How Leaders Are Investing In Artificial Intelligence To Improve Public Relations

How Leaders Are Investing In Artificial Intelligence To Improve Public Relations

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While many companies invest all their marketing dollars in digital marketing, leaders in the public relations (PR) space claim they can achieve similar or better results combining public relations with artificial intelligence. Artificial intelligence (AI) and other advanced technologies have penetrated every aspect of life. In a survey of 6000 consumers, 33 percent responded that they think they use AI technology; in reality, 77 percent were using AI.

Public relations is often an integral part of companies, big or small. But traditionally, most PR operations rely on humans. For instance, press releases, memos, and other written communication are done by PR specialists, either inside the company or hired through an agency.

AI is now making strides in the PR industry, with many impressive tools in the market utilizing AI to improve PR capabilities for PR agencies and businesses alike. Still, PR has a long way to go toward fully embracing AI.

Here are five ways AI can benefit and change PR:

Speech to Text Conversion

AI’s capabilities to transcribe speech can come in handy for PR teams immensely. This technology can be a big time saver because there’s a lot of use of audio speech in PR, from transcribing interviews to writing about podcasts.

PR agents can record interviews and speeches and transcribe them using AI-based tools. With natural language processing (NLP) technology, such tools may be able to not only transcribe audio but also translate it without losing context.

Contact Search and Recommendation

Perhaps the biggest benefit AI can offer PR is to identify and recommend contacts from media to pitch. In the State of PR 2021 report, 34 percent of PR professionals said that finding journalists is their biggest challenge. It’s time-consuming, and often unsuccessful.

“With data analysis and language processing capabilities, AI can find and recommend contacts that are perhaps more likely to respond to PR pitches,” says Valentin Saitarli, CEO and co-founder of the AI-powered public relations platform PRAI.co. Artificial intelligence can handle these mundane and arguably more challenging tasks, freeing up time for other important PR tasks.

Also, by selecting journalists and media personalities relevant to the company's niche/industry, it increases the likelihood of your pitches being picked up.

?Predictive Data Analysis

Big data is transforming different industries by culling huge amounts of data for actionable insights from huge amounts of data. But data analytics doesn’t just stop at contact recommendations. It can also further predict granular details about those contacts so PR pros can make better decisions about what to pitch and to whom.

Predictive data analysis can also help identify trends in PR and their likely success rate. This can help tweak PR strategy and pursue contacts most likely to help you achieve your goals.

Natural Language Generation

AI’s natural language generation capabilities have come a long way. In 2016, the Washington Post experimented with an AI tool to cover the Rio Olympics. In 2020, the first AI-written press release was distributed.

With this technology, companies can use AI bots to write press releases and other materials for PR. As these tools learn more, they can sound more natural and relevant.

AI Fact-Checking

Fact-checking is arguably the most important task in publishing news. Because of the amount of data it processes, AI can back all of its claims with facts.

Processing Emotions in Responses

The next step in AI, particularly robotics, is human emotion recognition. This application can have positive consequences for PR. By recognizing emotions and sentiments, PR tools can assess what to say or write and how to handle a situation.

With language processing and facial expression recognition, AI tools may be able to register how the public or media responds to a brand’s image.

Quantifying ROI in PR

Saitarli says “AI is truly revolutionary for public relations, which is why we created PRAI. It’s an AI-powered platform designed for small and medium-sized businesses to take autonomy of their PR processes.”

Companies can use the power of AI to do most of the work in-house and generate publicity for their brands.

It is often difficult to measure the ROI of a PR campaign, because visibility and authority tend to be unquantifiable. One of the benefits of using software-based PR outreach, however, is in its ability to provide campaign metrics that help quantify campaign success.

Overcoming the C-Suite’s Distrust of AI

Summary.??Data-based decisions by AI are almost always based on probabilities (probabilistic versus deterministic). Because of this, there is always a degree of uncertainty when AI delivers a decision. There has to be an associated degree of confidence or...more

Despite rising investments in artificial intelligence (AI) by today’s enterprises, trust in the insights delivered by AI can be a hit or a miss with the C-suite. Are executives just resisting a new, unknown, and still unproven technology, or their hesitancy is rooted in something deeper? Executives have long resisted data analytics for higher-level decision-making, and have always preferred to rely on gut-level decision-making based on field experience to AI-assisted decisions.

AI has been adopted widely for tactical, lower-level decision-making in many industries — credit scoring, upselling recommendations, chatbots, or managing machine performance are examples where it is being successfully deployed. However, its mettle has yet to be proven for higher-level strategic decisions — such as recasting product lines, changing corporate strategies, re-allocating human resources across functions, or establishing relationships with new partners.

Whether it’s AI or high-level analytics, business leaders still are not yet ready to stake their business entirely on machine-made decisions in a profound way. An examination of AI activities among financial and retail organizations by Amit Joshi and Michael Wade of IMD Business School in Switzerland finds that “AI is mainly being used for tactical rather than strategic purposes — in fact, finding a cohesive long-term AI strategic vision is rare.”

More than two in three executives responding to a Deloitte survey, 67%, say they are “not comfortable” accessing or using data from advanced analytic systems. In companies with strong data-driven cultures, 37% of respondents still express discomfort. Similarly, 67% of CEOs in a similar survey by KPMG indicate they often prefer to make decisions based on their own intuition and experience over insights generated through data analytics. The study confirms that many executives lack a high level of trust in their organization’s data, analytics, and AI, with uncertainty about who is accountable for errors and misuse. Data scientists and analysts also see this reluctance among executives — a recent survey by SAS finds 42% of data scientists say their results are not used by business decision makers.

When will executives be ready to take AI to the next step, and trust it enough to act on more strategic recommendations that will impact their business? There are many challenges, but there are four actions that can be taken to increase executive confidence in making AI-assisted decisions:

1. Create reliable AI models that deliver consistent insights and recommendations

2. Avoid data biases that skew recommendations by AI

3. Make sure AI provides decisions that are ethical and moral

4. Be able to explain the decisions made by AI instead of a black box situation

Create reliable models

Executive hesitancy may stem from negative experiences, such as an AI system delivering misleading sales results. Almost every failed AI project has a common denominator — a lack of data quality. In the old enterprise model, structured data was predominant, which classified the data as it arrived from the source, and made it relatively easy to put it to immediate use.

While AI can use quality structured data, it also uses vast amounts of unstructured data to create machine learning (ML) and deep learning (DL) models. That unstructured data, while easy to collect in its raw format, is unusable unless it is properly classified, labeled, and cleansed — videos, images, pictures, audio, text, and logs — all need to be classified, labeled for the AI systems to create and train models before the models can be deployed in the real world. As a result, data fed into AI systems may be outdated, not relevant, redundant, limited, or inaccurate. Partial data fed into AI/ML models will only provide a partial view of the enterprise. AI models may be constructed to reflect the way business has always been done, without an ability to adjust to new opportunities or realities, such as we saw with disruptions in supply chains caused by the effects of a global pandemic. This means data needs to be fed real time to create or change models real time.

It is not surprising that many data scientists spend half their time on data preparation, which remains as a single significant task in creating reliable AI models that can deliver proper results. To gain executive confidence, context, and reliability are key. There are many AI tools that are available to help in data prep – from synthetic data to data debiasing, to data cleansing, organizations should consider using some of these AI tools to provide the right data at the right time to create reliable AI models.

Avoid data biases

Executive hesitancy may be grounded in ongoing, and justifiable, concern that AI results are leading to discrimination within their organizations, or affecting customers. Similarly, inherent AI bias may be steering corporate decisions in the wrong direction. If proper care is not taken to cleanse the data from any biases, the resulting AI models will always be biased, resulting in a “garbage in, garbage out” situation. If an AI model is trained using biased data, it will skew the model and produce biased recommendations.

The models and the decisions can be only as good as non-bias in the data. Bad data, knowingly or unknowingly, can contain implicit bias information — such as racial, gender, origin, political, social, or other ideological biases. In addition, other forms of bias that are detrimental to the business may also be inherent. There are about 175 identified human biases that need care. This needs to be addressed through analysis of incoming data for biases and other negative traits. As mentioned above, AI teams spend an inordinate amount of time preparing data formats and quality, but little time on eliminating bias data.

Data used in higher-level decision-making needs to be thoroughly vetted to assure executives that it is proven, authoritative, authenticated, and from reliable sources. It needs to be cleansed from known discriminatory practices that can skew algorithms.

If data is drawn from questionable or unvetted sources, it should either be eliminated altogether or should be given lower confidence scores. Also, by controlling the classification accuracy, discrimination can be greatly reduced at a minimal incremental cost. This data pre-processing optimization should concentrate on controlling discrimination, limiting distortion in datasets, and preserving utility.

It is often assumed — erroneously — that AI’s mathematical models can eventually filter out human bias. The risk is that such models, if run unchecked, can result in additional unforeseen biases — again, due to limited or skewed incoming data.

Make decisions that are ethical and moral

Executive hesitancy may reflect the fact that businesses are under pressure as never before to ensure that their businesses operate morally and ethically, and AI-assisted decisions need to reflect ethical and moral values as well. Partly because they want to appear as a company with ethical, moral values and operate with integrity, and partly because of the legal liabilities that may arise from making wrong decisions that can be challenged in courts – especially given that if the decision were either AI made or AI assisted it will go through an extra layer of scrutiny.

There is ongoing work within research and educational institutions to apply human values to AI systems, converting these values into engineering terms that machines can understand. For example, Stuart Russell, professor of computer science at the University of California at Berkeley, pioneered a helpful idea known as the Value Alignment Principle that essentially “rewards” AI systems for more acceptable behavior. AI systems or robots can be trained to read stories, learn acceptable sequences of events from those stories, and better reflect successful ways to behave.

It’s critical that works such as that conducted by Russell are imported into the business sector, as AI has enormous potential to skew decision-making that impacts lives and careers. Enterprises need to ensure there are enough checks and balances to ensure that AI-assisted decisions are ethical and moral.

Be able to explain AI decisions

Executives could be wary in absorbing AI decisions if there is lack of transparency. Most AI decisions don’t have explainability built into it. When a decision is made and an action is taken that risks millions of dollars for an enterprise, or it involves people’s lives/jobs, saying AI made this decision so we are acting on it is not good enough.

The results produced by AI and actions taken based on that cannot be opaque. Until recently, most systems have been programmed to explicitly recognize and deal with predetermined situations. However, traditional, non-cognitive systems hit a brick wall when encountering scenarios for which they were not programmed. AI systems, on the other hand, have some degree of critical thinking capability built in, intended to more closely mimic the human brain. As new scenarios arise, these systems can learn, understand, analyze and act on the situation without the need for additional programming.

The data used to train algorithms needs to be maintained in an accountable way — through secure storage, validation, auditability, and encryption. Emerging methods such as blockchain or other distributed ledger technologies also provide a means for immutable and auditable storage. In addition, a third-party governance framework needs to be put in place to ensure that AI decisions are not only explainable but also based on facts and data. At the end of the day, it should be possible to prove that if a human expert, given the same data set, would have arrived at the same results — and AI didn’t manipulate the results.

Data-based decisions by AI are almost always based on probabilities (probabilistic versus deterministic). Because of this, there is always a degree of uncertainty when AI delivers a decision. There has to be an associated degree of confidence or scoring on the reliability of the results. It is for this reason most systems cannot, will not, and should not be automated. Humans need to be in the decision loop for the near future. This makes the reliance on the machine based decisions harder when it comes to sensitive industries such as healthcare, where 98% probability of a decision is not good enough.

Things get complex and unpredictable as systems interact with one another. “We’re beginning to accept that the true complexity of the world far outstrips the laws and models we devise to explain it,” according to David Weinberger, Ph.D., affiliate with the Berkman Klein Center for Internet and Society at Harvard University. No matter how sophisticated decision-making becomes, critical thinking from humans is still needed to run today’s enterprises. Executives still need to be able to override or question AI-based output, especially within an opaque process.

Tasks to raise executive confidence

Consider the following courses of action when seeking to increase executives’ comfort levels in AI:

? Promote ownership and responsibility for AI beyond the IT department, from anyone who touches the process. A cultural change will be required to boost ethical decisions to survive in the data economy.

? Recognize that AI (in most situations) is simply code that makes decisions based on prior data and patterns with some guesstimation of the future. Every business leader — as well as employees working with them — still needs critical thinking skills to challenge AI output.

? Target AI to areas where it is most impactful and refine these first, which will add the most business value.

? Investigate and push for the most impactful technologies.

? Ensure fairness in AI through greater transparency, and maximum observability of the decision-delivery chain.

? Foster greater awareness and training for fair and actionable AI at all levels, and tie incentives to successful AI adoption.

? Review or audit AI results on a regular, systematic basis.

? Take responsibility, and own decisions, and course correct if a wrong decision is ever made — without blaming it on AI.

Inevitably, more AI-assisted decision-making will be seen in the executive suite for strategic purposes. For now, AI will be assisting humans in decision-making to perform augmented intelligence, rather than a unicorn-style delivery of correct insights at the push of a button. Ensuring that the output of these AI-assisted decisions is based on reliable, unbiased, explainable, ethical, moral, and transparent insights will help instill business leaders’ confidence in decisions based on AI for now and for years to come.

Artificial Intelligence in creating future of global strategy – International companies’ leadership approach

Today’s world offers as well as many possibilities of development as threats. We face problems with pandemic, military conflicts like war in Ukraine, change of climate, pollution, and unsolved economic problems as well. Those issues affect companies’ strategies, especially taking into consideration international approach. Companies aim to achieve competitive advantage by trying to employ different tools including artificial intelligence (AI) (Rusia, et.all. 2021).

AI can work as human, based upon neural network. It may even improve some performance due to the possibility of constant learning (Kumari, Bhat, 2021). Intelligence itself is defined as the ability to learn and create suitable proposals to solve complex problems. There are lot of definitions of AI.?Originally AI was linked with computers and other IT technologies. It was categorized as any software which could use programs and provide solutions analyzing usually big data collections. All those solutions were finally evaluated by humans and were regarded as the basis for decision-making process. It should have been stated that this approach anyway required human intelligence.

Quite often AI is used as synonymous to Machine Learning (ML). ML figures computational models that provide predictions of possible problems solutions. ML is usually divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning. So, there are many definitions around, but most of them can be classified into the following four categories: systems that think like humans, systems that act like humans, systems that think rationally, systems that act rationally (Raynor, 1999). I argue that this definition underline very important and challenging feature of AI. It means system can act rationally.?Humans pretend to act rationally as well.?Theoretically humans choose the best option. In empirical approach the situation varies, significantly. Humans decisions are habitually affected by emotions and headed in the favor of selected groups; the others are ignored. The same story applies to companies. In market-oriented economies companies must follow characteristic guidance. They habitually try protecting their own benefits first then shareholders and stakeholders.?If the situation from company perspective is satisfactory, companies can support even competitors. Whenever such cases are becoming more popular, still competitiveness among companies are very vital.

Moreover, the problems both in local and international scale are not solved. Furthermore, they started to be more dangerous and create barriers. The “rich part” of the world has been developing fast, leaving behind the “poor one”. Underdeveloped countries do not employ advanced technologies they do not create big domestic companies operating worldwide, as well. International companies operate in different markets, including underdeveloped countries as well. International companies focusing to employ strategy that help to assure good business result both in well and less developed countries (Thomé, Medeiros 2016). Strategy should foresee basic vision and long-term objective of any company. Especially international companies should be long-time oriented. Spot transactions can be accepted but the success does not rely on temporarily deals (Griffin, Pustay 1991).?That is why local partners and relations with them for international companies play crucial role. Strategy of international companies should include the way of cooperation with the local companies. Usually, such feature makes some important problems, for example due to the fact of the gap of development, culture differences and profits shares.?The emotions as well as lack of trust can hammer any business attitude and make strategy less effective. This applies not only to international companies but local ones as well. That is why there is an obvious need to present some ideas to create better working strategy of international companies. Taking this into consideration AI can be one of such thoughts.

Typical approach to create strategy of international companies

International companies are convinced in strategic planning. Their strategy is supposed to be focused on long term goals. Short term ones must fulfill future targets. The key question is: Does this long-term strategy really pay off not only for international company but for all co-partners?

Another important issue is related for the motivation of those entities operating globally.?Such motion provides usually more profits and less risk (Harrigan, 1984). International companies operate very skillfully, and they habitually come from more developed economies. They offer wide range of products and services employing advanced technologies including AI.

There are international companies originating from less or moderate developed countries, some of them even from emerging markets, but their offer is not so wide one, and they are focusing on raw materials and semi-ready products predominately (Dunning, 1992).?Those companies are not enough capable to engage AI and other advanced technologies. Such approach requires not only proper institutional order but progressive R&D spendings.?Albeit some less developed countries spend reasonable (as the % of their GDP) money on R&D but the effectiveness of such investment are still very poor ones. International companies are generally coined multinational corporates and they predominately choose three basic types of strategy: transnational, global and multidomestic (Cullen, Parboteeah, 2010). Transnational strategy is simply a business action across borders.

According to Levitt (1983), the idea of global strategy is to produce a single standardized product or service and deliver them to different markets. In this way company achieve benefits due to the scale of production.?Standardized product helps to assure accuracy and skillful manufacturing (Mahoney, Pandian, 1992). Multidomestic strategy is understood as the business motion plan of subsidiaries of a multinational firm to compete in domestic market by offering different products or services (Diaconu, 2012). Head office controls subsidiaries, providing them marketing and R&D assistance. It interferes whenever the whole company benefits are in danger. Such situation may happen whenever one entity can grow too fast, exploiting the others’ assets.

Taking this situation into consideration some technology advanced companies may employ AI in order not to elaborate the best (from optimum point of view) strategy but controlling subsidiaries fair competitiveness and development as well. The situation varies whenever the benefits of competitors or even co-partners are taken into an account. The priority of the business profits goes to the international company.

The role of AI in strategy formation of international company

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Each strategy must consist of several important and characteristic elements. They are like follows mission, vision, goal, organization, motivation, and control. Each element may be supported by AI. I argue that all elements of strategy are very imperative, and they should have been discussed all together. One missing element can spoil the whole strategy. Even though international companies have enormous amount of assets, they cannot make so many false steps. It was discovered by many researchers that various companies have successfully implemented AI technologies to meet customers’ needs (Tsai, H-Y, 2021). So, the usage of AI in creating successful strategy has been mainly devoted to marketing strategy. Based upon such approach all elements of strategy may be dependent on marketing goals. AI has been especially

. More important elements of the strategy are ignored.

AI employment in future global international companies’ strategies

AI is quite well-known tool in international companies. They recognized the benefits of this device very skillfully. International firms spend lot of money for R&D exploring all possibilities to achieve market advantage. They employed IT as well as already in the 70ties. This step made them leaders not only in communications but gathering and analyzing big data. AI has been used to investigate data to make the best final decision. I argue that all decisions were in the favor of international companies. The benefits for others were International companies set up algorithms in the way searching for mainly financial benefits. The other benefits are ignored. We can indicate many examples of violating natural environment, pollution even devastating social systems, especially in less-developed countries. Such activity finally is not only danger for international companies but the whole societies in the world. Big exploitation of natural resources is the short-oriented goal. The idea of sustainability does not change this attitude so much.?I would even say that some of international companies are fed by imbalanced strategy. It means that they exploit others too much focusing on their benefits only. Since less developed countries lose their position in competitive advantage, AI of international companies supposed to take into consideration two important facts:

1. Long term cooperation with entities originating from less developed parts of the globe

2. Provide options for sustainable growth

?of raw materials caused irreversible damage. Even though headquarters of international companies are based in well developed countries and environment is officially better protected, but globalization and comprehensive processes affect them as well. Finally, they face similar effects like those entities in less developed economies.

The role of Artificial Intelligence in global strategy creation of international companies