ARTIFICIAL INTELLIGENCE AND MARKETING

Artificial intelligence (AI) has grown in recent years in many fields: research, industry, pharmaceuticals, automobile and education. In marketing, AI has also been effective. The aim of this article is to explore the usage and effect of AI in marketing. The authors posed two study concerns which fields are applied to AI marketing and how AI impacts marketers. The authors carried out secondary data investigations with samples of AI used for marketing purposes in order to address these queries. The review of the illustration given demonstrates, while the implementations are manual, that AI is broadly integrated in marketing. This may be a result of the latest technologies being implemented cautiously and experimentally. Uncertainty about the AI implementation outcomes can influence the caution when applying these developments. In the position of marketing managers, the emerging AI assistants often participate as the results of the survey reveal that they are becoming an increasingly valuable delivery platform such that products are increasingly important. The findings of this analysis show that improving the AI assistants would also be important. The benefit of competition is therefore reduced to a hygiene consideration inasmuch as the search engine optimization is optimised and saturated for each operator. Brand design would once again become an essential criterion for customer decision-making. Moreover, because consumer data collection is an essential component in the usage of AI technology in marketing, the results demonstrate that marketing managers strive more and more to enforce consistent customer policy. Furthermore, the growing usage of AI technology often requires sufficient technical expertise from marketing managers. For example, in both facets of the marketing mix, AI influences both the distribution of customer value and the marketing and management. In specific proposals for marketing AI deployment, innovation design and perspectives into how new capabilities may be incorporated into the new technology marketing team have consequences for the business.


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to revenue growth. The latest survey of 50 brand managers from organizations that operate actively with AI made by the same organization shows the most advantages that machinery training has in applications from search to consumer segmentation, targeting and recommending engines to the programme, to business forecasting ( Fagella 2019). It is crucial that new technological possibilities and IT businesses in industries result in enhanced demand by customers (Bus, Ti and Rt, n.d.). Therefore, research analysts should be able to discuss computer education and other AI techniques as well as the current knowledge on the marketing industry (Wedel & Kannan, 2016, p. 116). All this involves communications managers who are familiar with corporate strategies, technology and analytics (Wedel & Kannan, 2016, p. 116). In a global survey 79% of CEOs state they are committed to investing in skills or preparation to improve machine learning marketing productivity As a part of this relentless transformation (Schrage and Kiron, 2018b).

I.II Objective
The purpose of the analysis is to determine in a marketing combination the reach of the AI framework and to search:  Marketing technologies in all areas of AI (voice, text, image recognition, decision-making, autonomous vehicles and robots);  What sort of effect does AI have in the sales practice? Hanssens and Pauwels (2016, p. 173) argues that not only is CMOs financially accountable for bad performance. Whitler&Morgan (2017) mostly submitted that this was due to the dangerous location of the CMO, which contributes to only 22% of the working explanations showing how or how the CMO is measured. Because of the absence of CMO-rolling, just 22 percent of the job needs would suggest how the CMO would be measured or retained. He believes in a number of weakly associated indicators of success -mood, actions and finance -that Hansse and Pauwels (2016, p. 187) focus on since marketing is complex. This prevents the importance of marketing from being identified and thus contributes to the loss of confidence and a reduced marketing emphasis in high levels of decisionmaking (Hanssens&Pauwels, 2016, p. 187). A marketing value assessment is therefore necessary if marketing should play its part in the business management, from the pressure of short-term tactical choices to the organization's strategic growth (Whitler& Morgan, 2017). However, the central marketing responsibility for Webster (1992, p. 14) was defined by Christine MORman (CMO Survey 2019, p. 49) to make sure that every part of the group is focused on providing superior value to the consumer in a competitive environment: brand, digital marketing, advertising, social media, media, promotion, placement, customized marketing. Marinchak et al. are also involved. (2018, p. 22) notes that a fully digital, AI-enlarged, automatic mechanism as well as when and how to and from whom to purchase are rapidly available. In fact, the position of marketing managers seems to be changing significantly and the roles of technology are different. The accelerated growth in the data quality and quantity through emerging measurement solutions has been a positive improvement in marketability assessment (Hanssens&Pauwels 2016, p.187), allowing marketing managers to make the use of a range of AI technology possible.

II.IArtificial Intelligence Overview
This is the product of artificial intelligence. It is usually used interchangeably with notions like robotics or robotics. Machine learning or implementing algorithms often seem to be misunderstood. Oxford Dictionary states that AI is "artificial intelligence | Definition of artificial intelligence in English by Oxford Dictionaries" An artificial intelligence device can mimic the cognitive activity of the human mind (Syam, Sharma, 2018). AI's job is to store, classify and process the acquired information. This artificial intelligence system operates in specific field and carries out its duties as directed by its creators (Shanahan, 2015). The second type of IA is intelligently competitive with the human brain (Sterne, 2017). AI has little current ability, and it will accomplish tasks for artificial learning, deep learning, and natural language processing. AI is higher than the standard in a predefined collection of rules for machine investigation (ML). The algorithms used with AI to date have therefore changed in their role. ML allowed computers to collect their own knowledge by connecting each data item to each other. With this ability, ML makes it possible to achieve assumptions and generalizations through studies (McIlwraith, Marmanis, &Babenko, 2017). ML is available in various ways,

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including trends, statistical processing, data exploration, information development, predictive analysis, data processing, adaptive systems, self-organizing systems, etc (Domingos, 2016). Deep learning (DL) is higher ML, since it is based on manually managed algorithms. DL allows you to decode easily by using big data and processing resources (for example server farm, capability for CPU, cloud computing) and immediately include the results from new knowledge (Alpaydin, 2016). A natural linguistic processing (NLP) software, ML and DL, aims at understanding languages. For several years of study in this field the creation in context, vocabulary, syntax and semantic meaning of large amounts of data (text samples) (Alpaydin, 2016). AI was created by advancements in the areas of speech, text, image recognition, decision-making and independent robotics and vehicles. Real applications may be reached for both of these fields. Voice recognition is possible on devices such as iPhone (e.g. Siri, Google Assistant). Text recognition tools are used to give direct, informal answers (e.g. Deakin University and IBM Watson). Paying system should verify faces in order to pay for them (e.g. food chain KFC). The policy framework is accessible to the educational community -IBM Elements provides teachers with a structure to analyze the student and provide the recommended human development course for each student. In the factories, individual robots and vehicles eventually handle the supplies (e.g. in Amazon Kiva system).

II.II Marketing mix
The American Marketing Association agreed in 2013 to a revised edition of the marketing concept. The association states that the fundamental element of marketing is consumer value -"What is marketing?"the definition of the marketing -AMA" (Grönroos 2006). Value can reflect various product facets, including products, concepts, resources, knowledge or any solution that meets customer requirements. McCarthy suggested that 'marketing' should be a strategic structure that would include marketing planning (Bennett, 1997). While marketing is not a statistical hypothesis, long-term and short-term marketing tactics can be implemented (Palmer, 2004). McCarthy has previously refined the idea of achieving the objective demand in Borden. The Borden's 12 components (inventory preparation, sales, branding, sales networks, personal marketing, publicity, promotions, packaging, display, operations, physical management, informatics and analysis) were divided into 4 components: product, nature, promotion and venue known for their name. Borden has a wide range of information. More progress has been made on a marketing blend, for example inclusion of more individuals, processes and physical evidence (Booms, Bittner, 1980).

II.III AI and its implications for marketing
The participants in the Dartmouth conference considered that a theory could be used by a machine to replicate a learning element or some other intelligence function (Epstein, 2015, pp. 36-37). This shows that computers are otherwise concentrated in performing human activity. Increasing hybrid intelligence by integrating machines and people, new intelligences of combining human beings, machines and networks and sophisticated systems of intelligence incorporating individuals, enterprises, physics and cyberstructures, for instance. AI dreams have since been developing (Pan, 2016, p. 410). The research focuses on the ability of AI to "the right thing" with what she knows. AI is a logical actor (Russell &Norvig, 2014, p. 1). Nilsson's AI (1998, Russell &Norvig, 2014, p. 2) has thus been described as "Intellectual actions of objects." Furthermore, Nilsson's AI (1998, Russell &Norvig), which is a restricted IA, is specifically concentrated on the so-called limited IA in which we are experienced in the least in a certain field (Ayoub& Payne, 2016, p. 795). (Russell &Norvig, 2014, p. 27). In addition, if the general phenomena of AI is understood and no technological or methodological requirements are required for the AI study, the aim should be to use a more accurate language to avoid ambiguity. Or the organizer and cook of the Dartmouth congress, John McCarthy, put it as follows:" Nobody will call this AI once it functions"(Vardi, 2017, p. 5). Machine learning can be defined as "optimization of performance criteria through programmation of computers using exemplary information or past experience" and may have gained greatest exposure in terms of business and marketing (

II.IV Research gap
According to Wedel and Kannan, few marketing scholars had addressed numerous marketing AI technology up to 2016. (2016, p. 102). More research is also needed from the Marketing Science Institute (MSI) where several of the 2018-2020 study objectives are related to IA in marketing (MSI, 2018). Few articles continue to explore how the function of marketing managers is changed by AI, as literature at the intersection of marketing, decision making and IAs is examined. Surveys show that AI is extensively used in different applications, but their deductive perception is insufficient of how marketing systems have been formed, as BrightEdge, 2018; CMO Survey 2019. Their perception is not sufficient. The managers' needs to respond to the change in AIs were indicated by Jarrahi (2018, p.12). The author is also of the opinion that employees who are humanely determined must continue to improve their understanding of how AI can support their enterprises and their own strategic benefits such as empathy and eQ in the symbiosis between humans and machines. These results show how brand managers can face AI, but generally, the essay does not apply in the field of marketing in general. Moreover, Wedel and Kannan (2016, p. 116) analyze the marketing analysis objectively, indicating that competitive Research Article marketers in the sector must be well printed, as well as marketers with sufficient technology and research expertise. The authors note, in addition, that organizations should recognize the use of appropriate intuition and supervision in information and management for automatic marketing decisions. The demands of marketing managers in Wedel and Kannan (2016) were thoroughly shown by the proliferation of marketing research opportunities but the AI revolution covering also other fields, such as AI assistants, does not give any indication of the whole position of the marketing managers. Marinchak et al. (2018, page 22) reviewed early expressions of the effect of AI on marketing governance and said that AI changes its rules, roles and tools for marketing. As the results are not empirical, but instead news stories and other non-academic outlets, it is essential to empirically examine the position of marketing managers. On the other hand, Dawar (2018) interviewed experts and managers in the market, claiming that B2C companies should expect IT aid providers to dramatically shift their consumer relations. The author proposes also key marketing performance variables directed at AI assistants, but the joint advertising of machines and humans is not adequately taken into account as he only examines the role of AI assistants' marketing management. However, the author has difficulty concluding how brand managers handle this complex world of industry. The new field of AI seems thus recognized for its marketing and decision-making relevance. To our understanding, previous studies did not sufficiently discuss the continuous transition with a systemic view of the marketing managers' position. The aim of this research is to identify what value managers relate to and cannot accomplish in the marketing sector by analyzing the goals and goals of all marketing managers working with AI and AI marketing experts. This paper also explores how AI affects interactions between business and consumer as well as challenges in AI adoption and how AI can automate and improve decision-making in marketing departments. This study seeks to learn how marketing managers are positioned by increasing the capacity of AI. We shall therefore conclude on the following question.

III. Research Methodology
The objective of this paper is to review AI market implementation in terms of recent trends and features. This report hopes to discover the application of AI and to discover answers to the questions of the analysis. Marketing activities for the advancement of artificial intelligence system technologies including speech recognition, text conversion, image recognition, decision-making, person, highway, and robotics. In order to resolve these issues, the writers have decided to carry out data review of AI queries. Thus, two moves were taken in the way of AI submission. First, you need to decide which marketing portal fits you best (www.marketingweek.com, www.adweek.com, www.warc.com). Next step included validating the examples using information included in the companies' home pages or news releases. While in the location of the research, the secondary analysis asked an illustration of an AI inquiry. Our findings include the writers' interpretation and evaluation of findings. Six interviews, three with marketing management, one CMO, one CRM and one brand manager, all with AI experience at their respective marketing departments have been conducted in order to collect qualitative data for this study. In addition, three interviews with experts, one expert and the other two co-founders of a marketing firm were conducted at the AI and Marketing Intersection. The purpose was, therefore, to provide a holistic view on the core sector by marketers and AI experts at the intersection between AI and marketing. This interview object configuration Therefore, marketing managers shared with each other what they plan to contribute, what qualifications and the impact their roles on the increase in AI capacity will be used to achieve their objective. In addition, through AI expertise, and AI's current and future believed capabilities, we were able to provide insight on how the AI can and will automate and/or enhance decision-making in the marketing departments. Furthermore, we have avoided possible distortions of marketing managers by interviewing AI experts in addition to marketing managers because of their potential emotional link to their future functions. This study includes using sampling technologies described as "widely used for qualitative research to identify and select information-rich cases for the most effective use of resources.

III.I Qualitative analysis
According to Bryman and Bell (2011, p. 571), one of the greatest problems in qualitative research is to produce a broad and often unstructured dataset quickly, where it is impossible to find an analysis. It is therefore important that the wealth of information collected is not too captivating (Bryman& Bell, 2011, p. 571). There are few acceptable methods for qualitative data analysis but generic approaches compared to quantitative analyzes are available (Bryman& Bell, 2011, p. 571). A topical analysis of a number of subcategories was carried out in this study, but all of these involved the identification and application of the data collected (Bryman& Bell, 2011, p. 571). We took three steps in this thesis: condensation, data display and final drawing and verification as described in Miles, etc (2014, pp. 31-32). Firstly, the transcript data for interviews were condensed by fraging the text and allocating it to labels in which we abandoned and tuned the theores of the predefined labels to improve the structure and to avoid overlapping. Secondly, the information was synthesized by label, the

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interviewed's responses and whether the interviewee was an AI expert or a marketing manager. Thirdly, the views of the marketing managers and the AI experts were comparated. The data were compared and our findings reached and the patterns and opposites existed. These conclusions were re-examined and the theoretical framework was interwoven. These conclusions were examined again.

III.II Quantitative analysis
The questionnaire was built based on the following comparison criteria to detailed study goals on Indian consumers' acceptability of AI:  Willingness to use AI to tailor services  Facility to use AI  AI's latest adoption Service personalization relates to user preferential customization of services and AI implementation refers to the consumer's approval of AI. The survey was carried out across the centuries (age group of 22-37). Millenniums have been selected as early adopters of new innovations and potential users of the survey. 302 responses were received, 26 outliers and, consequently, 276 responses were analysed. Sampling comfort has been used. The extracted data has been processed using a method named SPSS. In the preliminary portion, the questionnaire contained age, sex, income and internet use details while in the other questions the agreement or difference with regard to the implementation of AI was reported in customer care systems, online shopping portals and virtual aids.

III.III Hypothesis
In knowing the user demand and tailoring the service based on it, AI allows you to have a more customized service experience. Researchers need to consider the readiness of Indian customers to personalize their services before knowing the effect that adoption of AI will have on Indian personalization. H0A: The adoption of AI in personalizing facilities is inappropriate H1A: The personalization of resources is ready to consider AI When framing the study questionnaire the Indian customers chose to personalize their experience with ecommerce websites. This was because the target demographic was extremely acquainted with it.

III.IV Reliability
Reliability helps to guarantee that another study analyzes the same methodology as prior experiments again and then draws the same findings and conclusions (Yin, 2009, p. 45). Therefore, the purpose of reliability is to eliminate errors and distortions in a sample (Yin, 2009, p. 45). One way to overcome this is to build a case study database which organizes and records along with the paper all the data and proof for this study (Yin, 2009, p. 118-119). A second analysis may be done through this index, irrespective of the first thesis, and the same results achieved (Yin, 2009, p. 119). The collected data is now stored via cloud computing on Google Drive. Using the Mendeley software papers have been saved and kept. Also detailed and carefully used throughout the entire essay were references, including page numbers, for better access to the referenced content. All of this according to Yin's high reliability research recommendations (2009, pp. 120-122). However, no notices or protocols, which are critical components of the database for high efficiency, according to Yin, have been implemented or retained (2009, p. 120; 2009, p. 45). However, because of the essence of qualitative analysis and the constructionist interpretation of truth, the findings rely very much on the people and meaning of the study (Bryman& Bell, 2011, p. 38). Consequently, reliability is hard to attain in LeCompte and Goetz consistency study (1982, p. 33). The authors suggest it is nearly impossible to recreate qualitative analyzes as they rely on a given framework. The incident or human behaviorcannot therefore be replicated regardless of its complexities (LeCompte& Goetz, 1982, pp. 34-35). This study thus cannot be entirely replicated, however several measures have been taken to enhance the studies' reliability. Another significant feature of the study's reliability is the accelerated growth of different AI technologies.

V Findings and discussion V.I Qualitative Analysis Artificial Intelligence, Machine Learning and Deep Learning:
An analytical branch of computer science is Artificial Intelligence, behaving as human beings to create intelligent devices. The Turing test suggested by the English mathematical artist Alan M. Turing in 1950 is a test that tests the intellect of the system and determines whether the device can carry out every cognitive operation at the human level. Machine learning is an AI subset that helps computers to enjoy practice and to automatically grow. Therefore, sophisticated architectures are built and no explicit programming to implement new database principles is needed. Deep learning is a branch of computer education that consists of a large range of neural networks and algorithms programmed to mimic human intelligence.

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Google's return to direct answer is driven by machine learning, and the return of the "People Ask" segment is driven by deep learning. Google is still studying and imitating human intelligence without trying to supply human beings with all the answers across its massive database.

How Artificial Intelligence works? :
In troubleshooting operations artificial systems function based on algorithms which follow the guidelines, especially on a computer. The Rank brain algorithm, as the word "artificial intelligence" moves, is immediately considered by digital marketers. Rank Brain is a Google-based learning machine that will provide users with answers in 2015. It utilizes artificial intelligence to understand and solve market problems as a human being. Moreover, a similar list of questions entered in Google sometimes falls out. The precise returns and associated problems are all focused on artificial intelligence.

MARKETING STRATEGIES IN DIGITAL ERA:
Owing to the availability of support technologies, numerous organisations are exponentially growing in this modern era. The most prominent technical application in marketing is social networking. Social networking have been an increasingly useful tool in marketing innovation and are important to business development. It's not just today's modern age, but fast. These emerging developments provide several modern advertisement strategies for a company's promotion of items. Any of these have been listed. Content Marketing -Content Marketing becomes a significant area for many brands in the social networking, interactive and mobile hunt. Many organizations currently do not grasp the significance of trends and the foundation behind virtually all digital ads for advertising commercialization. In reality, it is very necessary to create brand awareness. Mobile Marketing -Many users today choose smartphones and laptops, since they are fast and time intensive, because they can still be transported and downloaded. The unabated rise of smartphones and tablet users ensures that mobile platforms need marketing content. Firms should upgrade their website to make it possible for mobile devices to respond. Integrated digital marketing -Integrated marketing is important to co-ordinate and consumer-based messaging and relations strategies used in marketing. For starters, for different reasons, Google developed Google+, but one of them is to see and catch social signals and patterns.

Continuous Marketing -
One of the popular marketing strategies right now is to consciously and regularly educate the customer about the bid. However, it is also essential to promote the product in offline mode. Personalized Marketing -The promotional policy of mass media like television will be less effective as a consequence of network penetration. A new marketing strategy called personalized marketing is currently being developed. Using computer program to read and construct individual messages. App will read and provide consumer actions with the necessary information. Visual Marketing -With Instagram, Pintrestslideshare and more this method can be used. In an object or image, advertisement signals and signals can be incorporated. The new trend of marketing enhances the attention to digital marketing. How digital marketing campaigns are pumped utilizing AI: Modeling of provision and statistical data to forecast consumer behaviour: Property models are predictive scoring cards to identify opportunities that respond more efficiently to a bid. Interaction between customer characteristics and expected behavior. This explains the population goal and the goals of the program and suggests strategies for automatically reaching the target outcomes. Predictive analysis allows manufacturers to extract data and use them to model customer activity buying preferences and trends. This approach explores enormous volumes of information and attempts to detect the most impactful results. Following are the steps: Identification of goals and analysis of data obtained for the identification of designs or models that satisfy our criteria. Templates are generated and evaluated. Data mining is used for optimizing and choosing a final model in this level. The models are evaluated along with the goals identified. The paradigm applies to strategic options which regularly refines models for optimal performance. Accelerated Mobile Pages to reduce load time: The Rapid Web Pages is a project that requires mobile content to be manufactured and loaded by publishers instantly. Google has recently released AMP websites, a small subset of traditional web pages, which aim to improve mobile web performance dramatically, including reductions in loading time for users on page. AMP sites are expected to boost Google's search performance in the top three.
Using AI-powered chatbots to improve user experience: Many companies, when chatting in instant message mode, know and utilize live chatbots or AI programs. It makes our website exclusive, stylish and attractive. Advantages in adapting Artificial Intelligence systems: To customers:

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• AI services assist consumers 24x7. • Search sessions are rendered smoother by AI's tracking of user interaction and forecasting the web actions and possibilities, helping to improve keywords and even enhancing usage of semantine keywords. • Customer awareness should be preserved and no interaction should be repeated. • They're kind and they still deal for our consumers politically and patiently. • You handle several orders concurrently, thereby reducing the wait period to zero. To Marketers:  AI is the foundation for interactive service. Internet marketers will maximize their initiative's efficiency and return on investments by big data insights.  Physical work minimum with less reports of mistakes.  The correct message would be delivered to the right person at the right time on the chosen network.  It enhances the market image of the business and creates new customers to boost goods and service revenue.  Custom ads will be created for consumers to increase their sales.  They track recruitment patterns that enable the company reinvigorate existing brand strategies and boost sales. It serves as an important way to establish a comprehensive and relevant consumer relationship.
 Artificial intelligence targeting threats and limitations:  Only AI is restricted to the availability of data.  Repeat jobs may be easily converted to AI systems but even people have activities requiring human effort.  As in this kind of new technology, there would be significant buying and maintenance costs. Our company wants to carefully analyze investment gains before an AI system is placed in motion.  Design of AI applications needs time.  Algorithms might be inaccurate, but computers can't do it without human beings.  The customer's integrity is not considered. Necessity of adapting AI as a marketing strategy: The best opportunities will be to build a sustainable company online with a creative marketing strategy. Emerging media trends must be monitored and customer interactions must be preserved to achieve a successful strategy. This helps a marketer, leveraging the influence of artificial intelligence technology in digital marketing campaigns, to market his goods or products and services efficiently. It can be observed from the table above that as many respondents as possible who do not currently buy the recommendations on the e-commerce websites feel the recommendations are too generic and need to be personalized.

V.II Quantitative Analysis
If the level of trust is 95% it can be observed that the meaning value is less than 0.05 in both cases (in reference to the significance of PHI and Cramer's V). Null hypotheses are therefore rejected. It can therefore be concluded that the personalization of services is prepared for the adoption of AI. This section outlines how AI can help marketers and for what purpose AI applications may be used. We conclude that there is an absence between readiness and the actual adoption of the AI from the quantitative conclusions. AI contributes to bridging this gap from mass marketing and 1:1 marketing. AI may assist marketers more accurately in targeting the segment. The accurate knowledge of segments helps to personalize content for every single person. From Forrester's survey 57 percent would buy more if a 73 percent personalizing feature, which we obtained from our primary research, was made available. From the point of view of customers, 61% of them believe that AI can offer superior personalized experience to business managers. Example: Persado AI app writes dynamic e-mails. Another way to fill this gap is through AI's action in real time. Example: In daily planning, execution and reporting on campaigns, IBM Watson, Albert supports. It helps from optimization to performance. AI plays a critical role in addressing the issue of improving consumer comfort. AI is able to help build emotional connections, but it is only a machine. Centiment, for example, is a social semantiquity tool that allows marketers to get their emotions understood by their customers and thus help them to understand how their brands and products feel. It uses NLP, a component of AI for that purpose.
(NLG) is an AI application capable of generating independently stories using existing data sources, such as scores and names of the Fantasy football club players. This makes it convenient for users to see stories on topics that they also like with little effort from the marketing point of view, which is a good example for consumers. Therefore, as marketers, the use of data to maximize personalization and enhanced consumer experience is necessary.
With the increase in the data collected, AI can help generate a broader view of mass information through the perception of deeper shoppers' insights and how shoppers are effectively interacted. Advanced systems and advanced analytics can change how we use our mobile, wearable and thus network of objects to expertise (IoT). BFSI and Telecom are the first companies to accept AI or indeed any kind of innovation because they are under constant pressure of profitability.

VI Conclusion
The research illustrates that AI is utilized in many various aspects of marketing. All five AI fields are exploited dependent on these solutions: image recognition, text analysis, decision making, voice recognition and automated robots and automobiles. Though the first three are typically used for ads, it is unusual to realistically introduce voice recognition and has been broadly applied by leading technological companies such as Amazon, Google, Apple or Microsoft. Similarly, single cars and robots are not very common, as Business 4.0 is much more identified with the innovative field of the marketing mix. AI tends to be implemented operationally in communications, usually single tasks or activities. That can be built on the premise that we first engage with the practical application of AI and that businesses are dedicated to integrating and experimenting with this revolutionary technology. Caution may often be motivated by the cost of implementing new concepts and the misunderstanding of their implementation. The marketing departments and organisations' practices have an undeniable impact on these trends. Specifically, different positions and technology, i.e. people with enough AI experience, information processing and the capacity to develop and deploy new solutions are required in the marketing departments. It also includes managing a new model of partnership with companies that include bleeding edge AI technology and establish synergies with AI and other functions. The analysis has shown that AI solutions are used in all fields and five related AI innovations inside marketing activities. Although the authors have described the first AI implementations as a single application, and most of them as experiments, further research is needed to evaluate the marketing impact of AI, particularly the effect of industry. The way we communicate with consumers has also improved. It's morphing every day and here's been the perfect change. The big developments that take place in marketing are artificial intelligence and computer education. It creates unique opportunities for narration and advertisement. The connectivity between people and information, technology, brands and services will alter. Marketing firms will have to adjust their campaigns for connectivity into the modern digital economy through the adaptation of artificial intelligence technologies. It