I originally published this article on my LinkedIn profile.
As the hype around AI evolves, and most enterprise companies now include AI in their 2024 strategies, the demand for enterprise-grade business applications is rising. According to IDC, 'the experimentation phase' came to an end in 2023, and 2024 will represent 'the buildout phase.' This phase signifies a period of heavy investments in infrastructure, while starting implementation and adoption of specific AI applications, primarily those related to productivity.
Time to Partner
In my view, 2024 represents a significant opportunity for AI-powered startups - but only if they act wisely before a major market consolidation that is likely to occur in the following years. As reported by CB Insights in the Generative AI Bible,' 71% of Generative AI startups are early-stage or hadn't raised any funding (as of Sep 30, 2023), making 2024 a challenging year for them from a Go-To-Market perspective. This implies that the real opportunity for most of these startups will come through platform partnerships and joint Go-To-Market strategies.
Another indication of this opportunity is evident in a recent Deloitte report, which surveyed more than 2,800 business and technology leaders. It clearly states that they are now primarily relying on off-the-shelf generative AI solutions, aimed at enhancing the efficiency and productivity of existing activities (which I interpret as simple and smaller-scope use-cases). According to the report, leaders are also seeking greater collaboration and partnerships to rapidly build generative AI expertise.
The Hype
Companies are under immense pressure to implement AI everywhere. I am particularly fascinated by the following graph, which uses data from a CB Insights advanced search of earnings transcripts (see Generative AI Bible), showing how companies' interest in generative AI has skyrocketed. Mentions of 'generative AI' in earnings calls are a clear indicator of market expectations and the pressure on senior executives to implement AI solutions. This pressure comes not only from shareholders but also from consumers who now understand GenAI and expect to benefit from it.
We observe a similar sentiment in a recent BCG report, which surveyed over 1,400 C-suite executives on the topic of AI. 89% of them ranked AI as one of their top three tech priorities for 2024. At the same time, a majority of them (66%) reported falling behind, being ambivalent, or dissatisfied with their progress in AI/GenAI realization. Will startups be part of the solution? This report identifies five characteristics that will distinguish the winners. Among these, 'building strategic relationships', including partnerships with GenAI startups, to gain access to cutting-edge technology and create near-term value.
Enterprise Backed AI
Another reason startups should prioritize partnering, and this is not new, is the expectation among companies to work with a limited number of tech vendors, especially when planning and executing their AI strategies.
Large enterprise companies prefer standardization and simplification, favouring enterprise-grade solutions offered by tech giants. This preference applies to AI as well. 'Enterprise-grade' implies reduced risk, a significant consideration now as AI technologies have been developed rapidly and on a massive scale. Consequently, some early and late-stage startups, even those that were early developers of AI, find themselves at risk. Similar to previous technological hypes, we will witness market consolidation, a winner-take-all phenomenon, acquisitions by these large giants, and a decline in startup valuations.
Large tech vendors, operating both in the physical and data spaces, along with those offering holistic solutions (combining infrastructure, business AI, and a strong partner ecosystem), will dictate market dynamics and lead the industry. To enhance their survival rates, startups must refine their partnership strategies. Securing a certified partnership with a large tech vendor often signifies that the startup's solutions and business capabilities have been validated by a more established and trusted entity. It also indicates that the solution is pre-integrated with a leading platform, there is a business mechanism for purchasing it through a marketplace or an organized partner program, and it aligns with existing business models and processes.
Industry-specific AI Applications
In my opinion, in 2024, enterprise companies will prioritize the adoption of relatively small-scope AI solutions that enrich their existing systems, platforms, and applications, rather than implementing large-scale solutions that involve complicated, long, and risky transformation projects.
These 'small', use-case-specific solutions are also more affordable from a budget perspective, as companies look to control their AI spending. According to IDC, worldwide IT spending on AI and GenAI is expected to increase dramatically in the coming years, making the reduction of AI costs a key priority.
We also see companies prioritizing the development of their workforce's AI skills. According to Deloitte, talent represents a critical area where preparedness for generative AI is significantly lacking, with only 22% of leaders considering their organization as highly prepared. Adopting third-party solutions allows companies to leverage external talent effectively, while dedicating resources to develop internal talent.
A robust startup-partner ecosystem enables large tech vendors to meet these priorities today by offering smaller-scale, industry-specific solutions from partner-certified startups, in addition to and as an extension of their own in-house solutions.
Examples of industry-specific AI applications:
Human Experience Management: Recruiting chatbots, personalized assessment, automated screening, multi-lingual video interviews, automated job descriptions, sourcing passive talent, internal talent mapping and mobility matching, employee productivity / co-pilots.
Ecommerce: Personalized marketing and customer experience (catalog / PDP / search / recommendations), sales and service bots, generation of avatars, 3D stores / products, virtual try-ons, and size matching.
Supply Chain Management: AI visual search, preventive maintenance, real-time transportation management, quality control, advanced robotics, optimizing shelves / showrooms / warehouses, inventory predictions, analysis of physical spaces.
Financial Services: Accelerate market analysis, simplify decision processes, improved forecasts, advisory bots, accurate and faster personalized offers, procurement optimization.
The Bottom Line: Seizing AI Opportunities through Partnerships
AI-focused startups need to seize the opportunity presented in 2024 before the market begins to consolidate. To navigate the Go-To-Market challenges successfully, they should prioritize partnerships with established tech companies. Such collaborations can leverage the larger companies' scale, customer base, and market reach. Additionally, aligning their solutions with the high standards expected by enterprise companies—including reliability, scalability, and security—will make them more attractive to large organizations aiming to simplify and standardize their AI technology stack.
On the other side of the equation, as demand for non-core AI supporting functionalities increases, large tech vendors will need to maintain a robust ecosystem of AI startup partners. This ecosystem will enable them to expand their offerings and quickly bring to market joint AI solutions. Similar to practices in other technology domains not directly related to the AI hype, leading platforms will need to differentiate their AI strategy between core platform solutions developed through in-house R&D and certified, pre-integrated add-ons offered through marketplaces and partnership programs.
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