Generative AI Strategy Primer for Executives

Despite the excitement around generative AI continuing to build, software firms have been adjusting their revenue estimates and growth projections downward this year. This shift is partly because software buyer executives are taking their time to thoughtfully position themselves for success in the generative AI era.

As a result, executives are delaying their purchasing decisions and replacing expiring multi-year contracts with annual contracts until they can develop a comprehensive strategy for generative AI-related investments. This has led to a decrease in the purchase of traditional, non-AI-forward products and services, resulting in reduced growth estimates for software vendor companies.

So, what's behind this temporary slowdown? It's simple – executives are holding back on making major commitments until they've fully contemplated their generative AI strategy.

Adoption Cycle of Generative AI

As you evaluate the potential of generative AI, you're likely wondering where we stand in the hype and adoption cycle. To put this into perspective, let's look at the journey of cloud computing, which emerged around two decades ago. cloud adoption faced resistance and concerns about security, privacy and compliance, taking nearly a decade to overcome. The mainstream adoption of cloud and SaaS solutions did not occur until a decade later.

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The generative AI landscape bears some similarities to cloud's early days. Executives are currently grappling with the security, privacy and compliance implications of feeding their data into generative AI models and ensuring safeguards against misuse and liabilities. However, there's a crucial difference between cloud and AI. Unlike cloud, which required re-platforming existing solutions, generative AI is a platform evolution that can be delivered via existing cloud infrastructures.

This distinction implies a shorter Diffusion of Innovation cycle for generative AI compared to cloud. As a result, we can expect generative AI adoption to accelerate more quickly, driven by the growing recognition of its benefits and the increasing availability of robust generative AI platforms and tools.

Understanding where we are in the generative AI adoption cycle can help you navigate the current landscape, anticipate future developments and make informed decisions about incorporating generative AI into your organization.

We are currently in the midst of the "build" phase, estimated to last around 1-2 years. During this phase, companies are proactively investing in AI hardware and infrastructure, laying the groundwork for future generative AI-driven innovation.

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In this stage, organizations are:

As a result, the value accumulation in generative AI is currently focused on the following key players:

As the generative AI market continues to evolve, we can expect a shift from the "build" phase to a "growth" phase, estimated to last around three to five years. During this phase, adoption will move from innovators to early adopters and eventually to the early majority.

This accelerated adoption pace is due to the critical economies of learning derived from digital transformation experiences, which will expedite the transition to generative AI. Companies that have successfully built AI-led products and services, such as Adobe, Workday, ServiceNow and Microsoft, will be well-positioned to capitalize on the growth phase. Cloud and Edge data center operators will also benefit as the industry prepares for massive scale.

Next, the industry will enter the "scale" phase, estimated to last around five to ten years. Here, generative AI will be widely adopted by the late majority and become an integral part of mainstream work practices. The urgency of value creation, ROI quantification and the success stories and proven deployment models of prior adopters will instill confidence in the rapid application of generative AI solutions.

AI solution integrators will also thrive during this phase, as they capitalize on the growing demand for generative AI solutions and expertise. As the market scales, we can expect to see a significant shift towards large-scale adoption, with generative AI becoming a standard tool in many industries.

As executives navigate the complex landscape of generative AI adoption, they are carefully weighing the potential risks against the opportunities for return on investment (ROI), use case prioritization and vendor selection. Moreover, they are also mindful of the need to ensure employee readiness and training, as well as cultural and change management processes that support a smooth transition.

Specifically, executives are considering the following key factors:

By carefully considering these factors, executives can make informed decisions about generative AI adoption, mitigate potential risks and unlock the potential for business value creation. The percentage of organizations reporting they were already achieving their expected benefits to a “large” or “very large” extent is 18% to 36%, depending on the type of benefit being pursued.

Applicability of Generative AI

Generative AI's scope is vast and varied, with potential applications spanning multiple industries and functions. A non-exhaustive set of use cases for generative AI includes search, content creation, predictive maintenance, personalized healthcare, financial analysis, customized education and training programs, AI outsourcing and labor augmentation, business process automation and workflow optimization, virtual assistants and chatbots and product conception.

As generative AI becomes increasingly integrated into the enterprise, its impact will be felt across various departments, including:

Companies that fail to prioritize AI adoption and integrate it into their businesses in a strategic manner risk falling behind the curve. Exponential value decay can set in as competitors seize the opportunity to innovate and disrupt the market. On the other hand, those that successfully integrate AI into their operations will reap significant benefits, such as increased efficiency, improved decision-making and enhanced customer experiences.

By taking proactive steps now, organizations can:

Value Drivers of Generative AI

The value drivers of generative AI can be grouped into five key categories:

Business Models in the Generative AI Era

As generative AI adoption continues to grow, we can expect significant changes in business models. The traditional per-seat/per-license model is giving way to recurring revenue models, particularly with the rise of SaaS solutions.

With generative AI, this trend will accelerate as the value paradigm shifts from seat-based models to pay-as-you-grow or outcome-based value sharing models. AI-as-a-service and data-as-a-service models will likely become prominent in the enterprise space, offering scalable and flexible solutions for companies. For enterprises, multi-tenant or dedicated resources will become popular models for balancing costs and specificity.  

In the investment landscape, many generative AI investors believe that the enterprise market will be larger and more valuable than the consumer market. While this may currently be the case, new device usage paradigms will emerge as generative AI promotes greater facility and productivity in daily life interactions. Consumer-focused companies will need to adapt by pricing the value that AI provides to consumers into their products or services. This will require a deep understanding of how generative AI enhances the consumer experience, as well as a willingness to innovatively bundle and price AI-powered features.

Go-to-Market, Build vs. Buy Considerations for Generative AI

In today's software landscape, getting to market and scaling product-market fit have become increasingly crucial for business growth. By adopting a disciplined approach, companies can achieve and sustain success in their target markets.

The process begins by identifying and classifying the initial target verticals for pursuit. Next, establishing beachheads in these target verticals through rapid iteration and product refinement is key. Once a degree of success has been achieved, companies should plan to expand by establishing new beachheads and iterating through the process.

Executives are also facing the critical decision of whether to invest in developing AI models organically or to purchase from industry leaders. This decision is complicated by the primacy of capabilities, costs, vendor specialization and the democratization of solution development enabled by AI. Additionally, companies must consider the value of their proprietary business data and the pricing and business model under which it is offered to customers and vendors.

In conclusion, generative AI is poised to have a profound impact on the enterprise. To stay competitive, executives must carefully design and plan their strategies on prioritization, pricing and go-to-market execution.