Artificial Intelligence (AI) is having a renaissance moment these days. With the risk to disclose my age, I can remind to my readers about “expert systems” and Prolog programming and pattern matching back in 1980s. Investors are getting more excited about AI these days. Check out my last year article – Future PLM AI machines – a drew for top VC firms.
The complexity of product information is a place where companies investing in deep learning can find some excitement moments – Future trajectories of PLM and AI platforms. The complexity of product information, manufacturing processes and supply chain will be growing in the next 10 years. So, the demand for “intelligent” platforms capable to make analysis and help people to make decisions. Future of intertwined intelligence will come in the intersection of product lifecycle systems and AI platforms. It is a moonshot, but something worth thinking about. Remember, when Sergey Brin was talking about indexing the whole web back in 1995, people were laughing on him.
My attention was caught by Forbes article – Top 10 Hot Artificial Intelligence (AI) Technologies. The beginning of the article brings some interesting data points about how AI technologies are used by enterprise companies and startups.
The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.
Future in the article you can read about segments of AI development and specific opportunities. Have a read and draw your opinion. I was looking for those technologies and opportunities that can drive interest of engineering and manufacturing software vendors. Two of them are resonating – Decision management and text analytics. Here are passages I captured from Forbes article:
Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath.
Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify.
Although it is not exactly connected a combination of decision management and NLP technologies can empower the development of smart PLM systems. Those systems can be capable to hide complexity of engineering and manufacturing processes. Think about change management decisions, service optimization, customer quality programs and maybe others. All these application domains are heavily dependent on structured, but mostly unstructured information logged and stored by enterprise companies.
Another area of application can be heavily connected to IoT. Although IoT is overhyped these days, PLM and IoT are experiencing a very interesting moment of growth. Read more here. Future IoT platforms can use lots of AI technologies and might have interesting applications in engineering and manufacturing.
What is my conclusion? Engineering and manufacturing companies can be a future goldmine for AI technological vendors. These companies are full of legacy software and data and can be a sweet spot to test some of AI technologies. At the same time, manufacturing business is very slow market. Will it create an opportunity for AI vendors? Absolutely. Is it a right timing for AI firms? I don’t know. If you familiar with AI projects and manufacturing, share whatever public information you have – it can help tech vendors developing technologies these days. Just my thoughts…
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Disclaimer: I’m co-founder and CEO of openBoM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.