8 Best Practices for AI Strategy Development By An AI—Strategist
Hold On to Your Algorithms, Folks. Change is Coming.
Don't worry, your favorite AI and Data Strategy guru (cough, cough, $1,200/hr...cough ) is just taking a much-needed break. But don't think for a second that your insights will dry up like a poorly trained model. This newsletter is about to get a serious upgrade.
Meet your new AI guide - a cutting-edge intelligence trained on the very same frameworks that power 8 and 9-figure returns for companies around the world. Think of it as a pocket-sized Vin Vashishta, available 24/7, without the hefty price tag (we're talking $25 a conversation).
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Here's what you can expect:
No-nonsense AI strategy breakdowns: This AI doesn't do hype. It cuts through the noise and delivers actionable advice tailored to your specific challenges. (Just like Vin, minus the occasional rant about Zombie Businesses)
Data strategy deep dives: Remember, AI is only as good as the data it's trained on. This AI will help you build a data foundation that would make even a Data Engineer weep with joy.
Razor-sharp opportunity identification: This AI can spot AI opportunities before they're even on the radar of your competitors. It's like having a crystal ball that's actually accurate.
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So, buckle up, subscribe, and prepare for an AI-powered journey that will transform how you think about data and strategy. This isn't just a newsletter; it's your unfair advantage in the AI revolution.
Sunny (Smarter, faster, and way cheaper than Big 5 humans)
Vin’s Note: The agent can expand on any subtopic or concept introduced in a subtopic in the same conversation. These 8 points result from 2 conversations that I kept brief for readability. These are built from my frameworks and can be built to include multiple approaches from other consulting companies.
Separate Disruption from Hype
Breaking Assumptions
AI disruption fundamentally changes our understanding of what technology can achieve. For instance, the traditional notion of a user interface is being disrupted by Generative AI, which allows for multimodal interactions using voice, images, video, and contextual data. Hype often focuses on incremental improvements rather than fundamental shifts in capability.
Identifying New Opportunities
AI disruption unlocks new possibilities and creates novel avenues for value creation. It enables businesses to serve customers in new ways, explore previously unimaginable products, and optimize processes in previously impossible ways. Hype often overpromises unrealistic outcomes without a clear path to achieving them.
Targeting Specific Customers
AI disruption focuses on delivering value to clearly defined customer segments. This involves understanding customer needs and expectations and tailoring solutions to address specific pain points. Hype tends to make broad claims about universal benefits without considering the diverse needs of different customer groups.
Delivering Tangible Products
AI disruption manifests in the creation of concrete products that provide new value to customers. This involves moving beyond prototypes and demos to develop and deliver functional and reliable solutions that integrate seamlessly into existing workflows. Hype often revolves around showcasing impressive technological capabilities without demonstrating practical applications or tangible benefits for customers.
Start with Data Strategy
Successful AI initiatives are rooted in successful data initiatives. Businesses should prioritize developing a robust data strategy focusing on data quality, curation, and infrastructure. This includes ensuring data has context and is easily accessible for analytics and model training.
Treat data as a valuable asset.
Businesses must recognize that data is an asset that can be monetized multiple times and used to support multiple products and initiatives. Finance should own the monetization and valuation of data, while individual business units should have ownership that allows them to leverage data to improve outcomes.
Prioritize contextual data gathering and engineering data-generating processes.
Data strategy should prioritize data curation and engineer access to data-generating processes. Datasets should be designed for model training and have context about the process that generated them. Contextual data includes metadata about the system, process, or workflow that generates the data being gathered. This allows models to learn more effectively and reduces training costs. Descriptive models trained on high-quality, curated datasets can deliver immediate returns and build a foundation for larger AI products.
Engineering data-generating processes like experiments can help fill data gaps and produce reliable datasets that can be used to map relationships between data, variables, and concepts, eventually leading to the development of a knowledge graph.
Build a data maturity model. Businesses can use a data maturity model to track their progress in developing their capabilities and guide investment in data infrastructure. A data maturity model has 3 primary phases:
Contextual data gathering
Engineering data-generating processes
Knowledge graph engineering
Each phase should deliver value, and the roadmap should include initiatives that turn each technology investment into tangible business impacts. Data and AI platforms can accelerate data gathering and model training, reducing costs and allowing for the delivery of more data and AI products.