Did you know that according to Nielsen, 59% of marketers worldwide already consider AI campaign automation a key trend, and in Ukraine the automation of routine tasks using neural networks became a reality as early as 2025? Today competition for customer attention has intensified so much that every minute of delay in responding to an inquiry can cost a lost deal. At the same time, manual processing of inquiries is not just a waste of time but also a risk of errors, loss of leads, and, ultimately, money.
In this guide I’ll show how to implement artificial intelligence into business processes so that automation becomes your competitive advantage, not another “unattainable” innovation slogan.
Neural networks in digital marketing: what they are and why?
In digital marketing they are used to automate analysis of user behavior, content personalization, optimization of advertising campaigns, and even generation of texts or images (for example, GPT, ChatGPT, DALL-E).
Machine learning (ML) allows systems to improve themselves based on accumulated data, and generative transformers (GPT) to create content that best matches the target audience’s requests. These technologies are at the core of modern marketing process automation: from lead segmentation to automatic routing of inquiries in CRM.
Integration of neural networks to automate inquiries in business
Integrating neural networks into processes for processing advertising inquiries allows solving several painful business issues at once: reducing client response time, minimizing human factors, improving communication quality, and ensuring analytics transparency. Modern CRM systems and marketing platforms already offer ready-made solutions for integrating AI models, enabling automation of both simple and complex inquiry processing scenarios.
Integrating AI into inquiry processing: how to do it?
- Analysis of the current inquiry handling process
I start with an audit: which stages take the most time, where “bottlenecks” occur, which inquiries get lost or are processed with delays. - Choosing tools for automation
There are already neural network–based solutions on the market that integrate via API with CRMs (for example, Salesforce, HubSpot, Zoho CRM), as well as universal automation platforms (Zapier, n8n). - Setting up automatic routing of inquiries
Using AI, inquiries are automatically distributed among managers according to specified criteria (source, subject, priority). - Implementing AI-based chatbots
Chatbots with GPT models can not only receive inquiries but also immediately answer common questions, collect additional information, and qualify leads. - Integration with analytics
All inquiries automatically go into the CRM, where the source, status, and processing time are recorded. This allows real-time analysis of channel and manager performance. - Testing and optimization
At the start it’s important to set up A/B testing of scenarios, analyze metrics (CTR, conversion, average processing time) and promptly adjust the algorithms.
Automating ad inquiry processing – case studies and tools
Global companies have long been integrating AI to automate inquiry processing. In the B2B SaaS field, automated AI models analyze the content of an inquiry, determine its priority, and automatically create tasks in the CRM.
Among popular tools:
- ChatGPT – for creating AI assistants and chatbots that process inquiries in messengers and on the website.
- n8n, Zapier, for building automated chains: receiving an inquiry → AI processing → writing to CRM → notifying the manager.
- HubSpot AI Tools, automatic lead quality assessment and recommendations for next steps.
Optimizing advertising with AI to increase ROI
Automating inquiry processing directly affects advertising ROI: the faster and better a lead is processed, the higher the likelihood of conversion. According to Salesforce research, implementing AI in marketing processes can increase conversion by an average of 30%, and budget optimization can reduce the cost of acquiring a lead by up to 20%.
This allows not only improving the effectiveness of advertising campaigns but also minimizing spending on ineffective traffic sources.
Neural networks in automating inquiry processing
Implementing neural networks is not only new opportunities but also new challenges: from data security to system scaling. My own experience showsIt indicates that success depends on the correct assessment of risks and the choice of technological partners.
Risks of integrating neural networks into business processes
- Data security and privacy
AI systems work with large volumes of personal information. It is important to comply with GDPR standards, use encryption, and implement multi-level access control. - Adaptability of algorithms
AI algorithms require regular training on up-to-date data. Without this, the risk of incorrect classification of requests increases. - Human factor
Excessive automation without oversight can lead to a loss of a personal approach. I recommend keeping a “human in the loop” to oversee critical decisions.
Automated processing of requests in large enterprises
Scaling AI solutions: it’s not just increasing the number of processed requests, but also implementing multichannel communication: email, messengers, telephony, social networks. In large enterprises it’s important to provide a single entry point for all requests and centralized analytics.
Best practices of global companies show that gradual implementation (pilot projects, phased feature expansion) allows avoiding disruptions and minimizing risks.
Integration of neural networks with CRM and marketing
Integrating AI models with CRM systems opens a new level of automation: requests are automatically qualified, prioritized, and managers receive recommendations for next actions. Modern CRMs (Salesforce, HubSpot, Zoho) already have built-in AI modules that analyze interaction history, predict conversion probability, and automatically launch personalized marketing campaigns.
AI-driven marketing enables building complex communication scenarios: for example, automatic sending of follow-up messages, personalization of offers based on user behavior, integration with e-commerce platforms for automatic order status updates.
Key takeaways for business and marketing
- Automation of request processing through neural networks reduces time costs, improves service quality, and minimizes the human factor.
- Integration of neural networks with CRM and marketing platforms ensures transparent analytics, personalized communications, and flexible scalability.
- Optimization of advertising using AI increases ROI, enables more efficient budget allocation, and boosts conversion.
- Risks of implementing neural networks require attention to data security, regular staff training, and flexible algorithm management.
- Scaling AI solutions is possible only with phased implementation, testing, and continuous process improvement.
And it is precisely the integration of neural networks into the automation of request processing that becomes the foundation for successful digital transformation of businesses.
