Thinking

AI for the Real World

Most AI projects die in the pilot phase because companies chase moonshots instead of solving actual problems. Davenport and Ronanki surveyed 250 executives and studied 152 AI projects to figure out what separates the ones that ship from the ones that stall. Their conclusion: the companies getting real value from AI are not trying to replace their workforce or reinvent their industry. They are automating specific processes, pulling insights from data they already collect, and augmenting the people they already have. If you run a small team and want to skip the hype cycle, this is the clearest framework for deciding where AI actually fits in your operation.

From Harvard Business Review by Thomas H. Davenport and Rajeev Ronanki

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The Imperatives for Automation Success

McKinsey surveyed hundreds of companies about their automation efforts and found a clear gap: 40 percent of large companies have scaled automation across at least one function, but only 25 percent of smaller companies have done the same. The difference is not budget or ambition. It is whether leadership understands the total cost of ownership, whether IT is involved from the planning phase, and whether the company treats automation as a strategic priority rather than a one-off project. For a business with 5 to 20 employees, this research is a blueprint. It shows that smaller companies actually have a higher success rate when they do automate, but they need to pick the right process and commit to it properly.

From McKinsey & Company by McKinsey Operations Practice

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When Should Your Company Develop Its Own Software?

Off-the-shelf software works until your process is the product. Until how you deliver is your competitive edge. Robert Sher breaks down the build-versus-buy decision with real case studies from midsize companies that chose to invest in custom code. He identifies three competencies you need before building makes sense: converting business requirements into technical specs, having development capacity, and maintaining what you build over time. The takeaway is not that custom software is always better. It is that generic tools eventually cap your growth when your operation has a workflow that no SaaS product was designed to handle. If you have ever outgrown a tool and duct-taped three platforms together to fill the gap, this article explains why and what the alternative looks like.

From Harvard Business Review by Robert Sher

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What Makes Some Teams High Performing?

A 10-person company cannot hire its way out of operational drag. But it can build a team that punches above its weight. David Burkus, an organizational psychologist, reviewed decades of research on team performance and identified the patterns that separate high-performing teams from the rest. It is not about hiring superstars or running more standups. It is about shared understanding, every person knowing how their work connects to the team's output, and psychological safety, where people flag problems early instead of hiding them. For a small business owner managing a lean crew, these findings are directly actionable. You do not need a bigger team. You need the team you have operating with fewer blind spots and less wasted motion.

From Harvard Business Review by David Burkus

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Using AI to Enhance Business Operations

Most conversations about AI in business jump straight to chatbots or predictive analytics. This article from MIT Sloan Management Review takes a different angle: embedding cognitive computing into the processes your business already runs. The research covers how companies use AI to automate repetitive, formulaic tasks like data entry, report generation, and invoice processing without ripping out their existing systems. The distinction matters for small businesses. You do not need a data science team or a six-month implementation plan. You need to identify the tasks that eat your week, understand which ones follow consistent rules, and apply automation where the pattern is clear. This piece gives you the vocabulary and the framework to have that conversation with whoever builds your systems.

From MIT Sloan Management Review by Cynthia M. Beath, Jeanne W. Ross, and Monideepa Tarafdar

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Turn Generative AI from an Existential Threat into a Competitive Advantage

This is the article that frames the stakes clearly. Cook, Hagiu, and Wright lay out three levels of AI adoption: Level 1 is using publicly available tools, and that is already becoming table stakes. Level 2 is customizing AI with your own data. Level 3 is building feedback loops that compound your advantage over time. The companies stuck at Level 1 are not gaining ground. They are just keeping pace. The ones that never start are falling behind in ways that get harder to reverse every quarter. For a small business owner, the message is direct: the window between AI being a competitive advantage and AI being a competitive necessity is shrinking fast. The businesses that move now, even with small, focused projects, will operate at a different level than the ones that wait.

From Harvard Business Review by Scott Cook, Andrei Hagiu, and Julian Wright

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