Is Generative AI a good idea for your business? 9 compelling strategies to prove its worth

Is Generative AI a good idea for your business? 9 compelling strategies to prove its worth

Including artificial intelligence development services in your development strategy is a great idea — that’s how you will be able to tailor your growth to modern trends and ensure its efficiency in the long run. In a nutshell, the secret of GenAI's success lies in how it interprets tons of data to produce new formats of information — code, pictures, audio, voice, and so on.

In the right hands, the advancements of AI-powered instruments you can get for your business in any market are second to none. GenAI’s contribution to global GDP is projected to surpass $15 trillion by 2030, with $2.5 trillion to $4.5 trillion per year by 2040. Once issues associated with the use of artificial intelligence are solved, including ethical concerns, its impact on IT product time-to-market, workflow efficiency, and overall business bandwidth will skyrocket.

Generative AI for your business/blog_image 1

If you wonder how exactly GenAI can change the landscape of travel and tourism, FinTech, insurance, retail, and other industries, stay tuned.

Trending GenAI business models in 2023

Such tools as Midjourney and ChatGPT showcase how AI tools can reduce the degree of human supervision and control in numerous professional spheres and handle the ever-shifting modification demands. Not only are they sufficient to boost content generation and deployment strategies, but they also come in handy to automate and structurize documentation, complete different tasks, and so on.

In general, there are four typical fields with the biggest ratio of AI-empowered architecture implementation:

  • research and development;
  • software engineering;
  • marketing campaigns;
  • customer care.

These cases amount to around 75% of the GenAI value across industries, as a McKinsey report showcases. Let’s dig in.


Without a doubt, AI-based tools and apps accelerate industrial growth and make it easier for market members to speed up their innovation and the discovery of new opportunities. It won’t be Mission Impossible to obtain a 10% increase in productivity gains and translate them to savings through AI and further in-market research.

Since artificial intelligence systems are capable of analyzing a huge amount of data, it might be a game-changing solution for complex processes and platforms, simplifying their operation and scaling efficiency. In the healthcare industry, this innovation has already shortened the drug development process — everything to how it reduces the time from ideation to analysis results.

Software engineering

At its current level of efficiency, AI won’t replace human labor in the IT sphere. Nevertheless, its impact on how easy it may become to sample data, generate tests, find bugs, create code, and fix errors shouldn’t be overlooked. For solving repetitive tasks, the use of GenAI is a real find. According to studies, the way AI contributes to developer productivity won’t remain unnoticed on a global scale — more than $1.5 trillion to GDP. In practice, such tools have already proven their dependability in expediting software development life cycles and delivering more top-notch solutions in a shorter time frame.

Their influence on robust data analysis for software engineering and other processes also stands out. Not only do they increase the final research and analysis results’ accuracy and delivery speed, but they also help make these processes customer-centric and data-driven.

Digital marketing

Artificial intelligence simplifies targeted marketing thanks to how it interprets large amounts of data quickly and effectively. Whether you are interested in creating social media posts or finding out more about your audience’s demographics, AI tools will pay off. Their algorithms help complete several tasks and personalize your campaigns, which positively influences your brand identity and customer satisfaction in the long run. Free up valuable resources and time by automating tasks and let your staff focus on more crucial project management and development areas, where AI is still lacking.

Customer care

Visual assistants and chatbots are the prominent samples of AI in practice. These instruments ensure your connection with the target audience isn’t lost, increasing your chances of solving consumer inquiries fast and reducing the bounce and cart abundance probability.

Since it is an effective communication method in real-time, it will take less time and fewer resources to maximize your troubleshooting potential. As research shows, the implementation of AI-empowered customer support solutions can increase the efficiency of non-tech-savvy employees with poor expertise in the field — by 35% at that.

Management Improvements

The increasing adoption of AI-empowered tools will assist businesses in handling their legal documentation and project management matters:

  • The legal industry will benefit from the introduction of artificial intelligence services — by 3.5 times bigger market share by 2028.
  • Another study highlights the shift toward AI tools versus classic project management tactics, which is projected to drop in-field work by 80%.
Generative AI for your business/blog_image 2

Regardless of what niche you occupy, AI business apps are extremely effective for streamlining multiple processes such as budget tracking, accounting, automated reporting, feedback collection, resource forecasting, and much more.

Top 9 business-ready plans to implement GenAI in your workflow

The spidernet of AI is more impressive than it might seem at first — it is revolutionizing the way experts work and reshaping the quality and specifics of jobs too. Given its automation, data analysis, and real-time performance potential, some positions have already lost their primary appeal in the labor market.

The right combination of human- and AI-based initiatives across industries will come in handy to advance business models and start a new era of successful cross-market relations. According to Statista, 23 percent of organizations expect technology to displace jobs, and 49 percent expect AI to create jobs. The professional oversight of employees will control the quality of AI-generated results and ensure personalization and customization needs aren’t omitted in the pursuit of fast efficiency.

Generative AI for your business/blog_image 3

For those interested in data-driven and information-rich projects, it is crucial to teach their staff how to work with ever-changing technological requirements to gain the full GenAI potential. Let’s summarize nine major strategies to make business miracles happen here and now.

1. Flexible and data-driven efficiency

Staying hydrated is the core basics of a healthy lifestyle. The same goes for modern businesses — ensuring your awareness of the latest trends is crucial. Otherwise, you will easily miss crucial innovations in the AI landscape and won’t adjust your company’s structure and workflow to meet ever-changing legal and ethical requirements for AI services.

2. Adhering to AI ethical mindset

Let’s be honest — AI’s source of power is roughly any data is can find over the internet. However, if you don’t comply with specific regulations in the target field, your chance to accomplish your goals will be close to zero:

  • adopting data retention policies;
  • training AI-based systems, including large language models, to gain end-user consent;
  • securing flows of data between departments and partners;
  • using advanced data protection means, including methods like access limits and encryption;
  • aligning with governance processes;
  • conducting comprehensive evaluations of the ethical influence of AI services to avoid worst-case scenarios;
  • improving terms and conditions to match the market needs for service transparency;
  • abiding by industrial guidelines, including HIPAA for healthcare projects and GDPR for distribution services in Europe.

3.Trust-based relations & AI products

In a nutshell, the boom of AI efficiency is impossible without human supervision. Its purpose isn’t to eliminate the traditional job market — it is to make repetitive tasks easier. That’s why it is so essential to adopt the leading standards of AI-human collaboration. If you don’t know how or lack the resources to train your team individually, feel free to request assistance from experienced third parties. COAX experts will unite specialists with several skill sets and next-gen AI strategies under your business’s roof.

The Toyota Research Institute is a marvelous example of how things can ideally work. Its main purpose is to examine the ranges of human-AI collaboration, creating a well-nourished and safe environment for GenAI-driven robotics to be created.

4. Identifying pain points for AI services to solve

Numerous brands don’t dive deeper into the ways AI tools can be actually implemented in their architecture, missing out on the accuracy and versatility they can present. Start by estimating your needs and identifying the areas where GenAI will prove its worth the most. Take your time to decide on how to use AI services — no need to adopt artificial intelligence structures for every department and task to complete.

Besides, your mission is prone to fail unless you consider potential AI risks and mitigate them to align with your business vision, project feasibility, projected revenues, etc. You can already learn from third-party mistakes and start your story with GenAI on your terms and conditions.

5. Data security and privacy

Since AI’s success is impossible without access to information sources and their analysis, your task is to ensure no laws and regulations breaches will take place. Maximum efficiency is a natural outcome of attention to detail and awareness of the crucial standards to stick to:

  • Vulnerability checks — never stop checking your system’s performance and define its weaknesses. Frequent tests will help you locate and fix errors at early stages, avoiding data leaks and ensuring their integrity.
  • Protected storage — the scope of databases varies from company to company, but it doesn’t mean startups and small businesses can afford to be neglectful about their data storage efficiency and safety. Regular updates and checks will assist in addressing the system’s issues and adopt the latest privacy, encryption, and other standards.
  • Limited access control — don’t make access to sensitive data universal. Two-factor authentication is the simplest example of a must-have approach to coordinate your work. Another excellent strategy is to assign certain individuals to reach different parts of your business’s and customers’ data, following the principles of RBAC.
  • Data privacy regulations — before deploying any AI-based product, take care of its data integrity and alignment with the legal landscape in the corresponding market, i.e. GDPR, CCPA, and so on.

6. More about AI capabilities

Despite how accessible AI technologies have become, they require precision and expertise for business purposes that cater to the target audience’s needs and international service quality standards. You can either train your in-house team to become pro-users of AI or outsource third-party experts. Don’t hesitate to combine these methods to achieve the best result — focus on what is more sufficient at the current business or project development stage.

7. Project supervision

Remember the importance of result tracking. Without precise KPIs established based on your brand identity and future development plan, reaching the maximum AI potential won’t be a feasible task. You can use AI-based tools to add more progressive tactics to track your performance, but human supervision and understanding of the ideal outcome are still necessary. If done correctly, the collaboration of AI and human effort to monitor your performance will streamline budget management, supply chain management, stakeholder engagement, scheduling optimization, and other aspects.

8. Sophisticated data infrastructure

AI-based systems are getting more complicated by default. Without high-quality sources of information to review and self-train, such ML systems will be lacking in processing, collecting, and storing data. Start by implementing the following practices to achieve robust and reliable results:

  • Data governance — employ such frameworks to determine the inner and outer policies of the company, e.g. its ownership and data access capabilities.
  • QA — polish your services with special quality assurance procedures. They can be easily automated. The main thing is to validate information before its delivery to AI centers.
  • Catalogs — structurize your data sources and storage, simplifying navigation within the system thanks to clear schema and metatada.
  • APIs — don’t overwhelm your native system. Feel free to use the advancements of professional third parties to advance your AI model.
  • Data integration — take care of various data collection scenarios and stick to consistency and transparency practices in your work. For example, ETL processes are quite efficient for beginners.

9. Existing systems & AI implementation

Last but not least, there is no need to start from scratch if you want to add the GenAI potential to your tool stack. Without proper preparations, though, deploying AI models will stagnate your workflow. From this perspective, it is crucial to provide a robust integration of AI and the existing system’s functionality:

  • Estimating the value of the target implementation strategy — make these processes custom. If you don’t consider the existing system’s capacity, you risk ruining your current databases and increasing the overall business’s vulnerability to cyber threats. As practice shows, seasoned developers don’t have a lot of issues with API-based integration procedures.
  • Considering data compatibility — one of the common beginner mistakes is the assumption that this aspect is of minimal importance. However, the difference in information sources and formats may lead to workflow disruptions and data losses during mitigation processes. 
  • Working with effective models — not all solutions are created universally and equally. The lack of understanding of what you require and what your business is capable of will lead to incomplete data backups and limited operational transparency. Explore the market of AI models to find what will suit your objectives. Along with GPT products, take into account the promising potential of PaLM 2 and Claude.

Wrapping it up

The possibilities of GenAI are nearly limitless. Take into account its huge impact on the market and businesses of any scale, regardless of its current infancy stage. Now is the right time to explore its potential in-depth and adapt to how restructuring its power is in reality. Otherwise, relying on 100% traditional performance in the target niche will prevent you from entering new markets and expanding your horizons.

Harnessing the potential of AI doesn’t only change the way people operate data-related processes. As in the case of chatbots and virtual assistants, such tools will create a robust connection between companies and audiences, increasing brand awareness, ROIs, and online visibility. By adopting the best GenAI strategies, highlighted in this guide, it will be easy to implement next-gen standards of cybersecurity, performance transparency, and operational openness.

With the help of COAX, it won’t take ages to master such complex technology and get early access to cutting-edge websites and applications that will be in demand tomorrow. Lead the market and set new trends with how effective your work on generative AI services is.

Subscribe for our newsletters
Thank you! Your submission has been received!
Oops! Something went wrong
Arrow icon

Featured news