The product manager in the artificial intelligence world
Cloud applications, artificial intelligence, machine learning, data insights, rapid prototyping, design thinking, and faster decision making are becoming more and more significant in the daily life of the product manager. Basic physical, digital, and biological technologies are intersecting to create large scale system changes, altering the very fabric of our social system. More than ever, the product manager is faced with the challenges of proactively designing the systems of the future. This study will deal with the question: how can the product manager thrive in an artificial intelligence machine learning world?. With the advancement in machine learning, products can now significantly differ from the traditional style of product designs. One widely known example is how Google answers our questions with the best possible answers through ranking. Similarly, Netflix or Spotify suggest media to customers using the process of recommending, giving users things they may be interested in, without them explicitly searching. On the other hand, Gmail groups an email as spam or not spam through classifying. With these many possibilities, today’s product manager must understand the actual problem that must be solved to grow customer value systems in line with the company’s goals. And while product managers inspire through vision, decisions must roll downstream and be implementation-based like an assembly line. More than ever, it has become crucial for a product manager to set and manage the anticipations of users, gather measurable feedback frequently, communicate meticulously to engineers, and make sure products logically progress with market shifts.
AMMAR, H.; ABDELMOEZ, W.; HAMDI, M. S. Software engineering using artificial intelligence techniques: current state and open problems. Mississauga: Institute of Communication, Culture, Information, and Technology, University of Toronto, 2012.
ANDERSON, M.; JIANG, J. Teens, social media & technology 2018. 2018. Available from: <https://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/>. Access in: 31 May 2018.
ATKINSON, R. “It is going to kill us!” and other myths about the future of artificial intelligence. IUP Journal of Computer Sciences, v. 12, n. 4, p. 7-56, 2018.
BAILEY, G.; ALLISON, J.; PEARSON, M. A comparison of machine learning applications across professional sectors. IUP Journal of Information Technology, v. 14, n. 4, p. 7-20, 2018. http://dx.doi.org/10.2139/ssrn.3174123.
BELL, J. Machine learning: hands-on for developers and technical professionals. New Jersey: John Wiley & Sons, Inc., 2014.
BOOBIER, T. Advanced analytics and AI: impact, implementation, and the future of work. West Sussex: John Wiley & Sons, Inc., 2018.
BYRUM, J. Engineering the intelligent enterprise: augmented intelligence can’t match human thinking but can optimize business processes. ISE: Industrial & Systems Engineering at Work, v. 51, n. 1, p. 40-43, 2019.
DASSAULT SYSTEMS. Morphosis experience. 2019. Available from: <https://trends-events.3ds.com/design/content/morphosis-experience>. Access in: 03 Oct 2019.
FRANK, M.; ROEHRIG, P.; PRING, B. What to do when machines do everything: how to get ahead in a world of AI, algorithms, BOTS, and big data. Hoboken: John Wiley & Sons, Inc., 2017.
JOHNSTON, J. The allure of machinic life: cybernetics, artificial life, and the new AI. Cambridge: MIT Press, 2010.
KARP, A. Deep learning will be huge – and here’s who will dominate it. 2018. Available from: <https://venturebeat.com/2016/04/02/deep-learning-will-be-huge-and-heres-who-will-dominate-it/>. Access in: 04 Feb 2016
KHODADADI, F.; DASTJERDI, A.; BUYYA, R. Internet of things: an overview. In: DASTJERDI, A.; BUYYA, R. Internet of things: principles and pradigms. Cambridge: Elsevier Science & Technology, 2016. p. 3-27.
LIPSON, H.; KURMAN, M. Fabricated: the new world of 3D printing. Indianapolis: John Wiley & Sons, Inc., 2013.
MEZIANE, F.; VADERA, S. Artificial intelligence in software engineering: current developments and future prospects. Pensilvânia: IGI Global Disseminator of Knowledge, 2012.
NĚMEC, O.; WROBLOWSKA, Z. Requirements for applicants for the position of “product manager” in the USA. ACTA VŠFS, v. 12, n. 2, p. 125-139, 2018.
PFEIFER, R.; BONGARD, J.; GRAND, S. How the body shapes the way we think: a new view of intelligence. Cambridge: MIT Press, 2006.
PRADEEP, A.; APPEL, A.; STHANUNATHAN, S. AI for marketing and product innovation: powerful new tools for predicting trends, connecting with customers, and closing sales. New Jersey: John Wiley & Sons, Inc., 2018.
RECH, J.; ALTHOFF, K. Artificial intelligence (AI) and Software Engineering (SE): status and future trends. Germany: Dr. Jorg Rech, 2004. v. 18, p. 5-11. Available from: <http://joerg-rech.com/Paper/Rech_KI_AI-SE-Survey.pdf>. Access in: day month year.
SIEGEL, E. Predictive analytics: the power to predict who will click, buy, lie, or die. New Jersey: John Wiley & Sons, Inc., 2016.
SKJUVE, M. et al. Help! Is my chatbot falling into the uncanny valley? An empirical study of user experience in human-chatbot interaction. Human Technology, v. 15, n. 1, p. 30-54, 2019.
SWAN, M. Blockchain: blueprint for a new economy. Cambridge: O’Reilly Media, Incorporated, 2015.
WUN-JAE, K. Knowledge-based diagnosis and prediction using big data and deep learning in precision medicine. Investigative and Clinical Urology, v. 59, n. 2, p. 69-71, 2018.