Artificial Intelligence and Competitive Advantage: How to Turn AI into a Strategic Resource
Artificial intelligence and competitive advantage are now at the heart of every company's decisions investing in innovation. Artificial intelligence and competitive advantage depend not only on the adoption of new technologies, but also on how they are integrated into internal resources and processes that generate value over time.
Companies have always been committed to building and maintaining a competitive advantage over their competitors. In the race for customer satisfaction, the winners are those who develop more efficient processes—capable of sustaining lower prices—or those who offer products and services with a perceived higher quality, for which customers are willing to pay more.
With the arrival of generative AI and advanced automation technologies, this balance is changing. The challenge is not just "adopting AI," but understanding where and how to use it to create lasting added value, avoiding fueling a new technology bubble of investments that are difficult to repay.
Artificial Intelligence and Competitive Advantage: Disruptive and General-Purpose Technologies
Artificial intelligence is considered a disruptive technology, capable of profoundly changing organizational processes, key functions, and consumption patterns, with potentially significant impacts on productivity and overall economic growth. In economic literature, AI is often described as a general purpose technology, that is, a general-purpose technology that, like electricity or the Internet, can enable innovation in many different sectors.
This type of technology has historically supported major advances in technological progress and economic growth because it generates non-linear benefits over time. Through automation, advanced data analytics, and the autonomous generation of incremental innovations, AI can reshape traditional production mechanisms and the traditional distinction between capital and labor, also impacting business organization.
Not all scholars, however, agree on the "revolutionary" nature of AI. Some authors argue that AI should be viewed as a "normal" technology, to which businesses and economic systems will adapt without the need for extraordinary adjustments or dramatic technological leaps. In this scenario, AI becomes a basic requirement, but not necessarily a source of sustainable competitive advantage.
The key point for those involved in digital strategy and marketing is understanding when AI becomes a strategic resource and when it remains merely an easily imitated efficiency factor. This is where consolidated management models like Resource Based View (RBV) and the Porter's value chain offer a valuable key to understanding.
RBV Lens: When AI Becomes a Strategic Resource, Not Just Efficiency
The Resource-Based View explains competitive advantage by starting from a company's resources: tangible and intangible assets, skills, knowledge, and organizational routines. It is these resources, and especially their unique combinations, that make a company difficult to imitate and capable of sustaining superior profits over the long term.
To generate a true competitive advantage, resources must exhibit specific characteristics (often summarized by the acronym VRIN): they must be valuable, rare, difficult to imitate, and not easily replaceable. Applied to AI, this lens highlights a clear distinction between "substitute" and "enabling" applications.
AI applications that simply automate repetitive tasks or reduce operating costs improve efficiency, but rarely meet the VRIN criteria. These are commercially available, often off-the-shelf solutions that competitors can quickly replicate. They quickly become industry prerequisites, rather than differentiating factors.
The situation changes when AI is co-specialized with company-specific resources: proprietary data, tacit knowledge, specialized skills, customer and supplier relationships, and consolidated decision-making routines. In these cases, AI isn't just a tool, but becomes part of an integrated system of resources, becoming a difficult-to-imitate capability.
In particular, AI's strategic potential emerges in processes that are highly human-judgmental, where technology complements—not replaces—human work. Here, AI reduces the cognitive costs of search and analysis, expands the range of alternatives, highlights trade-offs and risks, and accelerates learning through continuous feedback.
This integration creates a real cognitive moat: a cumulative learning and decision advantage that leads to better choices, faster innovation, and a more distinctive value proposition or superior risk management, resulting in higher margins over time.
Value Chain and AI: Where Competitive Advantage Is Really Created
To understand where artificial intelligence and competitive advantage can meet in a concrete way, it is useful to go back to the model of Porter's value chain. This framework breaks down business activities into primary activities (logistics, operations, marketing and sales, services) and support activities (infrastructure, human resources management, technology development, procurement).
Each activity contributes to the total value created for the customer and to the margin, that is, the difference between value generated and costs incurred. Analyzing the value chain means asking which activities are currently focused on value creation and where key human resources, critical expertise, and long-term strategic decisions are most concentrated.
From this perspective, AI can take on different roles:
- Substitute role: automation of routine processes with low strategic impact (e.g. standard administrative micro-activities, data entry).
- Complementary role: strengthening of cognitively and relationally intensive activities (e.g. strategic marketing, dynamic pricing, risk management, R&D).
It's in this second space that artificial intelligence and competitive advantage begin to converge. For example, in marketing and sales, AI combined with proprietary customer data can support segmentation, personalization, and customer journey design decisions that are difficult to replicate externally.
Similarly, in R&D or product innovation, AI applied to internal databases, customer feedback, and market information can reduce development times and increase the success rate of new launches. In these cases, it's not just about cutting costs, but about building distinctive capabilities in decision-making, learning, and innovation.
EU and Italian data: AI diffusion and the role of skills
To understand how concrete the transformation underway is, just look at European data. According to Eurostat, in 2024, over 131 TP3T of European Union companies will use AI technologies, up from 81 TP3T in 2023. As was the case with cloud computing, adoption is much more widespread among large enterprises (41 TP3T) than among SMEs (131 TP3T).
The most common applications include automatic analysis of written text (7% of companies), automatic generation of text or speech content, and the transformation of speech into machine-readable formats (5%). Geographically, there are significant differences: Denmark leads with 28% of companies using AI, followed by Sweden and Belgium (25%), while Romania (3%), Poland, and Bulgaria (6%) lag far behind. An authoritative summary of these trends is also available on the website. Eurostat.
The European Union has set clear objectives for 2030: over 90% of SMEs will have to achieve at least a basic level of digital capability and at least 75% of businesses will have to use cloud computing services, big data analytics tools, or AI applications. To monitor this progress, a Digital Intensity Index (DII), based on the use of 12 digital technologies: a score of 4 indicates the basic level, today reached by the 73% of companies, with peaks of 98% among large companies.
In Italy, ISTAT data show significant growth: the introduction of AI in companies with at least 10 employees increased from 5.01 TP3T in 2023 to 8.21 TP3T in 2024, and to 16.41 TP3T in 2025. Larger companies recorded growth of 53.11 TP3T, while SMEs doubled their adoption, going from 7.71 TP3T in 2023 to 15.71 TP3T in 2024, with a greater concentration in the North-West.
Popular AI technologies include:
- knowledge extraction from text documents (70.8% of companies using AI);
- Generative AI on Language, Image, Video, Audio and Speech Conversion (41.3%);
- machine learning for data analysis (20.0%);
- image recognition and workflow automation (18%);
- technologies for the autonomous movement of machines (5.9%).
Despite progress, 83.61% of Italian companies still do not use any AI technology. Among the main barriers are the lack of adequate skills (cited by nearly 60% of companies that have considered but not implemented AI investments), high costs (43.01% of companies), and ethical considerations (25.71% of companies). Only 14.81% believe that adopting AI would not be useful for managing their business.
The business functions where AI is most present are marketing and sales (33.11 TP3T), administrative processes (25.71 TP3T), and R&D/innovation (20.01 TP3T). In marketing and administration, generative AI, linguistic analysis, and workflow automation prevail; in cybersecurity and R&D, predictive machine learning techniques dominate. An updated overview of AI and competitiveness is also available at Wikipedia and on the portal Digital Strategy of the European Commission.
Artificial Intelligence and Competitive Advantage: Impact on Marketing and Business
From a marketing and business perspective, the connection between artificial intelligence and competitive advantage lies primarily in the ability to use data to improve decisions, experiences, and customer relationships. It's not enough to simply introduce a chatbot or a few additional automations: the key is how to integrate AI into the processes that truly matter for business performance.
In digital marketing, AI enables:
- advanced segmentation based on behavioral and predictive data;
- real-time customization of content, offers and messages;
- multi-channel optimization of campaigns and journeys, from the site to instant messaging;
- more accurate measurement of the ROI of the initiatives.
For customer experience, AI allows for more seamless and consistent journeys across channels, anticipating needs, and reducing friction at critical moments (purchase, support, renewals). When these systems are fueled by proprietary data and specific business knowledge, they become progressively more effective and difficult to replicate.
On the operations and organizational front, AI reduces the time spent on repetitive, low-value microtasks, freeing up people and teams for activities with greater strategic impact: developing new offerings, engaging with key customers, market analysis, and product innovation. Competitive advantage doesn't come from replacing human labor, but from enhancing it.
Finally, on the governance level, AI can support top management in assessing scenarios, managing risk, and defining data-driven strategies. Companies that build an AI infrastructure integrated with their distinctive resources are able to react faster to market changes, test new initiatives more quickly, and allocate capital more effectively.
How SendApp Can Help with Artificial Intelligence and Competitive Advantage
For many companies, the first concrete area in which to connect artificial intelligence and competitive advantage is direct communication with the customer, especially on WhatsApp Business. This is where data, automation, and human relationships meet every day: lead generation, support, follow-up, sales, and loyalty.
SendApp It was created to help businesses transform WhatsApp into a strategic channel, with intelligent automations and tools designed to integrate AI into marketing and customer care processes.
Official WhatsApp API and scalable infrastructure
With SendApp Official (WhatsApp API), companies can connect their systems (CRM, e-commerce, management) to WhatsApp Business in a secure and compliant way, building automated flows that leverage AI to:
- qualify leads in real time;
- forward requests to the right team;
- personalize messages and offers based on data in internal systems.
In this way, AI is not a generic layer, but works on the company's proprietary data (conversation history, purchases, tickets, preferences), becoming a key component of its relational capabilities.
Conversation and Team Management: AI to Support, Not Replace
SendApp Agent It helps businesses and sales or support teams manage large volumes of WhatsApp conversations in a coordinated manner, with shared views and intelligent assignment rules. Here, AI can:
- automatically route requests based on content;
- suggest quick replies or custom templates;
- report critical conversations or up-sell and cross-sell opportunities.
The result is a model in which technology enhances the judgment and relational skills of agents, increasing the quality of interactions and the likelihood of conversion, rather than simply replacing human intervention with generic bots.
Cloud automation and data-driven conversational journeys
With SendApp Cloud, companies can design advanced automation flows on WhatsApp and other channels, integrating triggers, segmentation, and data-driven personalization logic. AI can be embedded into these flows to:
- analyze conversation content and update customer profiles;
- adapt the process based on the responses (e.g. different follow-ups for those who show interest or for those who express objections);
- Test message and offer variations and automatically optimize performance.
These systems, when connected to internal databases, build a true capability over time: a company-specific way of interacting with the market, learning with every interaction. This is where the integration between artificial intelligence and competitive advantage becomes tangible, because personalization and responsiveness become difficult to imitate.
For businesses looking to take their WhatsApp Business use to the next level—from simple mass campaigns to strategic channel management—SendApp offers dedicated consulting and scalable solutions, from the testing phase to enterprise-scale volumes. You can start with an operational trial of the platforms and evaluate, with data in hand, where AI applied to instant messaging generates the most value.
