Rethinking Consulting in the Era of AI: Why ‘Build With’ Beats ‘Build For’

The 80/20 Flip

Ten years ago, building enterprise technology solutions meant 80% of the effort went into infrastructure: standing up servers, configuring platforms, wiring integrations. Only 20% was left for features that made a visible difference to the business.

That ratio has flipped.

Cloud-native platforms and AI toolsets allow foundation work to be deployed in a fraction of the time. Today, 20% of the effort delivers the foundation, leaving 80% for features that drive measurable business outcomes.

This shift demands more than speed. It demands a new consulting model built for real-time decision-making. When a business stakeholder provides key insights during a morning meeting, same-day responsiveness keeps the project on track and on time. If priorities change mid-sprint—a common reality in AI projects with technology evolving so quickly—you need partners in your time zone who can adapt with you in the moment, not respond tomorrow.

That is the value of the “build with” co-innovation model: consultants and clients building together, adapting in real time, and sharing ownership of outcomes. The traditional “build for” approach, with rigid requirements, delayed communications and overnight handoffs, simply cannot keep pace.

Why AI Breaks the Old Model

AI initiatives rarely follow a straight line. A system relying on large language models (LLMs) appears sound during planning but may require re-design once real data flows through. A customer service bot’s early results can shift the entire product strategy. The most effective path often emerges only after the first version is live. Let’s face it, in the Era of AI you don’t always know what you want until you see what you can get.

This is the reality of AI innovation. Models evolve. Tools change. Competitors introduce new capabilities with little warning. In this environment, six-month assessments and twelve-month roadmaps aren’t going to cut it. Progress depends on the ability to identify what is changing, adapt with speed, and deliver in short, repeatable cycles.

Organizations that navigate this well work with consulting partners who combine technical depth with business context.

One client put it directly: “We’re going to prioritize partners who, at a minimum, have curiosity about how our business works and ideally, bring some working understanding of our industry.”

When every decision has the potential to reshape the project, execution alone is not enough. The most effective partners understand the customer journey, the business model, and the industry dynamics. They pivot in step with the team rather than waiting for the next project phase to begin.

The Power of Same-Time-Zone Collaboration

When AI projects demand daily iteration, time zone alignment becomes as critical as technical expertise. Real-time collaboration often determines whether a solution ships in days or weeks instead of months or years.

Consider a typical AI development cycle. Morning data reveals your model is hallucinating on specific customer queries. By 11 a.m., your team has identified the cause. With same-time-zone partners, you can review the fix together, make the required changes, and deploy by mid-afternoon. With offshore teams, that same sequence stretches into a two-day delay, emails to explain the issue, a day to receive questions, and another to deploy the change.

This is not about working longer hours. It is about working at the same time. When product owners, engineers, and consultants share business hours, they solve problems in one working session instead of across multiple handoffs. They pair-program through complex implementations. They make decisions with all stakeholders present, reducing rework and missed opportunities.

At Lantern, time-zone alignment is intentional. It allows our teams to operate as extensions of our clients’ own teams, moving with their pace and priorities. When an AI initiative encounters an unexpected challenge, you need partners who can be in the conversation now, not after a 12-hour delay.

What Great Co-Innovation Actually Looks Like

The strongest AI consulting partnerships share a few defining traits:

• Business-First Thinking: Partners who understand the customer experience, the revenue model, and the competitive pressures driving the project.
• Technical Range: AI projects span engineering, data science, UX, and change management. Effective partners have fluency across disciplines, not just depth in one.
• Adaptive Execution: When an API is deprecated or user testing reveals a fundamental flaw, strong partners pivot without losing momentum.
• Context Awareness: A solution designed for a Toronto financial services firm will require different considerations than one built for a Dallas logistics provider. Great partners adapt to the organization’s culture, technical maturity, and risk tolerance.

In the “build with” model, these traits are essential. They allow consultants and clients to deliver meaningful outcomes together, side by side, even as priorities shift and new challenges emerge.

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What Clients Must Bring to Succeed

Co-innovation works when both sides are equally committed. The most successful AI initiatives share a few internal characteristics:

• Engaged Executive Sponsors: Leaders who stay involved week to week, clear blockers, reinforce priorities, and make quick decisions when the team needs direction.
• Cross-Functional Teams with Authority: AI impacts every part of the business. Teams should include decision-makers from IT, product, operations, and often legal—each empowered to act for their function.
• Shared Technical Fluency: Business leaders do not need to code, but they do need enough understanding to engage meaningfully in architectural decisions. A shared vocabulary reduces misalignment and accelerates decision-making.
• Comfort with Iteration: The first version will not be the final one. Teams that treat each release as a learning step maintain momentum and achieve better results over time.

The Competitive Edge of Building Together

Organizations succeeding with AI are rethinking how they work. They replace lengthy requirements phases with rapid prototyping. They trade rigid contracts for flexible partnerships. They measure progress in business outcomes, not just features deployed.

This approach builds capability beyond a single project. Teams get better at handling ambiguity, identifying which changes matter, and shipping quickly without sacrificing quality.

The result is speed that compounds. At Lantern, we call this Innovation Velocity. While competitors are still debating requirements, co-innovation teams are already learning from real users. Each iteration builds knowledge, strengthens capability, and increases confidence in the next move.

Moving Forward

The organizations making real progress with AI choose partners who understand their business, share their urgency, and work in their time zone. They strengthen organizational muscle through collaboration rather than simply consuming services. The Era of AI is not the time for outsourcing or insourcing. It is the time for co-innovation with partners who share expertise openly and build capability alongside your team.

AI’s pace and complexity require consulting partners who think like team members, adapt with changing conditions, and deliver as if their success depends on yours – because it does.

If you are evaluating partners for an AI initiative, start with three questions:

• Can they engage with your business leaders in real time?
• Will they adapt when priorities shift?
• In addition to technical expertise, do they bring curiosity and capability in your industry and not just your technical stack?

The future belongs to organizations that build with their partners, not through them. The faster you make that shift, the faster you start learning and moving forward with clarity in the Era of AI.