As we explained in our previous article, Small and medium-size enterprises (SMEs) have been considered the lifeblood of the Global economy for many years. Despite of that, the limited size of many SMEs means they have difficulty accessing capabilities and resources that would make them more productive, including talented individuals with the latest knowledge of technology, finance, and managerial practices.
On the other hand, antitrust crusaders have built up serious momentum in Washington, but so far, it’s all been theory and talk. Groups like Open Markets have made a strong case that big companies (especially big tech companies) are distorting the market to drive out competitors. We need a new standard for monopolies, they argue, one that focuses less on consumer harm and more on the skewed incentives produced by a company the size of Facebook or Google. Recently, European Union regulators have filed antitrust charges against Amazon, accusing the e-commerce giant of using data to gain an unfair advantage over merchants using its platform.
There are many actions that could be addressed by SMEs to improve the productivity, competitiveness and sustainable growth. Indeed, SMEs could accelerate the digital transformation process. First, integrating proven practices and technologies is faster and safer than testing new ones, and SMEs have a large adoption gap to close. In the same way that emerging markets can grow faster than high-income markets by adopting tested technologies, SMEs can grow faster than large companies by adopting the proven technologies and practices of larger enterprises. Second, start-ups, which are a critical subsegment of SMEs, have become important sources of innovation. Because they are unhindered by legacy systems and outdated strategies, new market entrants are often able to rethink established practices and cut through traditional industry boundaries.
If we are talking about innovation-centric approaches, Machine Learning (ML), Artificial Intelligence (AI) and process Automation will be present in almost all the new solutions released to the market on 2021. Especially, ML- and AI-based solutions have been a sector with an exponential growth during the Covid-19 pandemic. The enterprises have been seeking new routes to efficiency, organizations with an abundance of furloughed or fired workers have bandwidth crunches, and some companies may have used the hardships of the past year to clean house with the intent of hiring fresh using new technology-driven strategies.
In 2021, we will see the rise of The wave of AI-enabled digital transformation will expand from “early adopters” such as financial services, insurance, and manufacturing to all other industries, and AI and machine learning will be embedded into multiple business functions, across key business areas to not only drive efficiencies but also to create new products and services. Another clear trend rising on 2021 will be AutoML 2.0 platforms that take “no-code” to the next level and finally begin to deliver on the promise of “one-click” no-code development with an environment that automates 100% of the workflow, due to organizations will be continue facing an increased pressure to optimize their workflows, more and more businesses will begin asking business intelligence teams to develop and manage complex AI/ML models.
Tech SMEs should take advantage of these trends as a clear market need for businesses to invest in AI-based technologies that help them accelerate and democratize the data science process. This has given rise to what some call “no-code” AI platforms are workflow-driven, visual drag-and-drop tools that claim to help make AI easier for non-technical people. One of the key reasons that this is happening now is the availability of AI and ML automation platforms that make it possible for organizations to implement AI quickly and easily without investing in a data science team.
On the other side, non-tech SMEs should be also focusing on AI/ML adoption for the coming years. An article published by McKinsey Global Institute has estimated an increase of productivity growth from digital adoption of 1.2 percentage points per year for some countries, representing the main contribution to productivity growth overall. Much of the impact relies on or is enhanced by AI applications. For SMEs, the theoretical opportunity is likely higher, but the corresponding implementation challenges are also more difficult. Limited awareness of AI, limited access to digital talent, and limited capital to invest in AI applications can significantly hinder the uptake of these technologies by SMEs. Governments have started expanding their productivity programs toward digital adoption or setting up dedicated programs to help SMEs deploy AI technologies in their processes and products. Similar to productivity programs, digital- and AI-adoption programs also rely on centers of excellence and model factories for demonstrations. This process allows companies to identify opportunities for improvement and implementation at their facilities. Moreover, SMEs will learn how to roll out progressive human capital practices and job redesign to augment their transformation.
As many other predictions, this should always be taken with a grain of salt, but without doubt Covid-19 pandemic has created an unusual opportunity for AI-based technologies adoption aimed at efficiency, competitiveness, adaptability and productivity. In future articles, Xymbot team will continue covering the digital transformation trends, especially on SMEs sector to highlight the best practices coming from the market in the year ahead.
Furthermore, we recommend consulting the following literature to continue your digital transformation journey:
- Designed for Digital: How to Architect Your Business for Sustained Success, MIT review
- The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives, by Simon & Schuster
- Artificial Intelligence: The Insights You Need, by Harvard Business Review
- The Year in Tech, 2021: The Insights You Need, by Harvard Business Review
- The Deep Learning Revolution, by MIT Press
- Competing in the Age of AI, by Harvard Review Press
The objective of this blog is to provide a personal vision of how digital transformation trends will be impacting in our daily activities, businesses and lifestyle.
Industry 4.0 and Smart-mobility expert, his research interest includes Industry 4.0, Smart-Maintenance, Process Optimization, Machine Learning, AI engineering and Cloud-based solutions.
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