Tech was Caught Flat-Footed by AI
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By Thomas Lah
Pick up any business publication and you’ll read about why artificial intelligence is a game-changer. A recent Fortune poll reported that 79% of CEOs polled expect generative AI investments to increase their business efficiency.
Many pundits expect AI to reduce operational business costs by 30% or more in the coming years. The question is how quickly and efficiently can a company make this move.
The trending concept to learn is the idea of an AI-native company, and why this is the desired future state for many tech companies. Becoming an AI-native company requires this new technology to be a core business model component, with every internal workflow designed with a lens toward exploiting AI capabilities. The paramount company principle must be to establish automation and efficiency through AI, and management must aggressively prioritize data and analytics-driven decision-making.
Are there a large group of emerging AI-native companies? No, there aren’t. However, over three-quarters of existing technology companies are already investing in AI solutions.
Since last summer, my discussions with technology executives have focused on the state of technology business models. In each of those meetings, we’ve veered into the state of AI capabilities. I’m disappointed to report that more than three-quarters of these companies lack a mature strategy and organization to support their investment in and deployment of new AI capabilities.
In 2023 alone, we saw a rapid evolution of perspectives on how to embrace AI in the tech sector. We now know that technology companies that migrate toward an AI-native posture are going to be better positioned to compete effectively in their marketplaces, and the most successful are likely to dominate. Those are the high stakes facing tech leaders today.
AI is already disrupting many industries, however, knowledge-worker-intensive industries—including tech— have the greatest potential for unprecedented disruption. Today, our research team has identified more than 70 potential use cases for AI within technology businesses. Most of those use cases can improve companies in three key ways:
• Data-driven decision-making
• Efficiency and cost reduction
• Enhanced customer experience
It’s important to understand these benefits.
Data-Driven Decision-Making: AI can propel informed, data-driven decision-making across a business. But, any artificial intelligence capability must have a stable, company-wide data science and analytics function. AI can analyze vast amounts of data quickly and accurately, providing enormous insights for decision-making. Accordingly, data-driven decisions will likely lead to better outcomes, improved competitiveness, and increased profitability.
Leveraging this dimension of AI demands specialized data science and data analytics functions. Simply, data science helps an organization understand where data-driven decision-making could be applied. Then, data analytics helps organizations understand where data-driven decision-making should be applied
Efficiency and Cost Reduction: AI can automate some tasks, streamline operations, and optimize resource allocation, which are tasks previously only effectively performed by human beings. These functions are primarily successful through machine learning (ML) and robotic process automation (RPA).
Through ML, enormous volumes of data are used to train algorithms leveraging various approaches, identifying patterns where the process becomes known and the outcomes become expected. Then, RPA applies the ML data to automate tasks with better efficiency and fewer errors.
Enhanced Customer Experience: Finally, AI can effectively engage customers and deliver high levels of customer satisfaction. Natural language processing (NLP) and Generative AI are quickly becoming helpful tools to enhance customer satisfaction.
NLP solutions focus on interactions between computers, humans, and all human language. A digital system can understand, interpret, or act on spoken or written information by recognizing this information as data.
Generative AI is an emerging tool powered by artificial intelligence that leverages the previously mentioned aspects of the technology. Leveraging data science and machine learning, AI can be used to create content that is often indistinguishable from human-generated content.
These tools are regularly fused to power chatbots, virtual assistants, and recommendation engines with a high level of personalization that would otherwise be too cost-prohibitive to deliver in modern business models.
Based on these three areas of business improvement, we can see how AI is changing tech companies operations.
Harnessing AI for tasks like predictive analytics, product development, and marketing automation, B2B firms are starting to differentiate themselves from competitors and offer innovative solutions to their clients. There are already several companies that provide AI-driven competitive analysis capabilities as a service! This is an analysis that surpasses any level of service an existing competitive analysis team could hope to complete using their current staff.
AI will also have the ability to impact a company’s scalability. It is much easier to change bits than atoms. In other words, scaling software capacity is much easier than hiring, training, and deploying human capital.
This technology will also be used to identify and mitigate risks in supply chains, financial operations, cybersecurity, and the impact of new market entrants. A data-driven, proactive approach to risk management will be a game-changer for leadership teams, while not having this capability in place could prove disastrous.
Additionally, AI can help companies comply with changing regulations and standards in their primary industries. Businesses can reduce the risk of costly penalties and legal issues by automating compliance checks and reporting.
Of course, this is not an exhaustive list of how AI will advance tech business models. However, the list is long enough to support our main assertion: AI is beginning to fundamentally disrupt the operating model of technology companies. How is your company navigating these changes?
Thomas Lah is the executive director and executive vice president of TSIA, the leading association for today’s technology and services organizations. For more than 20 years, he has been helping many of the world’s largest technology companies improve the efficiency of their daily operations. He is the author of Technology-as-a-Service Playbook: How to Grow a Profitable Subscription Business (2016) and Digital Hesitation: Why B2B Companies Aren’t Reaching Their Full Digital Potential (2022). Lah also hosts TSIA’s podcast, TECHtonic: Trends in Technology and Services.
