leader for complex IT services who builds the opportunities that matter. I have led service sales across regulated sectors, coached EMEA teams, and taken deals from 5-digit projects to 9-figure contracts.

My approach is outcome-driven, client-centric, and rooted in simplifying complexity into KPIs, SLAs, scope, and negotiation that deliver business impact.

Documented results include +35% average annual service growth, 80%-win rate on RFx, and strategic pursuits up to €160+M, with deep practice in DaaS, workplace, managed services, cloud and cybersecurity.

Q2. What is the estimated total addressable market (TAM) for AI-driven sales solutions and GTM platforms, and how is it segmented by enterprise size or industry vertical?

Public estimates put AI-for-sales and GTM software in the mid-tens of billions globally in 2025, roughly 25 to 35 Bn USD, growing at a high-teens CAGR. This roll-up aligns with mainstream category sizes, sales engagement ~$9.6B 2024; sales enablement ~$5.23B 2024; sales intelligence ~$4.85B 2025; CPQ approaching $5B by 2026; price optimization ~$1.4B 2025.

By enterprise size
Enterprise and upper-midmarket drive most spend due to data volumes, governance, and integration needs. Enablement and revenue intelligence skew enterprise first; SMB adoption is rising through bundled GTM suites. 

By vertical
Highest near-term penetration: Software, fintech, banking, then telecom, manufacturing, healthcare; these show stronger AI leadership and willingness to fund RevOps. 

Q3. Which sectors or industry segments have experienced the highest adoption rates of AI-powered sales and GTM tools in the past three years, and what drives this acceleration?

Software and fintech, banking: Fastest adoption, driven by data maturity, digital customer journeys, and measurable RevOps ROI. 

Manufacturing and telecom: Acceleration from AI-assisted forecasting, pricing, and channel orchestration. 

Healthcare and regulated services: Growing use for compliant outreach and field enablement, with strong governance requirements. 

Cross-industry adoption in marketing and sales more than doubled from 2023 to 2024, making sales the GenAI beachhead inside many firms.

Q4. Which recent innovations have led to measurable uplifts in conversion rates, sales velocity, or average deal size across industries?

Conversation and revenue intelligence

Documented lifts in win rates and forecasting accuracy when call AI and guided execution are embedded in daily workflows. Examples:

Case study: +16 percent win rate, +30 percent revenue per rep, 95 percent forecast accuracy after deploying call AI and revenue intelligence. (Gong) 
Independent analysis reports +8 percent average win-rate improvement and +38 percent seller capacity with conversation intelligence at scale. (Nucleus Research)

Sales engagement with AI scoring and sequencing

Users report ~26 percent boost in win rates by focusing reps on the right opportunities and tightening execution. (Outreach)

Targeted, AI-assisted personalization: incremental +1 to +2 percent sales and +1 to +3 percent margin through precise offer targeting at scale in retail, a pattern now mirrored in B2B ABM motions. (McKinsey & Company)

AI inside CRM

Teams using AI are 1.3x more likely to report revenue growth year over year, highlighting the impact on data quality, personalization, and productivity. (Salesforce)

Q5. What organizational strengths and capabilities distinguish the top performers in AI-enabled sales transformation, and how do these translate into competitive business outcomes?

Strengths that matter, and how they show up in outcomes:

Data foundation and RevOps discipline

Clean CRM plus telemetry across calls, emails, and product usage

Outcome: Reliable pipeline health and forecast accuracy. (Gartner)

Unified enablement and GTM execution

One place for content, training, coaching, and plays

Outcome: Faster ramp, higher win rates. (Highspot)

Pricing and deal-desk intelligence

CPQ, and price-optimization aligned to value

Outcome: Higher average deal value with fewer concessions. (360iResearch/Technavio)

Governance and compliance for AI

Clear policies, risk controls, and model lifecycle

Outcome: Scale without regulatory surprises. (Gartner)

Contracting clarity and service culture

Scopes, SLAs, and change-control aligned to outcomes

Outcome: Smoother negotiations, fewer escalations, measurable delivery. 

Q6. How are buyer preferences for self-service and hyper-personalized experiences shaping the development of AI-assisted sales enablement tools?

B2B buyers increasingly want to self-educate, compare, and progress without a rep, while still valuing expert guidance at critical moments. Implications:

Rep-optional buyer journeys

Build digital product rooms, interactive ROI models, click-to-try, and transparent pricing where feasible; Forrester notes self-service now spans all buying stages, with a strong buyer preference for rep-free experiences. (Forrester / Gartner)

Hybrid engagement by design

AI routes the correct next action, switching from self-serve to expert assist at decision risk points, reducing buyer remorse seen in pure self-service paths. (Gartner)

Bigger online deal sizes

More buyers are willing to spend $500K or more via self-service or remote interactions, which raises the bar for digital content, proof, and security.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

If I were an investor, the one critical question I would ask:

“Show me the durable unit-economics lift your platform creates for a sales leader, per 1 dollar invested, at 6 and 12 months, without your professional services.”

Then break it down by driver, win rate, sales cycle, average deal value, cost-to-serve, with third party-verified cohorts across at least three verticals and a clear path to gross margin resilience as inference costs and governance requirements rise. This is where conviction beats storytelling.

Of course, in the current macroeconomic context, this is a very challenging exercise that everyone should still try to run. I wouldn’t be expecting any commitment, though, at this point, but the pretender that would sound the most reasonable would certainly get my vote. Hopefully, this type of exercise may be simpler in 2030.