FOR two decades, large enterprises have benefited from the adoption of enterprise resource planning and customer relationship management solutions while smaller competitors, constrained by cost, have lagged.1 The same has happened with AI adoption: Though the technologies delivered clear business benefits, most midsize companies took a wait-and-watch approach.2 A 2018 study found that only a quarter of midsize companies used AI or were planning to use it in the next year, versus nearly half of large enterprises.3
But cloud-based and SaaS delivery models have lowered the traditional cost barriers to software adoption, and midsize companies are utilizing AI more heavily. Deloitte’s latest State of AI in the Enterprise survey finds 80% of midsize companies intending to increase their annual AI investments, against only 57% of very large enterprises.4 Moreover, they are outpacing their larger counterparts in implementing cloud-based AI such as data science and machine learning platforms, automated ML, and AI transparency tools and systems (figure).5
Given the current fluid environment, midsize and small companies are increasingly using AI and ML for digital transformation.6 Demand for AI-as-a-service is growing, and the ecosystem is expanding.7 With wider availability of such flexible consumption-based IT models, midsize companies are poised to optimize their IT spending.8 As cloud and as-a-service models make AI more affordable and available, they are not only catching up but strengthening their competitive position vis-à-vis larger competitors.9 With affordable access to cloud-based business applications, midsize companies will likely continue to adopt new technologies and innovate faster. Cloud also offers the agility to adapt to changing customer and end-market demands.
Embed AI in all business applications. As cloud is proving to be a top driver for midsize companies’ growing use of AI, the next frontier for tech software providers is to embed AI everywhere across digital cloud-based business applications, such as customer relationship management, enterprise resource planning, supply chain management, and human capital management. And leaders should consider this when designing the business applications and architecture of the future.
Take an ecosystem-based approach. Building AI platforms and supporting developer ecosystems to create and deliver smarter apps that learn and provide more business insights for midsize companies can help tech software providers to capitalize on this lucrative segment more effectively.
Identify business cases for AI. Cloud-based models require large companies to shift their business software adoption strategy to sustain their competitive edge. Here is where tech software providers can assist large enterprises to identify AI-enabled digital innovation opportunities by building on the latter’s established customer base, partner networks, and product portfolio.
For technology software industry executives, the two vital customer segments—midsize and very large enterprises—need distinct strategies and considerations to serve them better.
The authors would like to thank Susanne Hupfer, Sayantani Mazumder, and David Jarvis for their help with data analysis and validating the key inferences and Jeanette Watson for her review support. They also thank Negina Rood for her research support.
Cover image by: Viktor Koen
