The AI Implementation Reality Check

⏱️ Your Morning Brief (TL;DR)

Welcome back!

This week, we're diving into fresh research from Teneo (the global CEO advisory firm) that surveyed 300+ software vendors about their AI strategies.

Teneo published four playbooks on how AI is reshaping the software landscape and how incumbents can adapt their strategies and operations to stay competitive.

In today’s brief

• A breakdown of Teneo’s AI playbooks on Customer Service, Product, GTM, and Pricing Strategy

• 5 AI related reads

Let’s dive in 🤖

 💡 Turning Services and Customer Success into AI-Driven Growth Engines

The GPTLDR: Professional Services isn't just a cost center anymore – it's becoming the primary vehicle for AI adoption and expansion. Companies treating PS strategically are seeing 31-40% gross margins while driving software outcomes that matter.

The Details:

• The margin spread is massive: 43% of firms achieve 31-40% margins on services, while 26% operate at 11-20% or worse

• AI is reshaping the services catalog: Winners are creating new offerings like "data validation" and "accelerated diagnostics" that align with AI product releases

• Outcome-based pricing is replacing T&M: Smart vendors are moving from hourly billing to subscription packages tied to customer ROI

• CS is going proactive: Only 56% of companies use AI/ML for health scoring and churn prediction – those who do report "marked improvements" in preventing downsell

• RevOps is the glue: Integrated commercial functions powered by Revenue Operations are now table stakes for AI success

 💡 Pricing Optimization in the AI Era

The GPTLDR: Stop creating separate "AI tiers" – 56% of companies are embedding AI into core products and raising prices instead. The winners are those who price based on value delivered, not infrastructure costs.

The Details:

• The market is split: 56% embed AI in core products (with 34% raising prices), while 44% sell separately

• Cost-based pricing is a trap: LLM prices have fallen 97% in 18 months – if you're pricing on COGS, you're doing it wrong

• Usage-based pricing often backfires: The better your AI, the less it needs to be used – weak correlation with value

• Hybrid models are emerging: Outcome-based pricing with subscription floors protects revenue while aligning incentives

• Fair-usage clauses protect margins: Cap outliers without penalizing typical adoption patterns

Why It's Important:

1. Creating AI/non-AI versions fragments your product strategy and confuses customers

2. Customers pay for business value, not your OpenAI bills

3. Simple price uplifts often beat complex usage models for capturing AI value

 💡 How AI Offerings Are Reshaping Sales and Marketing Models

The GPTLDR: AI isn't just changing what you sell – it's fundamentally altering how you sell it. Sales cycles are lengthening (45% report 10%+ increases), but AI-powered productivity tools are helping sellers spend more time on high-value activities.

The Details:

• Time allocation is shifting dramatically: Post-AI adoption, customer engagement jumps from 25% to 45% of seller time, while admin drops from 35% to 20%

• New roles are emerging: AI Sales Strategists and AI Product Evangelists are bridging the gap between technical depth and business value

• The buyer journey is non-linear: Multiple touchpoints, education requirements, and trust-building are extending cycles but increasing deal sizes

• Predictive lead scoring actually works: ML models analyzing conversion patterns help sellers focus on winnable deals

• Sales compensation needs rethinking: Traditional comp plans don't align with longer, consultative AI sales cycles

Why It's Important:

1. If your sales team isn't using AI tools internally, they're likely spending 35% of their time on admin instead of selling

2. Companies maintaining GTM silos are delivering inconsistent messages and losing deals to integrated competitors

3. Without dedicated AI enablement resources, end-users never realize full value – killing expansion opportunities

 💡 Building AI Features Around Data, Not Just Functionality

The GPTLDR: Your proprietary data and API strategy matters more than feature count. 83% of software vendors have launched AI features, but winners are those treating data as their moat and monetization as a day-one priority.

The Details:

• Monetization timing matters: High performers are 30% more likely to define pricing during product development, not after launch

• Speed isn't everything: Rushing AI features without product discipline leads to buyer frustration

• Data is the real moat: Vertical players are beating horizontal competitors through proprietary workflow data

• Platforms beat point solutions: AI-enabled platforms see 25% better performance than standalone tools

• API strategy is critical: Unrestricted access can let competitors replicate your core value prop

Why It's Important:

1. "Launch now, monetize later" is officially dead – it sets low value expectations you can't escape

2. Without unique data advantages, competitors quickly match capabilities and undercut price

3. Isolated AI modules miss network effects that create real stickiness

📚 Interesting Reads

• 2025 Mid-Year LLM Market Update - OpenAI's enterprise lead is shrinking as Anthropic and others gain ground with better pricing and features

• Why Millions Of Managers Are Becoming Obsolete. It’s Not Rocket Science, or AI.

• 5 IT Roadmap Gotchas in a Disruptive Era. Long-term planning — once the bedrock of IT operations — has become increasingly challenging for CIOs of late.

• MIT Sloan Study: Generative AI results depend on user prompts as much as models

• How to Create an AI Product Strategy: The AI Strategic Lens Framework

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 ➜ Until Next Week

There’s a big shift in how software is being created and how it captures and defends value. The companies winning aren't necessarily those with the most AI features, but those who've thought strategically about data moats, monetization models, and service delivery from day one.

Stay curious,

The GPTLDR Team