Want to adopt AI? Build a managerial culture of clarity

When running a business using best practices from the offline world, many inconvenient facts may go unsaid, but are still understood. Similarly, crucial details that can make or break results 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 enterprises adopt AI technologies at their core, this informal operating culture becomes a liability. AI requires a more structured, explicit and data-driven approach to decision-making.

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

Is this only true when we adopt AI? Not. Any business that relies on a “one-size-fits-all” strategy will struggle, regardless of technology. However, AI amplifies the consequences of strategic misalignment. For example, armies that optimize costs in a military conflict end up bringing knives to a gun fight.

In this post, we’ll explore three traditional business strategies—win at all costs, optimize costs, and balance trade-offs—and examine how the introduction of AI fundamentally changes each approach.

Let’s discuss three different business outcomes:

Win at any cost (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 some such situations:

• Transitional income: Disney has 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 has become the dominant video sharing platform by integrating user-generated content with a powerful recommendation algorithm. This helped it overcome competitors like Vimeo and Dailymotion, establishing its undisputed dominance.
• Talent acquisition: Spotify has expanded its podcasting division by acquiring top talent, including the Joe Rogan Experience and The Ringer. Similarly, Facebook’s entry into AI was driven by the acquisition of Yann LeCun, a renowned artificial intelligence researcher.
• Securing exclusive partnerships or supply chains: From 2017 to 2022, Star India fueled Hotstar’s growth by acquiring the exclusive broadcast rights of the Indian Premier League.
• Reducing supply risks: Netflix has spent billions on original content to reduce its reliance on third-party licensing deals, moving away from popular shows like Friends and The Office.
• The Race for Regulatory Approvals or Market Entry: Pfizer and Moderna have battled to be the first to get COVID-19 vaccine approvals.
• Acquisition of strategic companies or technologies: Facebook’s acquisitions of Instagram and WhatsApp have locked out competitors in the social media market. Microsoft’s strategic investment in OpenAI has given it an edge in competition with Google.
• Winning long-term contracts: Lockheed Martin’s $1 trillion F-35 contract win ensured cash flow for 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 given issue 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 must:

• Spend more than the competition in areas such as marketing, research and development and strategic procurement. Competitors should think twice before trying to compete, and those who do should ultimately lose in the war of attrition.
• Take the fight to competitors’ marketsas Apple did when it disrupted the Nokia smartphone market. After securing its existing revenue from laptops and music players, Apple raised growth capital to challenge Nokia’s dominance of the smartphone market. Nokia has had to catch up with Apple’s R&D and marketing spending while grappling with declining revenue from its feature phone division.
• Have strategic clarity to cannibalize your own revenueas Apple did with the iPod to augment the iPhone.
• Beat the competitionas Tesla did by opening up his patents. This set off a prisoner’s dilemma among existing car companies: if they didn’t use the patents, their competitors could, putting them at a disadvantage. Eventually, everyone started working on electric vehicles, which increased the EV market share and made Tesla the dominant car company in the US.

What about the role of AI? AI could be one of the technologies to invest in when you’re outspending your competitors. However, in most other respects, AI and machine learning cannot replace strategic planning.

Cost optimization (efficiency)

Cost optimization is the process of strategically reducing expenses while ensuring that 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:

• Automate repetitive or boring tasks: Using AI for translation and transcription to produce subtitles and dubbing in different languages
• Increasing operational efficiency: Replacing reporter expenses with a centralized inbound office that relies on contributions from live TV, newswires and social media for news coverage.

While AI can bring substantial cost savings, it is 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 become table stakes. Consulting companies often excel at identifying and disseminating these best practices.

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

Frequently Asked Questions:

Why it matters: Like modern retail, media is often low margin, low average revenue per user business. With this in mind, cost optimization is provided.

What is the role of AI in such problems? As industries invest more in AI, companies will increasingly be expected to see AI as a mainstream investment (a necessary cost of participation) to operate the business.

What is the relationship between “Win at any cost” and “Cost optimization”? Pre-optimization is a sin. Once the strategic game has been won, 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 “I can’t win dynamic games with static trading rules.’

For example, large language models (LLMs) could be used to mass-produce variations of existing content in order to flood search and social markets and increase programmatic ad revenue. Over time, these platforms’ algorithms are likely to detect the near-duplicate nature of AI-generated content and reduce it in search results or social feeds. In addition, the increased supply of similar ad inventory could reduce CPMs (effective cost per thousand impressions), ultimately reducing expected revenue gains.

Other problem statements include:

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

In such problems, we must recognize the inherent tension and optimize within the 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. Therefore, at the heart of Facebook, Google Search, Google Ads, Amazon, etc. these systems are located.

Algorithms can help navigate these trade-offs, similar to Nassim Nicholas Taleb’s Barbell Strategy: allocating most resources (85%) to safe bets with stable returns, while reserving some (15%) for high-risk experiments and with high rewards. Once the high-risk-reward experiment is proven, then increase it to the safe bet pool (85%) and start a new experiment in the 15% pool.

Conclusion

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

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

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