#11 - Want to Adopt AI? Build a Management Culture of Clarity First

Originally published in The Times of India .

When running a business using best practices from the offline world, many uncomfortable facts may remain unspoken but are still understood. Similarly, crucial details that can make or break outcomes are often left undefined and delegated. This flexibility allows organizations to operate with a level of ambiguity that rarely derails their strategic goals.

However, as businesses adopt AI technologies at their core, this informal operating culture becomes a liability. AI demands a more structured, explicit, and data-driven approach to decision-making.

Why it matters: In the digital age, unclear objectives, undefined parameters, and ambiguous decision-making processes can lead to missed opportunities, suboptimal performance, or even catastrophic outcomes.?

Is this true only when adopting AI? No. Any business relying on a “one-size-fits-all” strategy will struggle, regardless of the technology. However, AI magnifies the consequences of strategic misalignment. For example, armies that cost-optimize in a military conflict end up bringing knives to a gunfight.?

In this post, we will explore three traditional business strategies—win at all costs, cost optimization, and balancing trade-offs—and examine how the introduction of AI fundamentally alters each approach.

Let’s discuss three different business outcomes:

Win at All Costs (Effectiveness)

Some business problems are zero-sum and high-stakes, with the potential for exponential, long-term rewards. Businesses must win these battles, no matter the cost.

Here are a few such situations:

• Transitioning Revenue: Disney invested billions and sacrificed revenue from its home video division and syndication deals with Netflix to maintain a direct relationship with audiences through Disney+.
• Market Dominance: YouTube became the dominant video-sharing platform by integrating user-generated content with a powerful recommendation algorithm. This helped it outpace competitors like Vimeo and Dailymotion, establishing its unchallenged dominance.
• Talent Acquisition: Spotify expanded its podcasting division by acquiring top-tier talent, including the Joe Rogan Experience and The Ringer. Similarly, Facebook’s entry into AI was driven by acquiring Yann LeCun, a renowned AI researcher.
• Securing Exclusive Partnerships or Supply Chains: From 2017 to 2022, Star India fueled Hotstar’s growth by acquiring the exclusive broadcast rights to the Indian Premier League.
• De-risking Supply: Netflix spent billions on original content to reduce reliance on third-party licensing deals, shifting away from popular shows like Friends and The Office.
• Race for Regulatory Approvals or Market Entry: Pfizer and Moderna raced to be the first to secure COVID-19 vaccine approvals.
• Acquiring Strategic Companies or Technologies: Facebook’s acquisitions of Instagram and WhatsApp locked out competitors in the social media market. Microsoft’s strategic investment in OpenAI gave it an edge in its competition with Google.
• Winning Long-Term Contracts: Lockheed Martin’s win of the trillion-dollar F-35 contract secured cash flow for multiple decades.
• Establishing Industry Standards: Microsoft won its battle against Apple by establishing Windows as the global standard for operating systems, making it difficult for competitors to challenge.

Without strategic clarity from the top that a certain problem is a win-at-all-costs battle, managers will focus on cost optimization or risk mitigation, ultimately losing in high-stakes situations.

To win these games, companies need to:

• Outspend competitors in areas like marketing, R&D, and strategic acquisitions. Competitors should think twice before attempting to compete, and those who do should ultimately lose in the war of attrition.
• Take the fight into competitors’ markets, as Apple did when it disrupted Nokia’s smartphone market. Having secured its existing revenues from laptops and music players, Apple raised growth capital to challenge Nokia’s dominance in the smartphone market. Nokia had to catch up with Apple’s spending on R&D and marketing while dealing with declining revenues from its feature phone division.
• Have strategic clarity to cannibalize one’s own revenue, as Apple did with the iPod to grow the iPhone.
• Outsmart competitors, as Tesla did by opening its patents. This triggered a prisoner’s dilemma among incumbent car companies: if they didn’t use the patents, their competitors might, putting them at a disadvantage. Eventually, everyone began working on EVs, which grew the EV market share and resulted in Tesla becoming the dominant car company in the US.

What about AI’s role? AI might be one of the technologies to invest in when outspending competitors. However, in most other aspects, AI and machine learning cannot substitute strategic planning.

Cost Optimization (Efficiency)

Cost optimization is the process of strategically reducing expenses while ensuring the quality and value of products or services are maintained or improved. In today’s competitive markets, AI plays a critical role in doing things more efficiently, faster, or cheaper with fewer resources. For example:

• Automating Repetitive or Tedious Tasks: Use of AI for translation and transcription to produce subtitles and dubs across languages
• Enhancing Operational Efficiency: Replacing spend on reporters with a centralized input desk that relies on input from live TV, news wires, and social media for news coverage.

While AI can bring substantial cost savings, it’s important to remember that cost optimization is not a long-term competitive advantage. Over time, these efficiency gains become industry standards, and what once differentiated a business becomes merely table stakes. Often consulting companies excel at spotting and spreading these best practices.?

However, be cautious; beyond a certain point, this makes all companies within a market identical copies of each other, turning the industry into a low-margin, commodity business. This reduces every company's ability to attract fresh capital because there are no future profits to be harvested, ultimately stifling innovation. As a friend puts it, cost optimization is like shaving off dead skin—beneficial, but only to a certain extent.?

Frequently Asked Questions:

Why it matters: Much like modern retail, media is often a low margin, low average revenue per user business. Given that, cost optimization is given.?

What is the role of AI in such problems? As industries invest more in AI, companies will increasingly be expected to view AI as a table-stakes investment (a necessary participating cost) for operating in the business.

What is the relationship between ‘Win At All Costs’ and ‘Cost Optimization’? Pre-optimization is a sin. Once the strategic game has been won, then focus on cost optimization to move the business from the investment stage to the cash flow stage.

Maximizing While Balancing Trade-offs

For a detailed understanding of this problem statement, read the post ‘Can’t Win Dynamic Games with Static Business Rules .’?

For example, one could use Large Language Models (LLMs) to mass-produce variations of existing content, aiming to flood search and social marketplaces and boost programmatic ad revenue. Over time, these platforms' algorithms are likely to detect the near-duplicate nature of the AI-generated content and down rank it in search results or social feeds. Additionally, the increased supply of similar ad inventory could drive down eCPMs (effective cost per thousand impressions), ultimately diminishing the intended revenue gains.

Other problem statements include:

• Optimizing revenue from feed and search in quick commerce platforms from low-margin, must-have products (fresh produce) and high-margin products like gourmet food products.
• Optimizing revenue from feed and search in social media and media products from content and monetization

In such problems, one has to acknowledge the inherent tension and optimize within constraints. The only way to be both efficient and effective with these problem statements is by using AI, specifically recommender systems, that are custom-built for your use case. Hence, at the heart of Facebook, Google Search, Google Ads, Amazon, etc. are these systems.

How it works: Algorithms can help navigate these trade-offs, akin to Nassim Nicholas Taleb's Barbell Strategy: allocating most resources (85%) to safe bets with steady returns while reserving a portion (15%) for high-risk, high-reward experiments. Once the high-risk, high-reward experiment is proven, then scale it up in the safe bet pool (85%) and start a new experiment in the 15% pool.?

Conclusion

As AI becomes increasingly central to business operations, companies must adapt their traditional strategies to harness its full potential. Whether it’s pursuing a win at all costs approach, optimizing costs for efficiency, or balancing competing trade-offs, AI’s role is not just to execute tasks faster or cheaper—it’s to fundamentally reshape how businesses operate, compete, and innovate.

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Want to republish it? This post was released under CC BY-ND — you can republish it as is with the following credit and backlinks: ‘Originally published by Ritvvij Parrikh on The Times of India . The author retains the copyright and any other ancillary rights to the post.