What’s McKinsey’s Three Horizons of Growth ?
The McKinsey’s Three Horizons of Growth is a management framework first introduced in the book “The Alchemy of Growth” published in 2000, which has since been referenced by top product strategists to help businesses execute existing business models while simultaneously innovating and pushing the boundaries and creating new products. What exactly are the three horizons ?
- Horizon 1 ideas provide continuous innovation to a company’s existing business model and core capabilities in the short-term.
- Horizon 2 ideas extend a company’s existing business model and core capabilities to new customers, markets, or targets.
- Horizon 3 is the creation of new capabilities and new business to take advantage of or respond to disruptive opportunities or to counter disruption.
The framework provides a structured approach to managing innovation and balancing short-term gains with long-term goals. Here’s how you can apply the framework to AI product development:
Horizon 1: Core Business Optimization
In this horizon, the focus is on leveraging AI to optimize existing products and processes within your organization. Identify areas where AI can be applied to improve efficiency, productivity, and cost-effectiveness. This could involve automating repetitive tasks, enhancing decision-making through data-driven insights, or streamlining customer support using AI-powered chatbots. The key is to generate quick wins and demonstrate the value of AI within your core business. Specific examples could be – real-time threat detection, vulnerability scanning, or identity and access management, etc.
Horizon 2: Emerging Opportunities
Horizon 2 involves exploring new opportunities and creating adjacent products or services that leverage AI capabilities. Identify emerging trends, customer needs, and market gaps where AI can be a differentiator. This could involve developing new AI-powered features, expanding into new markets, or creating AI-enabled solutions that complement your existing product portfolio. Invest in research and development to experiment with new AI technologies and validate their potential for growth. Cybersecurity specific examples could include
areas like behavioral analytics, anomaly detection, or advanced threat hunting techniques.
Horizon 3: Disruptive Innovation
Horizon 3 is all about driving disruptive innovation by exploring breakthrough AI technologies and business models. Look beyond the immediate market and envision the future of AI. Identify disruptive technologies, emerging ecosystems, and potential partnerships that can reshape your industry. This horizon involves taking calculated risks and making long-term bets on AI advancements that may not have immediate revenue streams but offer significant potential for future growth. Here specific security related examples could be AI-powered autonomous response systems, predictive threat intelligence, or decentralized cybersecurity frameworks etc.
Key Considerations:
- Talent and Capabilities: Ensure you have the right talent and capabilities to execute each horizon. This may involve hiring AI experts, data scientists, and engineers, as well as upskilling existing teams.
- Collaboration and Partnerships: Foster collaboration with external partners, academia, and startups to stay at the forefront of AI innovation. Partnering with industry leaders and research institutions can provide access to cutting-edge technologies and expertise.
- Iterative Approach: Adopt an iterative and agile approach to AI product development. Validate assumptions, gather user feedback, and iterate on your products to deliver value quickly and continuously improve.
Now comes the Question: Is the one-size-fit- all sequence of Horizons right for companies of all sizes and maturity ?
Probably not. I feel there are so many super specialized cybersecurity startups today targeting specific problems, and with the entry of Large Language Models to supercharge the speed of product development in general, I believe the above sequence of Horizons works well for a large corporation having an independent Data Science (DS) / Machine Learning (ML) team. There, the ML team has to first build core product supporting features to enhance existing products since it’s already a player in the field, and that’s probably the quickest route for Return on Investment (ROI). I have written a related article for how to choose ML projects, which seems relevant for large organizations.
If you are young startup though, today’s competitive scenario kinda makes this sequence of horizons a roadblock or a speed breaker in a lot of cases. I feel, for a young cybersecurity company focused on a small subset of problems, turning the sequence of the Horizons on its head and starting from Horizon 3 – Aggressive Disruptive Innovation will probably give you a quicker ROI in a lot of cases, followed by Horizon 2 – Pursuing Emerging Opportunities, where you have AI powered solutions assisting intelligent deterministic solutions built on top of solid cybersecurity knowledge. Agreed that this could widely vary on a case by case basis, but in general, if you are a small team with no typical red tapes found in large organizations, the time has never been better to start building AI products that question the status quo in the cybersecurity industry.
So the new upside-down framework for agile AI cybersecurity startups could be (again, wherever it fits right, may not work for all products):
- Disruptive Innovation
- Emerging Opportunities
- Core Business Optimization
Today, speed and staying agile to new developments (Large Language Models being the flag-bearer in the current tech scene) is key for staying ahead of competition, both against the malicious adversaries as well as business competitors. It’s good to challenge the status quo, and avoid getting bogged down by legacy frameworks / mindset.
Infinite Couch 