Oleksandr Krakovetskyi: Побудова корпоративної культури для впровадження інновацій з використанням штучного інтелекту
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Oleksandr Krakovetskyi: Побудова корпоративної культури для впровадження інновацій з використанням штучного інтелекту AI & BigData Online Day 2021 Website - http://aiconf.com.ua Youtube - https://www.youtube.com/startuplviv FB - https://www.facebook.com/aiconf
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Oleksandr Krakovetskyi: Побудова корпоративної культури для впровадження інновацій з використанням штучного інтелекту
- 1. Illustration CEO DevRain CTO DonorUA Microsoft Regional Director Microsoft AI Most Valuable Professional Microsoft Certified: Azure Data Scientist Associate Microsoft Certified Trainer The Best Professional in Software Architecture (Ukrainian IT Awards) Lector in Kyiv School of Economics
- 2. devrain.com Побудова корпоративної культури для впровадження інновацій з використанням штучного інтелекту.
- 3. devrain.com
- 4. What is innovation? devrain.com Illustration 1 document = 15 min, $5 100 000 documents = 2.85 years, $500 000 1 000 000 documents = 28.5 years, $5 mln
- 5. devrain.com • Process-centric -> human-centric • Use known approach to solve problem in another domain • Healthcare and wellbeing of employees • Technology zoo -> SSO • Communication -> collaboration • Edge innovations • Hybrid models Real but not «sexy» innovations
- 7. devrain.com • Corporate culture • Innovations • Iterative improvements • Strategic investments • AI Innovations -> Iterative improvements & Strategic investments
- 8. devrain.com
- 9. devrain.com • Corporate culture • Readiness • Innovations • Iterative improvements • Strategic investments • AI Corporate culture -> Readiness
- 12. devrain.com
- 13. devrain.com • Corporate culture • Readiness • Innovations • Iterative improvements • Strategic investments • AI • Data first AI -> Data first
- 14. devrain.com • Data Catalog • Data Lake • ETL/ELT • Common data model • BI/Big data/ML • MLOps • Model repository • Unified approach • Strategy Data culture
- 18. devrain.com • Incorrect AI implementation strategy (or its absence) • You don’t know what AI is supposed to do for you • One solution for everything • Your employees find it complicated to work with innovative technologies • Too high expectations Typical AI implementation failures
- 19. devrain.com • Ready infrastructure • Responsible role - DTO, CIO or Data Strategist • Domain experts • Data Lake • Ability to integrate new solutions fast • Open mind and ability to risk Ideal company for innovations
- 27. Create a state-of-the-art technology devrain.com Illustration Technology has a huge potential, but markets are unknown, and technology could not be applied immediately. E.g. “face swap”, “deep fakes”, “blockchain”, “shazam”. Exit strategy: if technology is good enough, it could be sold to the exciting business which could apply this technology to the existing successful product to increase awareness among users.
- 28. Win niche market devrain.com Illustration AI/ML could be unfair advantage to solve the real problem in the niche market. E.g. healthcare diabetes startups. Exit strategy: if technology performs well, it could be sold to big enterprises and market leader to improve their position or to get engineering team onboard.
- 30. devrain.com Illustration AI/ML is used to improve specific business processes without significant impact to the core business. E.g. FAQ chatbots, HR automation, marketing automation tools or standard clustering, classification, regression problems solving which remain the same across different domains. Chatty (a DevRain product) allows conducting structured job interviews automatically with chatbots and advice the best candidates. https://chatty.ai Improve company’s productivity
- 31. Become AI/ML task-based business devrain.com Illustration E.g. - Data Labeling - Tools for MLOps or MLOps services - Face recognition - Data Science - ML projects architecture - NLP-based solutions
- 32. Use AI/ML as PR/Marketing activities devrain.com Illustration AI/ML is used to highlight a company as pioneer or early adopter of innovation. In fact, the implemented AI/ML projects do not have significant advantages comparing to the standard methods.
- 33. Make AI/ML as essential part devrain.com Illustration AI/ML becomes the core part of your business on all levels and all decisions are data- driven.
Editor's Notes
- 1 Technology Trigger At this stage, the early proof-of-concept stories and media interest trigger significant publicity. But in most of the cases no usable products exist and commercial viability is unproven. 2 Peak of Inflated Expectations At this stage, a bunch of success and fail stories appears in media. Some companies take action but most don’t. 3 Trough of Dis illusion ment (ˌdisəˈlo͞oZHənmənt) At this stage, the most of implementations fail to deliver. Producers of the technology shake out or fail. Investment continues only if the surviving providers improve their products to the satisfaction of early adopters. 4 Slope of Enlightenment More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain inactive. 5 Plateau of Productivity Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology's broad market applicability and relevance are clearly paying off. If the technology has more than a niche market then it will continue to grow.