From Shadows to Spotlight: Growing Businesses Through Platform Strategies

27.03.2024 6 Min Read
Anna Katashvili

Management Consultant, Digital Strategist

From Shadows to Spotlight: Growing Businesses Through Platform Strategies

“How could they see anything but the shadows if they were never allowed to move their heads?”
― Plato, The Allegory of the Cave

In the cryptic corridors of Plato's allegory of the cave, where shadows dance upon the walls, lies a profound metaphor for the journey from obscurity to enlightenment. Similarly, in the realm of commerce, businesses embarking on the path of platform models traverse a transformative odyssey, breaking free from traditional silos to sculpt interconnected ecosystems that unveil a spectrum of new opportunities.

The allegory of the cave, in which chained prisoners mistake shadows for reality, resonates in the world of digital transformation. Traditional businesses, siloed and internally focused, often lack understanding of the transformative potential of platform models. This article draws inspiration from Plato's metaphor and explores the complexities of platform success in today's digital landscape.

Delving into the academic realm, the concept of digital platform ecosystems emerges as a cornerstone of modern business dynamics. Unlike traditional linear models, where value is created through the production and distribution of goods or services, a platform business model serves as a facilitator, connecting multiple groups of users to create value for each other. Defined as interconnected networks of producers, consumers, and third-party developers, these ecosystems function as vibrant marketplaces where value creation and exchange flourish. Through the orchestration of data, resources, and services, digital platform ecosystems catalyze innovation, foster collaboration, and unlock new avenues of growth.

Platform-based business models encompass a range of specifications that differentiate them from traditional business models. Here are some key specifications, supported by citations:

Network Effects: One of the defining features of platform models is the concept of network effects. As more users join a platform, the value for all participants increases exponentially. (Parker et al., 2016) In scholarly discourse, network effects are regarded as pivotal for the triumph of platforms, fostering a cyclical pattern of expansion and user involvement.

Multi-sided Markets: Unlike traditional businesses that primarily serve a single type of customer, platform-based models often cater to multiple distinct user groups, known as multi-sided markets. (Evans & Schmalensee, 2016).

Data-driven Insights: Platforms are fueled by data, enabling them to gain deep insights into user behavior, preferences, and trends. This data-driven approach empowers platforms to personalize experiences, optimize operations, and innovate continuously. (McAfee & Brynjolfsson, 2017). Furthermore, advancements in artificial intelligence (AI) and machine learning enable platforms to extract actionable insights, predict user behavior, and automate decision-making processes, thereby delivering unparalleled value and driving competitive differentiation.

Ecosystem Orchestration: Successful platform-based models excel in orchestrating ecosystems of complementary goods, services, and stakeholders. This orchestration involves designing interfaces, establishing rules, and fostering collaboration among participants. (Zhu et al., 2019).

APIs and Openness: Platforms often embrace openness through Application Programming Interfaces (APIs), allowing third-party developers to build upon the platform's infrastructure and create innovative offerings. This openness fosters creativity, accelerates innovation, and expands the platform's capabilities.

These specifications collectively define the essence of platform-based business models, showcasing their unique characteristics and competitive advantages in the digital landscape.

The Journey of Business Transformation through Platform Models

Breaking Free from Silos

Traditional businesses often operate in silos, with little interaction between departments and a focus on internal processes. This can lead to inefficiencies, a lack of innovation, and difficulty meeting the evolving needs of customers. Platform models, on the other hand, are designed to facilitate interaction and collaboration between different groups of users. This creates a more dynamic and innovative ecosystem where businesses can leverage the collective intelligence and resources of their users to create new value.

Finding equilibrium between algorithmic regulation and crafting personalized user experiences.

Unlike the single torchbearer in the cave allegory, successful platforms use sophisticated algorithms as conductors, orchestrating complex user journeys. A prime illustration of this is the implementation of recommender systems, which tailor the user experience and enhance overall satisfaction. Netflix's recommendation engine is perhaps the most well-known and widely used recommender system. It uses an algorithm to analyze a user's viewing history, rating, and search behavior to suggest movies and TV shows that the user is likely to enjoy.

Effectively managing platform dynamics, harmonizing the varied needs of stakeholders, and proactively mitigating risks necessitate astute leadership and strategic insight. Furthermore, fostering trust, transparency, and equity within the ecosystem is imperative for cultivating sustainable growth.

Platforms strive to create seamless experiences for customers, aiming to remove obstacles and optimize interactions to enhance satisfaction and loyalty. However, alongside this goal, there is a need for algorithmic governance. Platforms rely on algorithms to moderate content, manage transactions, and mitigate risks. Yet, excessive use of these systems can worsen the user experience, while insufficient regulation may jeopardize data protection and security. Therefore, platform operators must strike a balance between ensuring security with algorithms and delivering the best user experience. Achieving this balance requires platform developers to consider user needs, legal regulations, and ethical aspects carefully

Designing for User-Centricity

Designing for user-centricity is a pivotal aspect of successful platform models, rooted in the principles of empathy and understanding in the digital era. Just as the allegory of the cave underscores the pursuit of truth and enlightenment, businesses transitioning to platform models must embark on a journey to uncover the profound needs and motivations of their users. By placing users at the core of the design process, businesses can craft experiences that resonate deeply, fostering engagement, loyalty, and meaningful interactions.

Leading the Transformation

In an era defined by technological advancement, the transition from conventional to platform-driven models presents businesses with a definitive route to prosperity and expansion. This strategic evolution necessitates a systematic approach centered on three fundamental components:

Platform business models represent a paradigm shift in the value creation equation of the digital economy. Leveraging networks, ecosystems, and data, platforms have the potential to not just drive business growth, but to transform entire industries. However, unlike Plato's cave, the digital landscape is a dynamic ecosystem in constant flux. Platform success isn't a one-time disruption, but a continuous performance demanding constant adaptation.

Rohn D., Bican P. M., Brem A., Kraus. S, Clauss Th. (2021) Digital platform-based business models – An exploration of critical success factors, Journal of Engineering and Technology Management, Volume 60.

Parker, G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform revolution: How networked markets are transforming the economy--and how to make them work for you. W. W. Norton & Company.

Evans, D. S., & Schmalensee, R. (2016). Matchmakers: The new economics of multisided platforms. Harvard Business Review Press.

McAfee, A., & Brynjolfsson, E. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future..

Zhu, F., Iansiti, M., & Lakhani, K. R. (2019). Competing in the Age of AI. Harvard Business Review Press.

Featured Insights

Marketing inspiration can dry up from time to time. Looking at never-ending data reports or thinking about many other routine tasks can stifle the creativity of even the most experienced marketer. Artificial intelligence (AI) exists for that. Forget dystopian visions of robots taking over your marketing department. AI is not here to replace you; Imagine a tireless assistant that can analyze large amounts of customer data, spotting emerging trends that might escape even the most observant eye. AI can become your powerful and indispensable strategic partner, leveraging human creativity to help you create powerful, data-driven, effective strategies, offer innovative campaign concepts and personalized messaging strategies based on real-time insights.

AI: Performance Enhancer

Imagine a world where the routine tasks that consume your time—analyzing data, planning social media, and creating accounts—disappear into automated obscurity. This is not science fiction; AI can handle the routine, giving you more time to develop strategy and creative campaigns. Perhaps we agree that the number of routine tasks can stifle innovation. Embracing this bold change helps us focus on creating innovative campaigns that resonate precisely with audiences.

From data to insights

AI goes beyond mere automation; It is the best helper in terms of data processing. Imagine a powerful artificial intelligence engine that observes and analyzes massive data, revealing trends. It's not just about key demographics; AI can explore the depths of consumer sentiment on social media, website interactions, and purchasing behavior. Result? A detailed and dynamic portrait of your ideal customer. With these insights, messages and offers can be personalized as much as possible for the best results. This in turn will lead not only to higher conversion rates, but also to a deeper understanding of the ever-evolving customer journey.

Case: Netflix's AI-powered recommendations

Netflix's success story using AI goes beyond simply suggesting shows you might like. It serves as a masterclass in using artificial intelligence to personalize the entire user experience and optimize content creation. This not only personalizes the user experience, but also facilitates content creation by identifying genres and topics that accurately reflect the interests of their users. In more detail:

  • The magic of micro-targeting: Imagine having a crystal ball that shows you what your audience wants - their favorite actors, their preferred genres, and even the times of day they're most likely to watch. This allows Netflix to show you thumbnails (small pictures) and recommend shows you might really like. Netflix uses AI to conduct A/B testing on a massive scale. Various thumbnails, trailers and even descriptions are presented to users and AI analyzes which ones generate the most clicks and views. This data-driven approach enables continuous optimization.
  • Content is King, and AI Knows the Court: Gone are the days when content development depended solely on instinct. Netflix's AI analyzes viewing trends and completion rates, even where scrolling occurred. This gives them the opportunity to create content that caters to specific audience segments and increases engagement. Similarly, AI can analyze consumer behavior and social media sentiment to predict what kind of content will resonate with your target audience, allowing you to develop marketing campaigns that are more successful.
  • Predicting the Unpredictable: Consumer behavior can be erratic, but Netflix's AI is constantly learning and evolving. By analyzing large amounts of data, it can predict which shows are likely to be hits and which ones might not. That same level of predictive power can be incredibly valuable to marketers as well. AI can analyze market trends, competitor strategies, and even social media feedback to help you anticipate customer needs and tailor your communications accordingly.

Adaptation and development in the age of AI

Marketing is constantly evolving, and AI represents a major shift that resistance to can hinder progress. Instead, we should look at artificial intelligence as an opportunity to improve skills to achieve greater success.

Maintaining a positive attitude towards AI is crucial. Focusing on the potential benefits of AI, such as increased efficiency, deeper customer analysis, and the ability to create more targeted and personalized marketing campaigns.

We shouldn't be afraid to experiment with new AI tools and marketing strategies. "Fail fast, learn faster" approach allows for quick adaptation.

The indispensable person: creativity and empathy

Artificial intelligence is a powerful tool, but it lacks the creativity and empathy of humans to develop compelling narratives that resonate emotionally. Understanding people's moods on an emotional level and making real connections with your audience remains your exclusive opportunity. Artificial intelligence cannot replace the human ability to translate data into compelling brand stories. Think about it - who will write an emotional brand story that you can't read without tears? It's you, the creative genius behind the wheel.

A masterpiece of collaboration: humans and AI in harmony

The future of marketing is not a competition between humans and artificial intelligence; It is a masterpiece of collaboration. Imagine: AI builds data-driven insights, uncovering customers' hidden desires and emotional triggers. You then translate this information into compelling brand stories that resonate with your audience on an emotional level. This synergistic approach leads to effective, data-driven marketing campaigns that drive brand loyalty and long-term success.

The future of marketing is collaboration between humans and artificial intelligence. You can discover a new era of marketing efficiency. This future promises not only efficiency and meaningful insights, but also deeper analysis of the customer journey map, allowing for the truly meaningful connections that drive brand loyalty and long-term success.


In the modern era, we are all witnessing first-hand the unprecedented speed at which technological advancement is changing industries. The evolving technological environment creates different business needs and requires new strategies to respond to them, for which often traditional strategic planning alone is no longer effective and it becomes necessary to use various technological advances, including artificial intelligence (AI). Integrating AI into the management and decision-making process of companies gives managers the opportunity to collect, analyze and make decisions based on the maximum amount of data.

Problems of traditional strategic planning

Strategic planning is the process of setting long-term goals, analyzing internal and external factors, and determining how to respond to them, allowing businesses to seize opportunities and overcome challenges in a rapidly changing environment to optimally allocate resources, expand market share, or achieve sustainable growth.

Accumulated consulting experience shows that different businesses vary in their level of adaptation of the strategic planning process, although even the best of them often have significant shortcomings in their strategic planning efforts, e.g.

• Planned events are carried out infrequently (once a year or not at all)

• When making strategic decisions, reasoning is often based only on the intuition and experience of management and does not take into account rational factors of the external environment (market, competition).

• The planning process does not use sufficient, accurate and objective data.

• Once strategies are selected and implemented, progress is rarely monitored.

What does AI offer businesses today?

Currently, artificial intelligence is already actively used at various stages of business management and operations. In its simplest form, it serves a descriptive function and is used to create analytical data such as graphs/dashboards for competitive analysis or to study the performance of different lines of business. More advanced versions of AI have diagnostic intelligence, which refers to the ability to establish cause-and-effect relationships between events and understand root causes and driving forces. Such algorithms can identify patterns and trends in customer behavior, market demand, or competitor strategies by analyzing historical data.

The third stage of AI development and the most progressive stage currently actively used in business is predictive intelligence, which has the ability to create forecasts for the future based on certain assumptions and analysis of past experience. Predictive intelligence is used to create scenarios based on various changes and scenarios, allowing business owners to assess the potential impact of these scenarios on their business and develop customized strategies to respond to them. Predictive intelligence can also identify potential risks, which through ongoing monitoring enable organizations to proactively mitigate their negative impact.

The next levels of AI integration into business include developing analytics-based recommendations, delegating certain decisions to artificial intelligence, and full AI autonomy, although the full integration of these stages into management is still a work in progress and will take some time.


Still, how can AI be used in strategic planning?

Despite the fact that the use of artificial intelligence is characterized by growing trends in such key areas of business management as marketing, human resources, logistics, customer service, etc., the degree of its implementation in the strategic planning process is still quite low, due to important features of this process. Unlike other processes associated with business management, the strategic planning process has a particularly high proportion of human involvement, decisions based on personal experience and intuition, emotions, historical and cultural context, along with rational factors.

Therefore, if we ask the question as follows, is it possible to completely automate the strategic planning process using artificial intelligence, the answer is no (at least in the current situation and the future perspective that can be imagined from this situation). However, observing the current business context allows us to confidently say that artificial intelligence is already transforming certain stages of the strategic planning process, as well as the approaches and thinking of the people involved in these stages.

In the strategic decision-making process, we can think of AI-human interaction as a three-step process, in the first step of which a human tells the AI a problem/asks a specific question that it wants to solve. At the second stage, AI processes and analyzes the database associated with the problem, as a result of which it offers the “customer” several options for solving the proposed problem. After this, the “customer” obviously has a choice - make a strategic decision based on the given options, entrust the decision to artificial intelligence, or modify the problem to accept other options.

In other words, AI can play an important role in the strategic planning process in making rational, fact-based and data-driven decisions, the further review and final evaluation of which is still subject to human influence.

Delegating rational decision-making to artificial intelligence will itself change the specificity of certain positions in organizations and will lead to an increase in employer demand for so-called “strategists” who will be responsible for aligning the recommendations generated by artificial intelligence with the values and goals of the organization . Particularly important in this process are skills that will help you make intuitive strategic decisions. An example of such skills is creative thinking, the ability to analyze not only facts, but also context and abstract thinking.

What benefits does AI bring to the strategic planning process?


Incorporating artificial intelligence into the strategic planning process offers companies several significant benefits, which include, but are not limited to:

• Automating repetitive and manual tasks – reducing the time spent on these tasks, optimizing costs and increasing business productivity and efficiency.

• Optimization of the decision-making process. AI algorithms can provide objective and data-driven insights, allowing business managers to make faster and more informed decisions.

• Increased forecasting accuracy – leads to a reduction in risks associated with strategic decisions, and through constant monitoring allows for the timely identification of various anomalies.

Which businesses will benefit most from using AI in strategic planning?

Let's start with the fact that all businesses, regardless of size and industry, have the opportunity to use artificial intelligence more than today. However, the advantages of artificial intelligence in strategic planning are directly proportional to the presence of a number of prerequisites. Therefore, before you begin this process, questions need to be asked (note: the following are some basic questions, although they are not exhaustive; the readiness assessment process will be much more complex):

• Do you have all the data (internal and external) that can influence your strategic decisions? - Remember, often even minor problems in the environment can have a significant impact on your strategy.

• How good is the data you have? - Remember that the result obtained using artificial intelligence will be of exactly the same quality as the information you provide. Therefore, ensuring data accuracy and reliability is critical for effective AI analysis, which in turn requires additional investment in data management systems.

• How flexible is your business? – AI-generated scenarios and recommendations enable you to develop rapid response strategies in a turbulent environment, but you must ensure that your business processes, systems, structures and your team's mindset support the implementation of these recommendations.

What are the potential risks of incorporating AI into strategic decision-making?

If you decide to incorporate artificial intelligence into your strategic planning process, it is important to remember that there is no universal AI that can solve all your problems. However, it is important to understand that artificial intelligence is not “magical.” Therefore, to avoid unrealistic expectations and poor implementation, it needs to be properly structured and contextualized. In other words, it needs to be “trained” to give correct answers and predictions.

When using artificial intelligence, the issue of ethics must also be taken into account. When making strategic decisions, managers consider various ethical aspects and human values, potential impacts on society and the environment, which may not be integrated into the artificial intelligence system.

Another challenge to using artificial intelligence in strategic decision making is accountability. In fact, only humans, not machines (even intelligent machines), can be held responsible for their decisions. This issue will increase the need for legal regulation of strategic AI decisions in the future. To create a regulatory framework for compensation for damage caused by the operation of artificial intelligence systems, it will be necessary to define the concept of artificial intelligence and its status in civil law relations.


In conclusion, artificial intelligence has already changed and in the future will even more fundamentally transform the strategic planning process. By using it, organizations can ensure that their strategies adapt to a rapidly changing environment to ensure long-term success. However, organizations must also take care to address the challenges associated with the use of artificial intelligence and ensure that it is used responsibly in critical decision-making processes.