February 14, 2024
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5 min
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Cementing The AI Business Model Foundation

Adopting AI technology into modern business models is one of the most significant industry evolutions we’ve experienced since the advent of the internet. Without question, AI has fundamentally disrupted the operating model of technology companies everywhere—and it shows no signs of stopping.

Early predictions about AI’s potential for disruption include dramatic improvements in business efficiency. Our research has already documented case studies where companies have reduced the operating costs of departments by over 40%. Additionally, VC firm Battery is projecting roughly 30% in S&M head count savings for software companies. My organization has already identified 70 potential use cases for AI applications to advance modern tech companies. The rush to embrace AI has unquestionable merit, but the haphazard approaches being taken by most tech companies, as well as the mistakes being made, should provide ample reason for concern.

Technology business leaders need to reassess their approach to AI and focus on building a solid foundation for their business’s future.

The AI-Native Model

For most tech companies today, becoming an AI-native company is imperative. Achieving this goal means that these companies need to successfully integrate AI into the core of their business operations, leveraging the technology’s potential across their entire organization. The fundamental principle guiding these companies should be the establishment of automation and efficiency through AI, with leadership prioritizing data- and analytics-driven decision-making.

The result? These AI-native companies could be far more efficient than current businesses, able to leverage large datasets to provide a vastly improved product.

Unfortunately, evolving into an AI-native model isn’t an easy path to navigate. The path demands significant business model changes, and the fact of the matter is that almost every tech company is still only at the starting line of this evolution.

I’ve personally met with and surveyed leadership teams from dozens of successful tech companies currently working to get their arms around their own AI progression. One thing is apparent to me: The industry simply wasn’t prepared to begin adopting AI in 2023. However, there is some good news: The core mistakes being made by companies evolving toward AI-native models are both consistent and fixable.

Developing A Strategic Approach For AI Implementation

According to findings from Gartner, 80% of business executives believe AI "can be applied to any business decision." Investment in the future will likely be robust. However, lasting success with AI’s implementation will likely come from a cohesive approach.

1. Embrace data.

First, every leader needs to embrace organizing and centralizing data. Data is the fuel that drives AI, and as such, the days of siloed datasets are over. All departments in a business need to be working toward the same goals, and AI tools should be working with all of the data to advance that mission accordingly.

2. Craft a clear strategy for adoption.

Next, many companies need to prioritize both their AI investment and strategy. There needs to be a member of the leadership team who owns the company’s AI strategy as a fundamental business practice.

Remember, the goal is for AI to help the company achieve goals while driving cross-functional efficiency. A lack of clear oversight or direction from senior leadership hinders the potential that comes with AI’s implementation and ultimately demonstrates that the technology itself is simply not a focus for the business. A clear strategy for AI’s use is arguably even more critical. What are you trying to achieve with this solution? It’s easy to say that your company wants to save money with AI, but how?

At its core, AI’s adoption and implementation isn’t a single-department solution. Because it’s designed to crunch data, analyze results and help drive business efficiency, it’s imperative that strategy and goals also cross business silos. Furthermore, as disparate AI projects are implemented, business leaders must ensure these projects operate collaboratively to learn from one another.

3. Prioritize human capital.

In the coming months and years, AI could significantly impact human capital, but make no mistake: This is not code for layoffs. Rather, it’s a call to begin thinking about using humans differently in business.

As AI continues to grow, new company challenges and opportunities will arise. For example, in customer success, AI can better track product implementation, identify common stumbling blocks and deliver resources to help the customer overcome these challenges. However, AI won’t be able to replace the human relationships and insights garnered from interactions between a customer service professional and the client. As a result, customer service professionals could have added efficiencies, allowing them to manage more clients and focus on more important issues.

While this might sound like an appeal to eliminate human capital, AI can also identify opportunities to expand and improve the business. As a result, redeployed human resources will be necessary to advance a far more efficient and robust company.

Evolving Into An AI-Native Company

This year, the tech industry will likely continue to embrace the AI revolution, and as such, business leaders need to learn from their early mistakes and build a solid foundation to become AI-native. The question remains: Now that the first quarter is underway, how are you improving your AI investment?

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