Four years into the GenAI era, the story is consistent and uncomfortable: AI is everywhere, but ROI is not. The entire process—from selecting use cases to implementing generative AI and measuring business outcomes—is a struggle. MIT reports that 95% of organizations still aren’t seeing meaningful returns, while a small 5% are already generating millions in value.
This widening gap—the GenAI divide—represents one of the biggest challenges and opportunities for technology advisors today.
And it’s not because the technology doesn’t work. It’s because companies are approaching AI enablement the wrong way.
Telarus’ new eBook, Closing the GenAI Divide, breaks down why most generative AI projects stall, how the most successful companies scale quickly, and what advisors can do to guide customers toward real business outcomes.
Here’s a closer look at what’s inside.
Top Takeaways from Closing the GenAI Divide
It’s Not an AI Technology Issue—it’s a Business Problem
Because GenAI is relatively new, it’s easy to blame the technology for poor ROI. But in most cases, the real challenge lies in how companies deploy AI systems. More than 80% of organizations have explored or piloted GenAI, yet most have not translated that adoption into measurable business value.
The eBook outlines the three consistent reasons why GenAI projects fail. The main challenges in implementing generative AI are misaligned strategy, poor adoption, and fragmented platforms—and where advisors become essential in helping customers avoid these pitfalls.
Mid-Market and Enterprise Customers Are Struggling with GenAI Implementations for Different Reasons
Everyone is wrestling with AI deployments—but not in the same way.
Enterprises:
- Slower to move—rollout cycles stretch to 9 months or more
- Greater cost sensitivity
- Only 36% mandate GenAI training → major skills gap
Mid-market organizations:
- Move dramatically faster—top performers reach full rollouts in 90 days
- Lead in early governance frameworks (36% vs 23% of enterprises)
- Report stronger alignment between AI initiatives and business goals
These insights give advisors a roadmap for how to approach each customer segment with precision.
What the Winning 5% Do Differently
They extend GenAI into back-office/finance functions (not just customer-facing), scale deliberately, and lean on external partners—internal-only deployments succeed 33% of the time vs. 67% for partnered ones.
Outcome-Based Selling Will Separate Top Advisors from Everyone Else
Customers don’t need another platform demo. They need a way to turn AI initiatives into KPIs, measurable results, and confident executive buy-in.
Inside the eBook you’ll find:
- Practical generative AI frameworks for tying AI to business outcomes
- Advisor-ready questions that uncover real client needs
- Pitfalls to avoid when guiding AI conversations
These tools and GenAI best practices will help you lead strategic, value-driven AI discussions—not just AI technology recommendations.
The Pressure Is Rising for Tech Advisors to Provide GenAI Implementation Strategies
As GenAI enters a new phase, companies are demanding proof of ROI—not promises. Advisors who can overcome barriers and deliver measurable value will stand apart in 2026.
If you want the questions, the positioning, and the practical guidance needed to lead GenAI strategy conversations with confidence…
Before reading the eBook consider these questions:
- Where do each of your clients sit on the GenAI divide right now?
- Which of the three failure reasons hits closest to home to each?
- What would “AI readiness” actually look like for your top clients?
Download your free copy of Closing the GenAI Divide now.
Implementing Generative AI: Key FAQs
What are the main challenges in implementing generative AI?
Mid-market and enterprise companies usually face data quality and governance issues, security and compliance risks, integration with legacy systems, and a lack of internal AI skills. Change management is also a major hurdle, because employees and leaders need to trust the outputs and adapt existing workflows.
How to implement generative AI?
The best approach is to start with a few high-value use cases, such as support automation, document drafting, or knowledge search, then build the needed data, security, and governance controls around them. After a small pilot proves value, companies can expand gradually, with human review for sensitive outputs and training for the teams involved.
What are the benefits of implementing generative AI?
Generative AI can speed up content creation, automate repetitive work, improve employee productivity, and make customer interactions faster and more personalized. It can also reduce operating costs and help companies uncover new products, services, and revenue opportunities.
Closing the GenAI Divide eBook FAQs
Who is this eBook for?
This eBook is written for technology advisors and solution providers—the consultants, VARs, and MSPs who sit between AI vendors and the businesses trying to adopt GenAI, particularly those working with mid-market and enterprise clients.
How will this eBook help you with GenAI implementation conversations?
Closing the GenAI Divide will arm you with the research, the diagnosis of why deployments stall and a concrete playbook so you are viewed as a trusted “outcome translators.” It includes better discovery questions to ask clients, tips for steering clients toward platforms instead of point solutions, and guidance on building AI readiness assessments.