So, you're ready to bring an AI expert onto your team. That's a great decision, but the path from here to a successful hire can be complex. What technical skills are non-negotiable? Where do you even begin to look for top talent? How do you create a compensation package that attracts the best without breaking the bank? This guide is your comprehensive playbook. We'll cover everything from writing a job description that gets noticed to building a retention strategy that keeps your team engaged for the long haul. Consider this your step-by-step plan to confidently hire artificial intelligence engineers and build a high-performing team.
Key Takeaways
- Treat Hiring as a Strategic Investment: Attracting top AI talent requires a thoughtful plan that goes beyond a simple job post. A successful approach combines a compelling job description, a structured interview process, and a competitive compensation package that reflects the role's high value.
- Look for Proven Ability, Not Just Credentials: The best way to assess an AI engineer is to see their problem-solving skills in action. Use practical take-home assignments and ask deep questions about past projects to understand how a candidate handles real-world challenges and delivers results.
- Focus on Retention from Day One: The work isn't over once you've made a hire. To get the most from your investment, create an environment that fosters growth with challenging projects, clear career paths, and continuous learning opportunities to keep your team engaged and innovative.
Why Hire an AI Engineer?
Bringing an AI engineer onto your team is one of the most strategic moves you can make for your business. It’s about more than just keeping up with tech trends; it’s about fundamentally changing how you operate, compete, and grow. An AI engineer doesn't just write code—they build intelligent systems that can solve complex problems, automate tedious tasks, and uncover insights hidden within your data. This expertise translates directly into measurable business value. They are the architects of the systems that will power your company's future. Let's break down the core reasons why this role is so critical.
Measure the Business Impact and ROI
One of the most compelling reasons to hire an AI engineer is the direct impact they have on your bottom line. These professionals build systems that analyze vast amounts of information to spot patterns and trends, which helps guide your business decisions. By leveraging AI, your company can move from guesswork to data-driven strategies that improve operational efficiency and profitability. An AI engineer can develop tools that automate workflows, reduce human error, and optimize resource allocation, all contributing to a strong return on investment. They help you answer critical business questions with data, not just intuition.
Gain a Competitive Edge
In a crowded market, a skilled AI engineer can be your secret weapon. They build the custom solutions that set your business apart from the competition. Think personalized customer recommendations that increase sales, predictive maintenance that prevents costly downtime, or fraud detection systems that protect your assets. While competitors might use off-the-shelf software, your in-house AI engineering talent can create proprietary systems tailored to your business needs. This unique capability allows you to offer better products and services, positioning your company as a leader in your industry.
Drive Innovation and Growth
AI engineers are natural innovators. They don't just solve today's problems; they build the foundation for tomorrow's opportunities. By hiring an AI engineer, you are investing in your company's long-term growth. They can help you develop entirely new products, create smarter services, and even discover new business models. This fosters a culture of innovation where experimentation and learning are encouraged. An AI engineer can explore what's possible with emerging technologies, ensuring your business remains relevant and continues to scale in an ever-changing landscape.
What to Look For in an AI Engineer
Finding the right AI engineer is about more than just matching keywords on a resume. You’re looking for a unique blend of technical expertise, practical experience, and collaborative spirit. A great AI engineer doesn't just write code; they understand your business goals and can translate them into functional, innovative AI solutions. They are problem-solvers at heart, equipped with the tools to build, train, and deploy models that can make a real impact.
To identify a top-tier candidate, you need to assess a combination of hard and soft skills. While technical proficiency is the foundation, their ability to communicate complex ideas and work within a team is what will truly set them apart. Let’s break down the key areas to focus on during your search to ensure you find someone who can help you achieve your company’s vision.
Core Technical Skills
Beyond the buzzwords, a strong AI engineer needs a solid grasp of fundamental concepts. This means you should look for a deep understanding of machine learning algorithms, data structures, and data modeling. They should be able to explain not just what an algorithm does, but why it’s the right choice for a specific problem. Experience with data processing and validation is also critical, as the quality of an AI model is entirely dependent on the quality of the data it’s trained on. A candidate who can confidently discuss these core principles is likely to have the strong technical foundation you need for your AI engineering team.
Key Programming Languages
Proficiency in the right programming languages is a must-have. Python is the undisputed leader in the AI space due to its extensive libraries and frameworks, so fluency here is almost always a requirement. Depending on your existing tech stack, experience with other languages like Java, R, or C++ can also be highly valuable. Alongside programming skills, a solid foundation in mathematics—particularly statistics, probability, and linear algebra—is essential. This mathematical knowledge is what allows an engineer to truly understand and innovate with the models they build, rather than just implementing them. A candidate with a strong command of these languages and principles is well-equipped to handle complex data science challenges.
Essential AI and ML Frameworks
An engineer’s toolkit is defined by the frameworks they use, and in AI, a few key players dominate. You’ll want to see hands-on experience with major frameworks like TensorFlow or PyTorch. These are the power tools that engineers use to build and deploy sophisticated neural networks and other machine learning models. Familiarity with other libraries in the ecosystem, such as Scikit-learn for data analysis or Keras for simplifying model building, is also a huge plus. When you hire an AI engineer, ask about specific projects where they’ve used these frameworks to get a real sense of their practical skills and ability to deliver results.
Relevant Industry Experience
A resume can tell you what a candidate has studied, but their project portfolio tells you what they can do. Look for a history of relevant, hands-on experience with AI projects. Have they built a recommendation engine from scratch? Deployed a natural language processing model into a live application? This practical experience is invaluable because it shows they can handle the real-world complexities that don’t show up in textbooks. It also demonstrates their ability to see a project through from concept to completion. Don’t just focus on years of experience; focus on the quality and impact of the work they’ve done in machine learning.
Crucial Soft Skills
Technical prowess alone doesn’t make a great team member. An AI engineer needs strong soft skills to succeed, especially communication and collaboration. They must be able to explain highly technical concepts to non-technical stakeholders and work effectively with other engineers, data scientists, and product managers. Look for candidates who are curious, adaptable, and demonstrate strong problem-solving abilities. During the interview, ask questions that reveal how they approach challenges and work with others. Finding someone who fits your team’s culture is just as important as finding someone with the right technical qualifications.
How Much Does an AI Engineer Cost?
Figuring out the cost of hiring an AI engineer isn't as simple as looking up a single number. The total investment depends on a mix of factors, including their experience, where they're located, and the benefits you offer. Understanding these components is the first step to creating a competitive compensation package that attracts the right talent without breaking your budget. Let's walk through the key elements that will shape your hiring costs.
Salary by Experience Level
An engineer’s experience is a primary factor in their salary. A recent graduate will have different expectations than a senior engineer leading complex projects. In the United States, a full-time AI engineer typically earns between $80,000 and $150,000 annually. For contract roles, hourly rates can range from $50 for junior talent to over $200 for seasoned experts. When defining a role, think about the level of expertise you truly need. This helps you set a realistic salary range for your next AI engineering hire.
How Location Affects Pay
Where your AI engineer is based plays a huge role in their compensation. An engineer in a tech hub like San Francisco will command a higher salary than someone in a lower-cost city. The global talent pool also presents a wide range of salary expectations. For example, while a US-based AI engineer might cost $46 to $58 per hour, you might find talent in Latin America for $20 to $50 per hour. As remote work becomes more common, it’s important to understand local market rates to make a competitive offer wherever you find your talent.
Factoring in Benefits and Perks
A great salary is just one piece of the puzzle. The total cost of hiring an employee includes benefits, perks, and other associated expenses, which can add thousands to your budget. A competitive package typically includes health insurance, retirement plans, and paid time off, plus costs for taxes and equipment. In a competitive field like AI, a strong benefits package is essential for attracting and retaining top candidates. Thinking through your complete hiring solutions will help you create an offer that stands out.
How to Plan Your Hiring Budget
Before you start interviewing, you need a clear and realistic budget. This means looking beyond the base salary to calculate the total cost of employment. Start by defining the business goals for the role and then research market rates for the experience you need. Knowing your budget helps you understand if you can hire a small team or a single senior expert. To get the most accurate picture, work with a specialized recruitment partner who understands the nuances of the Data Science & Analytics market and can provide insights into current salary trends.
Where to Find Top AI Engineers
Finding the right AI engineer can feel like searching for a needle in a haystack, but it’s much easier when you know where to look. The best talent isn’t always actively applying for jobs; often, they’re deeply engaged in their work, contributing to open-source projects, or leading discussions in niche online communities. To find them, you need a multi-channel approach that goes beyond traditional job postings.
Tapping into specialized networks is key. This could mean partnering with a recruitment agency that lives and breathes AI, exploring platforms where engineers showcase their projects, or building relationships with universities that are cultivating the next generation of innovators. Each channel offers access to a different segment of the talent pool, from seasoned experts to brilliant up-and-comers. By diversifying your search, you not only increase your chances of finding the perfect candidate but also gain a better understanding of the talent landscape. The goal is to meet engineers where they are, engaging them in a way that respects their expertise and aligns with your company’s goals.
Specialized AI Recruitment Agencies
Working with a recruitment agency that specializes in artificial intelligence can save you an incredible amount of time and effort. These firms have deep networks of pre-vetted candidates and understand the specific skills required for complex AI roles. Agencies like us at People in AI focus exclusively on connecting companies with top-tier AI and ML talent, ensuring a strong match for both technical skills and cultural fit. Other established tech recruiters like Robert Half Technology, Randstad, Michael Page, and Korn Ferry also have dedicated divisions for sourcing AI professionals, leveraging their extensive reach to find qualified candidates for your team.
Online Job Boards
While general job boards can be useful, niche platforms tailored to tech and AI talent often yield better results. Websites like Upwork, Lemon.io, and Arc.dev are designed to connect companies with freelance and full-time AI engineers who have specific expertise. These platforms often feature portfolios and skill verification, which helps you quickly assess a candidate's capabilities. Posting on AI-specific job boards ensures your opportunity is seen by a relevant audience of professionals who are actively looking for their next role in the field, cutting through the noise of more generalized sites.
Professional Networks
Top AI engineers are often active in professional communities where they share knowledge, collaborate on projects, and build their reputations. Platforms like GitHub are invaluable because they allow you to see a developer's code and contributions firsthand. You can identify talented individuals by looking at who is contributing to popular AI/ML libraries or maintaining impressive personal projects. Niche subreddits and other online forums dedicated to machine learning and data science are also great places to engage with potential candidates, ask insightful questions, and show that your company is an active participant in the AI community.
University Partnerships
Forging relationships with universities and research institutions is a smart, long-term strategy for building a talent pipeline. Many of the brightest minds in AI come directly from strong academic programs. By connecting with computer science and data science departments, you can get access to emerging talent through career fairs, guest lectures, and internship programs. These partnerships are especially effective for finding candidates with fresh perspectives and a deep understanding of the latest theoretical advancements, making them ideal for research-focused roles or junior engineering positions on your team.
Which Industries Need AI Engineers Most?
While AI is making its way into nearly every field, some industries are leading the charge, creating an incredible demand for skilled engineers. These sectors are not just experimenting with AI; they are fundamentally rebuilding their operations around it. For companies in these areas, hiring top AI talent isn't just an advantage—it's essential for survival and growth. Understanding where the demand is most intense can help you position your company to attract the right people or, if you're an engineer, find the most exciting opportunities. Let's look at the key industries where AI engineering roles are booming.
Healthcare and Biotech
The healthcare industry is using AI to make huge leaps in patient care and operational efficiency. AI engineers are developing sophisticated algorithms that can analyze medical images with incredible precision, often spotting diseases earlier than the human eye. They also build systems that personalize treatment plans based on a patient's genetic makeup and lifestyle. Beyond patient-facing applications, AI helps hospitals optimize workflows, manage resources, and reduce operational costs. This means better outcomes for patients and more sustainable healthcare systems. The demand for engineers who can build and maintain these life-changing technologies is growing every day.
Finance and Banking
In the world of finance, AI is a game-changer. Financial institutions handle massive amounts of data, and AI engineers are creating the tools to make sense of it all. They build systems for algorithmic trading, enhance fraud detection to protect customer accounts, and develop personalized financial advice platforms. By automating routine processes and providing deeper insights, AI allows banks and financial firms to make smarter, faster decisions. This not only improves the customer experience but also strengthens security and ensures regulatory compliance. As a result, professionals with expertise in data science and analytics are highly sought after.
Manufacturing
Manufacturing has been transformed by AI and automation. AI engineers in this sector focus on creating "smart factories" where production lines are more efficient and reliable. They develop predictive maintenance systems that can anticipate when a machine will need repairs, preventing costly downtime. They also design machine learning models to optimize supply chains, ensuring materials arrive just in time and finished products are delivered efficiently. By automating quality control with computer vision and streamlining logistics, AI helps manufacturers reduce waste, lower costs, and improve the quality of their products.
Retail and E-commerce
The retail landscape is fiercely competitive, and AI is the key to staying ahead. AI engineers are the architects behind the personalized shopping experiences we've come to expect. They build recommendation engines that suggest products you'll love, create chatbots that provide instant customer service, and develop systems for dynamic pricing and inventory management. By analyzing customer behavior and sales data, these engineers help retailers understand what shoppers want and how to deliver it. This data-driven approach improves customer satisfaction, increases sales, and makes the entire retail operation more efficient from warehouse to checkout.
Transportation and Logistics
From self-driving cars to supply chain management, AI is at the heart of modern transportation and logistics. AI engineers in this field are working on some of the most complex challenges, like optimizing delivery routes in real-time to save fuel and time. They also develop systems for automating warehouse operations with robotics and improving the accuracy of demand forecasting. These advancements are making the movement of goods faster, cheaper, and more reliable. As the industry continues to innovate, the need for engineers skilled in machine learning and optimization will only continue to grow.
How to Create an Effective Hiring Process
Finding the right AI engineer isn't just about posting a job and hoping for the best. A thoughtful, structured hiring process is your best tool for identifying and attracting top talent while ensuring a great experience for every candidate. When you map out each step, from the job description to the final offer, you signal to applicants that your organization is serious, professional, and a place where they can do meaningful work. A well-defined process helps you evaluate candidates consistently, reduce bias, and make a final decision with confidence. It also respects the candidate's time, which is critical in a competitive market where top engineers often have multiple offers. By being organized and transparent, you build trust from the very first interaction. This structured approach prevents great candidates from falling through the cracks and ensures your hiring team is aligned on what they’re looking for. It’s about creating a clear path that allows you to assess technical skills, problem-solving abilities, and cultural alignment effectively, ensuring the person you hire will be a long-term asset. Let’s walk through the key stages of building a hiring process that helps you land the perfect AI engineer for your team.
Write a Job Description That Attracts Talent
Your job description is the first impression a candidate has of your company, so make it count. Go beyond a simple list of duties and create a compelling narrative about the role. Clearly define the problems the new hire will solve and the impact their work will have on the business. Be specific about the required technical skills, including programming languages and frameworks. To attract the best AI engineering talent, you also need to post your opening where they’ll see it. Leverage specialized job boards and AI communities on platforms like GitHub and LinkedIn to ensure your role reaches candidates who are actively engaged in the field and looking for their next great opportunity.
Assess Technical Skills Effectively
Once applications start coming in, you need a reliable way to gauge technical ability. The key is to move beyond generic coding quizzes. Instead, evaluate candidates on their experience with relevant AI frameworks like TensorFlow or PyTorch and their ability to solve real-world problems. A practical, take-home assignment that mirrors a project they would actually work on is an excellent way to see their skills in action. This approach not only tests their technical know-how but also gives you insight into their problem-solving process and how they structure their work. Follow this up with a technical interview where they can walk through their solution with one of your senior engineers.
Ask the Right Interview Questions
The interview is your chance to understand the person behind the resume. Move past standard questions and focus on digging into their practical experience. Ask them to describe a complex machine learning project they worked on, including the challenges they faced and how they overcame them. Inquire about their process for selecting the right model for a specific problem and ask for an example. These types of questions reveal a candidate's problem-solving abilities, critical thinking skills, and depth of knowledge far better than any hypothetical question ever could. The goal is to start a conversation that shows you how they think and approach their work.
Evaluate for Cultural Fit
Technical expertise is crucial, but it’s just one piece of the puzzle. A new hire also needs to align with your team’s culture and values to truly thrive. Cultural fit isn’t about hiring people who all think and act the same; it’s about finding someone who complements your team’s dynamic and shares your company’s core principles. During the interview process, set aside time for candidates to meet potential teammates in a more informal setting. Ask questions about how they prefer to collaborate, give and receive feedback, and handle disagreements. Understanding their communication abilities and work style will help you determine if they’ll integrate well and contribute positively to your environment.
Verify Skills and Experience
Before you extend an offer, take the time to verify the candidate's background and experience. This goes deeper than just confirming dates of employment. When speaking with references, ask specific questions about their contributions to projects. Check their experience with preparing and managing data, and ask for examples of how they’ve put AI models into production and ensured they performed well. For many roles in data science and analytics, this practical experience is what separates a good candidate from a great one. This final step helps confirm that their skills and experience are as strong as they appeared throughout the interview process.
How to Build and Retain Your AI Team
Hiring a talented AI engineer is a huge win, but it’s only the first step. The real challenge is creating an environment where they can do their best work and want to stay for the long haul. High turnover is costly, not just financially but also in terms of lost knowledge and momentum. A strong retention strategy is your best defense against the competitive AI talent market. It’s about more than just preventing people from leaving; it’s about building a team that is engaged, innovative, and committed to your company’s vision. The following strategies will help you create a workplace that attracts top AI professionals and encourages them to grow with you. By focusing on structure, compensation, growth, and engagement, you can build a resilient and high-performing AI team that drives your business forward.
Structure Your Team for Success
Building a successful AI team is like putting together a puzzle—every piece needs to fit perfectly. It’s not enough to just hire smart engineers; you need to think about how their roles connect and contribute to the bigger picture. Start by defining clear roles and responsibilities for each team member, from AI Engineers to Data Scientists. A well-defined structure prevents confusion and ensures everyone knows how their work impacts business goals. Whether you opt for a centralized team that serves the whole organization or embed AI experts within different business units, the key is to foster collaboration and clear communication. This clarity allows your team to focus on what they do best: analyzing data to find trends that guide your most important decisions.
Develop a Competitive Compensation Package
In the world of AI, a competitive salary is table stakes. To truly attract and keep top talent, you need to think about the entire compensation package. This includes a strong base salary, performance-based bonuses, and comprehensive benefits. But don't underestimate the power of equity. For many AI professionals, having a stake in the company's success is a major motivator. Some companies are even using off-cycle equity grants to retain their key players, which is a smart move in a fast-paced market. When you craft your offer, think holistically. A package that includes great health insurance, flexible work options, and a solid retirement plan shows candidates you’re invested in their long-term well-being, not just their immediate output.
Offer Professional Growth Opportunities
The field of AI changes at lightning speed, and the best engineers are lifelong learners. If you want to retain your team, you have to invest in their growth. This means creating a culture of continuous learning where curiosity is encouraged and supported. You can do this by offering a dedicated budget for professional development, which employees can use for conferences, workshops, or online courses. Beyond formal training, think about creating clear career paths within the organization. When your team members can see a tangible future and opportunities for advancement at your company, they are far more likely to stick around. Mentorship programs are another fantastic way to support skill development and build stronger internal connections.
Keep Your Top Talent Engaged
An unengaged employee is an employee who is looking for their next role. Keeping your AI team engaged comes down to providing them with meaningful and challenging work. Top engineers are driven by solving complex problems, so give them projects that stretch their skills and have a real impact on the business. It’s also crucial to build a strong, collaborative culture where ideas can be shared freely and innovation can flourish. Regular check-ins, constructive feedback, and public recognition for great work go a long way. When people feel that their contributions are valued and that they are part of a supportive team, their job satisfaction and loyalty increase dramatically. This is the kind of environment that makes people excited to come to work every day.
Set Clear Performance Goals
AI professionals are analytical thinkers who thrive on clarity. Vague expectations are a recipe for frustration and disengagement. To keep your team aligned and motivated, set clear, measurable performance goals that connect directly to your company's strategic objectives. When your engineers understand exactly what they need to achieve and how their work contributes to the bottom line, they are more empowered to succeed. These goals should be ambitious but realistic, and they should be reviewed regularly. This approach not only helps individuals track their progress but also provides a transparent framework for performance reviews, promotions, and compensation discussions. It creates a fair system where everyone knows what it takes to get ahead.
Overcome Common AI Hiring Challenges
Hiring top AI talent can feel like a major hurdle. The demand for skilled engineers far outstrips the supply, budgets are tight, and finding the right fit for your team is more complex than just matching keywords on a resume. But these challenges aren't insurmountable. With a strategic approach, you can build a process that attracts the right people and sets them up for success. Let's walk through some of the most common obstacles and how you can get past them.
Address the AI Talent Shortage
The simple truth is there aren't enough qualified AI engineers to go around. This means you can't just post a job and wait for the perfect candidates to apply. You need to be proactive. Go where the talent is by engaging with AI communities on platforms like GitHub and Kaggle. A strong presence on professional networks like LinkedIn is also essential. To get a real edge, consider partnering with a specialized AI recruitment agency. We have established networks and can connect you with passive candidates—highly skilled professionals who aren't actively job hunting but are open to the right opportunity.
Close Internal Skill Gaps
Sometimes, the best candidate is already on your payroll. Before looking externally, assess your current team's potential. Investing in your employees' growth by providing training budgets, access to online courses, and professional development opportunities can be a powerful move. Creating a culture of continuous learning not only helps you fill critical skill gaps but also shows your team you're invested in their careers. This approach can be more cost-effective than hiring new staff and significantly improves employee retention and morale. A mentorship program pairing senior engineers with those looking to grow can also accelerate development.
Work Within Your Budget
AI engineers command high salaries, and competing with tech giants on pay alone can be tough. If your budget is a constraint, get creative with your compensation package. While a competitive salary is important, you can sweeten the deal with other incentives. Consider offering meaningful equity, performance-based bonuses, or generous benefits. Don't underestimate the value of non-monetary perks, either. Things like flexible work hours, a remote-first culture, and a clear path for career advancement can make your offer stand out. It's about presenting a holistic value proposition that aligns with what top candidates are looking for beyond just a paycheck.
Ensure a Smooth Team Integration
Hiring a brilliant AI engineer is only half the battle; making them a successful part of your team is the other. A well-structured onboarding process is critical. From day one, your new hire should have a clear understanding of their role, responsibilities, and how their work contributes to the company's goals. Foster a collaborative environment where they can easily connect with colleagues across different departments. Building a successful AI team is about more than technical skill; it's about integrating new expertise into your existing workflows and culture to drive collective success.
Future-Proof Your AI Team
The field of AI is constantly changing, so building a team that can adapt is essential. Encourage your engineers to stay on top of the latest trends and technologies. Support their professional growth by giving them opportunities to attend conferences, participate in workshops, or contribute to open-source projects. This commitment to reskilling and upskilling not only keeps your team's skills sharp but also positions your company as a leader in the industry. When your team is always learning, they're better equipped to tackle future challenges and drive innovation.
Frequently Asked Questions
What’s the real difference between an AI Engineer and a Data Scientist? It's a great question because the roles can overlap. Think of it this way: a Data Scientist is like a detective who analyzes data to uncover insights and build predictive models. They answer the "what if" questions. An AI Engineer is the builder who takes those models and integrates them into functional, scalable products. They are focused on production, making sure the AI system works reliably in the real world. You need the scientist for discovery and the engineer for application.
Should my first AI hire be a senior engineer or can I start with someone more junior? For your very first AI hire, it's almost always better to invest in a senior engineer. This person will be responsible for setting the technical direction, choosing the right tools, and establishing best practices for your entire AI strategy. A junior engineer, while talented, needs mentorship and a solid framework to work within. A senior professional can build that framework and even help you hire and train more junior talent down the line.
How can I effectively assess an AI engineer's skills if I'm not a technical expert? You don't need to be a coder to spot a great candidate. Focus on their past work and problem-solving approach. Ask them to walk you through a complex project from their portfolio, explaining the business problem, their solution, and the final impact. A practical take-home assignment that mirrors a real challenge your company faces can also be very revealing. For the final technical validation, you can bring in a trusted technical advisor or partner with a specialized agency that can handle that deep-dive assessment for you.
Is it better to hire a full-time employee or a contractor for an AI project? This really depends on your goals. If you have a specific, well-defined project with a clear start and end date, a contractor or freelancer can be a fantastic option to get the job done efficiently. However, if you're looking to build a core competency and make AI a central part of your long-term business strategy, you'll want to hire a full-time employee. This invests that knowledge and expertise directly into your team for the long haul.
Besides a high salary, what really attracts top AI talent to a company? Top engineers are driven by more than just money. They are looking for challenging problems to solve and a clear sense of purpose. The best way to attract them is to offer meaningful work where they can see the direct impact of their contributions on the business. They also value a culture that supports continuous learning, provides opportunities for professional growth, and gives them the autonomy to experiment with new technologies.
