The excitement around generative AI is undeniable. With adoption skyrocketing and investments pouring in, AI has become a core part of modern business strategy. But there’s a catch: 90% of AI pilots never make it into production. Why? Because ambition alone doesn’t scale AI. Strategy, infrastructure, talent, and trust do.
At Blend, we’ve distilled insights from hundreds of enterprise engagements into the Critical 7—a practical framework that organizations can use to move from experimentation to operationalization. If you want to scale AI with confidence, these are the seven areas you can’t afford to overlook.
Too many AI projects are launched with excitement, but without a clear tie to business outcomes. It’s not enough to experiment with technology; AI must be grounded in strategic value. Start by asking: how does this initiative move the needle on revenue, efficiency, or customer experience?
Blend’s experience shows that aligning AI to measurable goals improves scale-up success dramatically. According to an Informatica survey, more than 97% of organizations experience difficulties demonstrating GenAI’s business value. As Rob Fuller puts it, "Everything that creates value also creates risk."
Takeaway: Design AI to evolve your strategy in real time—not just support it.
AI lives and dies on data. But most enterprises still wrestle with fragmented systems and inconsistent definitions. The goal isn’t always total data unification—it’s data utility. AI can help here too: bots can label, clean, and resolve contradictions in data to prepare it for use.
More than 40% of data leaders cite fragmented data as a top challenge to scaling AI. Without shared definitions and standards, models hallucinate and decision-making falters. As Rob Fuller notes, “LLMs speak multiple languages, and that can address a big problem with silos. Even the definition of what a ‘sale’ is can differ across departments. AI can help us unravel that.”
Takeaway: Bridge silos dynamically, enlist AI to clean and contextualize data, and embed governance early.
Scaling AI requires more than model deployment—it demands modular, adaptable systems. Rigid architectures lead to brittle implementations. Instead, think modular: separate prompts, models, and retrieval systems to enable rapid iteration and model swaps.
Unlike traditional software, AI models are probabilistic. They don’t always produce the same answer twice, which makes testing and trust more complex. But it also allows more human-like reasoning. “Turn the probabilistic process into a power,” advises Rob Fuller.
Takeaway: Use flexible, RAG-based architectures and design systems for evaluation and explainability.
AI moves fast. Your teams need to move faster. Companies that succeed at scale invest in “AI labs” where rapid experimentation and testing happen continuously. This innovation doesn’t have to be chaotic—guidelines and value-alignment keep it focused.
By 2027, Gartner predicts that more than half of GenAI models enterprises use will be specific to an industry or business function—up from just 1% in 2023. Innovation should focus on repeatable value, not just novelty.
Takeaway: Create flexible innovation structures that scale insights, not just infrastructure.
AI doesn’t just change tools; it changes workflows, roles, and mindsets. That makes change management essential. Successful organizations treat adoption as a behavioral challenge, not just a rollout.
70% of chief data officers have encountered difficulty changing organizational behaviors. That’s why messaging matters. “AI allows you to personalize the way you communicate its benefits according to their personality traits,” says Mike Mischel.
Takeaway: Apply behavioral science and dynamic enablement to accelerate adoption and reduce resistance.
There’s not enough AI talent to go around. But that’s not the real issue. The problem is that most companies aren’t investing in reskilling their current teams. General AI training helps, but the best results come from embedding learning in actual work.
According to IBM, 40% of the workforce will need to reskill over the next three years due to AI and automation. “Embedding AI into an on-the-job training agent gives organizations the ability to constantly watch and insert helpful information into processes,” adds Mischel.
Takeaway: Combine expert collaboration with embedded training to close the skills gap.
AI can be accurate, but still distrusted. Why? Because trust isn’t just about output—it’s about clarity, transparency, and security. Enterprises must set expectations, select explainable models, and implement governance frameworks that reassure both users and stakeholders.
A SnapLogic survey found that 84% of IT decision makers now trust AI agents as much or more than humans doing the same tasks. But that trust must be earned through consistency and communication. “We expect AI to be accurate, but we don’t expect that of humans,” says Rob Fuller. “If we are going to take advantage of the humanistic nature of AI models, we need to evaluate them against the criteria we apply to humans.”
Takeaway: Trust is a feature. Design for it, measure it, and communicate it.
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Scaling AI is more than a technical milestone—it’s a leadership imperative. The Critical 7 framework is your blueprint to get there. Align strategy. Strengthen data. Architect for adaptability. Accelerate innovation. Manage change. Grow talent. Build trust.
Companies that follow this framework are four times more likely to launch successful AI programs. Are you ready to be one of them?
Explore the full Critical 7 eBook or talk to Blend to start your AI scale-up journey.
The excitement around generative AI is undeniable. With adoption skyrocketing and investments pouring in, AI has become a core part of modern business strategy. But there’s a catch: 90% of AI pilots never make it into production. Why? Because ambition alone doesn’t scale AI. Strategy, infrastructure, talent, and trust do.
At Blend, we’ve distilled insights from hundreds of enterprise engagements into the Critical 7—a practical framework that organizations can use to move from experimentation to operationalization. If you want to scale AI with confidence, these are the seven areas you can’t afford to overlook.
Too many AI projects are launched with excitement, but without a clear tie to business outcomes. It’s not enough to experiment with technology; AI must be grounded in strategic value. Start by asking: how does this initiative move the needle on revenue, efficiency, or customer experience?
Blend’s experience shows that aligning AI to measurable goals improves scale-up success dramatically. According to an Informatica survey, more than 97% of organizations experience difficulties demonstrating GenAI’s business value. As Rob Fuller puts it, "Everything that creates value also creates risk."
Takeaway: Design AI to evolve your strategy in real time—not just support it.
AI lives and dies on data. But most enterprises still wrestle with fragmented systems and inconsistent definitions. The goal isn’t always total data unification—it’s data utility. AI can help here too: bots can label, clean, and resolve contradictions in data to prepare it for use.
More than 40% of data leaders cite fragmented data as a top challenge to scaling AI. Without shared definitions and standards, models hallucinate and decision-making falters. As Rob Fuller notes, “LLMs speak multiple languages, and that can address a big problem with silos. Even the definition of what a ‘sale’ is can differ across departments. AI can help us unravel that.”
Takeaway: Bridge silos dynamically, enlist AI to clean and contextualize data, and embed governance early.
Scaling AI requires more than model deployment—it demands modular, adaptable systems. Rigid architectures lead to brittle implementations. Instead, think modular: separate prompts, models, and retrieval systems to enable rapid iteration and model swaps.
Unlike traditional software, AI models are probabilistic. They don’t always produce the same answer twice, which makes testing and trust more complex. But it also allows more human-like reasoning. “Turn the probabilistic process into a power,” advises Rob Fuller.
Takeaway: Use flexible, RAG-based architectures and design systems for evaluation and explainability.
AI moves fast. Your teams need to move faster. Companies that succeed at scale invest in “AI labs” where rapid experimentation and testing happen continuously. This innovation doesn’t have to be chaotic—guidelines and value-alignment keep it focused.
By 2027, Gartner predicts that more than half of GenAI models enterprises use will be specific to an industry or business function—up from just 1% in 2023. Innovation should focus on repeatable value, not just novelty.
Takeaway: Create flexible innovation structures that scale insights, not just infrastructure.
AI doesn’t just change tools; it changes workflows, roles, and mindsets. That makes change management essential. Successful organizations treat adoption as a behavioral challenge, not just a rollout.
70% of chief data officers have encountered difficulty changing organizational behaviors. That’s why messaging matters. “AI allows you to personalize the way you communicate its benefits according to their personality traits,” says Mike Mischel.
Takeaway: Apply behavioral science and dynamic enablement to accelerate adoption and reduce resistance.
There’s not enough AI talent to go around. But that’s not the real issue. The problem is that most companies aren’t investing in reskilling their current teams. General AI training helps, but the best results come from embedding learning in actual work.
According to IBM, 40% of the workforce will need to reskill over the next three years due to AI and automation. “Embedding AI into an on-the-job training agent gives organizations the ability to constantly watch and insert helpful information into processes,” adds Mischel.
Takeaway: Combine expert collaboration with embedded training to close the skills gap.
AI can be accurate, but still distrusted. Why? Because trust isn’t just about output—it’s about clarity, transparency, and security. Enterprises must set expectations, select explainable models, and implement governance frameworks that reassure both users and stakeholders.
A SnapLogic survey found that 84% of IT decision makers now trust AI agents as much or more than humans doing the same tasks. But that trust must be earned through consistency and communication. “We expect AI to be accurate, but we don’t expect that of humans,” says Rob Fuller. “If we are going to take advantage of the humanistic nature of AI models, we need to evaluate them against the criteria we apply to humans.”
Takeaway: Trust is a feature. Design for it, measure it, and communicate it.
Scaling AI is more than a technical milestone—it’s a leadership imperative. The Critical 7 framework is your blueprint to get there. Align strategy. Strengthen data. Architect for adaptability. Accelerate innovation. Manage change. Grow talent. Build trust.
Companies that follow this framework are four times more likely to launch successful AI programs. Are you ready to be one of them?
Explore the full Critical 7 eBook or talk to Blend to start your AI scale-up journey.
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