In a recent engagement, my team and I were brought on to provide an independent perspective on another firm’s end‑to‑end 'transformation' proposal. During our review, it became clear that what was called a 'transformation' was, instead, a 'modernization'.   Like many orgs juggling competing priorities, and with our clients immersed in the granularity of the work, they missed the signs that the work had shifted from reimagining the business to incrementally improving their current state.   Alongside other recommendations, we encouraged the client to step back and clearly define its desired future‑state business strategy, anchored by a vision and design principles that can guide decision‑making, focus, and create accountability for driving actual, real-deal transformation.   🔍 This article, while focused on AI, helps to articulate a distinction that often comes up in my world, and one that is oh so fun to discuss: -Modernization improves how the business operates today. -Innovation experiments with new ways of creating value. -Transformation fundamentally rewires the business model itself.   ⭐ My favorite part: “The biggest misconception is that this is a linear journey. It is not. Healthy AI strategies run all three at once, with different expectations and different definitions of progress. The key is to know where you are and where you’re headed, because the same decision rules do not apply to every AI initiative.”   We can chew gum and walk at the same time - and life gets better when business leaders let their teams know where the destination is. There's nothing better for teams than crystal clear clarity on where they're headed and how they can help. https://lnkd.in/gB8S3d6C

Waiting to adopt AI this peak season could cost your business more than just missed gains. Multi-location operators face intensified pressure during peak periods—rising labor costs, supply chain unpredictability, and heightened customer expectations all demand swift, precise action. AI-driven automation and predictive analytics are no longer futuristic concepts; they are mission-critical tools changing operations and driving measurable ROI today. Consider this: retailers integrating AI-powered demand forecasting saw inventory waste drop by up to 32% last season. Restaurants using AI for workforce management reduced scheduling errors by 25%, directly impacting labor costs and service quality. Delaying AI adoption means risking inefficiencies that cascade into lost revenue and compromised customer satisfaction during your busiest times. The cost of waiting isn't just operational—it's strategic. Adopting AI now positions you to streamline workflows, anticipate challenges, and scale confidently. At ResultStack, we build AI solutions designed specifically for operational complexity, combining enterprise expertise with agile execution. We've been in your seat, and we understand that technology must serve your business goals—not complicate them. How are you preparing your technology strategy to meet the demands of this peak season with AI?

The AI Adoption Journey, What Business Leaders Should Understand Before Jumping In Every organization wants to tap into the power of AI, but the real success stories come from companies that treat AI adoption as a structured journey, not a quick bolt‑on tool. Here are the stages we guide our clients through to ensure AI is implemented safely, reliably, and in a way that truly improves business outcomes: 1. Define the Business Case AI should solve a clear problem, reducing manual work, improving decision making, enhancing customer service, or streamlining workflows. The goal is not “using AI”, it is achieving measurable impact. 2. Strengthen the Foundation Before bringing AI into the environment, businesses must ensure security, data quality, and governance are in place. AI is only as good as the systems and data behind it. 3. Identify High Value Workflows Not every process needs AI. We help teams identify the three to five workflows where automation, intelligence, or prediction can produce meaningful results. 4. Implement With Guardrails AI tools need policies, access controls, and monitoring. A strategic approach ensures sensitive data stays protected and employees use AI responsibly. 5. Train Teams, Not Just Technology The biggest gains happen when employees understand how to use AI to enhance their work, reducing friction, improving response times, and enabling higher value tasks. 6. Measure and Iterate AI is not set and forget. We help clients measure ROI, refine workflows, and expand adoption thoughtfully over time. Organizations that follow a structured AI journey see more predictable results, fewer risks, and a more empowered workforce. If you're exploring how to bring AI into your business or want help identifying where it can make the biggest impact, I will send a survey to identify your needs and schedule a workshop to review and discuss next steps for your organization.

Organizations are twice as likely to exceed their AI return on investment when they prioritize how humans and machines work together, That gap also shows up in boardroom discussions regularly. The conversation is usually about adoption rates and investment size. Less often about whether the work itself was redesigned to make the technology useful. One example in the piece makes the point well. A telecom company added AI to customer service without changing roles or workflow and saw a 5% productivity lift. When they dedicated serious resources to redesigning the human-AI interaction (new workflows, clear escalation paths, real training) that number jumped to 30%. Deployment is a procurement decision. Value creation is a design decision. They require different conversations, different owners, and different measures of success.

AI transformation plans often look airtight on paper but then break down in execution.   That’s the focus of the latest episode of Deloitte FWD: Technology: AI transformation is not just a tech project.   We spend a lot of time talking about tech ambition. What this conversation surfaces is where value leaks and not in the models or platforms, but in daily design. Workflows, decision rights, incentives, metrics – the elements that determine whether new capabilities compound or stall.   Brett Davis, Simona Spelman, and I sat down with Karenann Terrell, who has led large-scale transformations across industries. Her perspective reinforces something I see consistently... transformation failure isn’t a technology problem or even a people problem. It’s a design problem.   Organizations can often mistake activity for progress; dashboards light up, adoption numbers rise but if behaviors don’t change, value can never fully materialize.   AI raises the stakes. For every dollar invested in AI, the cost of not redesigning work multiplies. Without intentional design, intelligence increases friction instead of reducing it; it reinforces how execution doesn’t happen by hope or momentum.    Real transformation efforts have to be designed for the front line, not just the boardroom. Watch/listen here: https://deloi.tt/4sVxXmj

Generative AI (Gen AI) promises transformative possibilities for businesses, but without clear goals and expectations, its potential challenges and risks become an expensive experiment in disconnected innovation. Leaders must approach Gen AI projects with purpose and precision, aligning them with organizational priorities while encouraging an environment where creative experimentation thrives.  Dr. Gleb Tsipursky investigates!!! https://lnkd.in/ev7PbQrg

The fastest way for enterprises to fail at AI adoption? Keep adding more tools. → ChatGPT → Claude → Perplexity → Notion AI → Five more productivity apps Soon the stack looks impressive. But the workflow remains unchanged. And that’s the problem. AI transformation does not happen because teams have access to tools. It happens when workflows are redesigned. Without workflow engineering, three things happen inside organizations: 1. Fragmentation Different teams start using different tools in different ways. Outputs become inconsistent. Processes become harder to standardize. 2. Cognitive Overload Employees are expected to guess: → which tool to use → when to use it → how to prompt it Adoption drops because complexity increases. 3. No Measurable ROI If workflows stay the same, productivity gains are invisible. Leadership sees activity, not improvement. Real AI adoption looks very different. → First, map the workflow → Then identify repeatable tasks → Then align the right tools → Then standardize prompts and outputs → Then measure the performance change Tools are just components. Architecture creates results. I recently built a simple 'Growth vs. Chaos AI Adoption Rubric'. I use it with corporate teams to test whether they’re just experimenting or actually standardizing their workflows. If it would be useful, comment “ADOPT” and I’ll send it across.

Most companies are approaching AI transformation the wrong way. I recently watched a webinar from the Board of Innovation on how AI is reshaping markets and how businesses are responding. The biggest mistake is this: Many companies still treat AI as a productivity tool or as a list of quick-win use cases. That is the trap many businesses are falling into. AI is not just helping companies work faster. It is reshaping how companies create value, compete, and defend their position. If your AI strategy is just a list of use cases, you may be optimizing a business model that is already losing relevance. The disruption is happening across three structural shifts: 🤖 Discovery is changing Customers are increasingly relying on AI systems to filter, rank, and recommend their options. Businesses are no longer competing only for human attention. They are also competing to be surfaced and selected by AI-driven interfaces. 📉 Scale is changing What once required large teams and heavy coordination can now be done by smaller, leaner, AI-enabled teams. Coordination costs are falling, barriers to entry are dropping, and new competitors can emerge faster with fewer resources. 🎯 Pricing logic is changing As AI makes knowledge work, content, and code cheaper to produce, customers become less willing to pay for time, hours, or process alone. They increasingly want to pay for results, impact, and measurable performance. That is why an AI strategy cannot just be a list of use cases. It has to answer a harder question: How will this business stay valuable when generic work becomes cheaper and faster to produce? A more serious response to AI needs to happen on 3 fronts: ♟️ Strategic reinvention Reconsider where you play and how you win in a market where more generic output is becoming cheaper. Do not reduce AI strategy to use cases. Focus on building defensible advantages such as proprietary data, embedded workflows, trust, and differentiated customer experience. ⚙️ Workflow redesign Do not just add AI into existing processes. Redesign workflows from the ground up, especially high-volume and repeatable work where AI can structurally reduce effort and cost. 🧩 Operating model redesign Rethink team structure, roles, governance, and decision-making. Many businesses will need smaller, more senior, AI-augmented teams that can move faster and operate with a lower cost base. The bottom line: Small efficiency gains may help in the short term. But they will not protect you from a market that is being fundamentally reshaped.

The Biggest AI Opportunity Isn’t Automation. It’s Decision Speed. Most people think AI’s biggest value is automation. Automating emails. Automating reports. Automating repetitive tasks. But the real advantage is something far more powerful: Decision speed. Great companies don’t just work faster. They decide faster. AI changes things because it can: Turn raw data into insight in seconds Surface patterns humans miss Reduce the time between question → answer → action And when decisions happen faster, everything else accelerates: Product development Customer response Market adaptation Innovation cycles This is why the companies that win with AI aren’t the ones replacing people. They’re the ones augmenting leadership and teams with intelligence. Because in the modern economy, the biggest competitive edge isn’t capital. It’s how quickly you can make the right decision.

Another AI pilot is failing today. Not because the model is weak. Because step zero never happened. Dashboards multiply. Budgets grow. But employees still go back to old good Excel. Every time! The pattern is now predictable: → low adoption kills ROI → too many tools blur the workflow → costs rise, impact doesn’t → no clear ownership, no real outcome And then comes the verdict: “AI didn’t deliver.” But the truth is AI didn’t fail. The strategy did. At Makia Labs, we start before the first line of code. Because successful AI adoption doesn’t start with tools. It starts with clarity. We use the MAKIA framework to guide that: • Meaning — solve a real business problem • Actors — involve and upskill the people using it • Knowledge — build literacy before automation • Impact — measure value, not activity • Alignment — connect to strategy, risk, governance When these are in place: Adoption increases. Workflows improve. Teams trust the system. And AI finally becomes leverage. We bring this to life through: → hands-on integration workshops → tailored AI training for teams and leaders → innovation strategy combining design thinking + AI Always with one principle: AI should elevate human work. Not replace it. 👉 Where has AI adoption broken down in your organisation? The honest answers are usually the most useful ones.