Artificial Intelligence, commonly known as AI, is a term that has gained immense popularity and importance in recent years. From science fiction books to real-life applications, AI has become a buzzword that is reshaping the world as we know it. But what exactly is AI, and how does it work? 

In this blog post, we will delve into the fascinating world of AI, breaking down its key concepts and shedding light on its various facets, including: 

• What is AI? 
• Applications of AI
• Why should you implement it?
• What’s the hesitancy about implementing AI? 
• How to get started implementing AI?
• What kind of team do you need to get started? 
• How do I avoid ethical & regulatory pitfalls? 
• As an AI product company, how do I partner with an AI implementation partner?
• Doing the Implementation 
• What happens post Implementation? 
• What are the factors impacting AI implementation cost? 
• Who are Synergise AI’s competitors? 
• Conclusion

AI, or Artificial Intelligence, is a multidisciplinary field of computer science that focuses on creating machines, software, and systems capable of performing tasks that typically require human intelligence. These tasks include learning from experience, reasoning, problem-solving, understanding natural language, recognizing patterns, and adapting to new information. AI systems aim to mimic the cognitive functions of humans, such as understanding natural language, recognizing patterns, making decisions, and adapting to new information.

Building Blocks of AI

To understand AI better, it is essential to know its fundamental components:

• Machine Learning (ML): Machine learning is a subset of AI that focuses on enabling computers to learn from data. ML algorithms analyze data to identify patterns and make predictions or decisions without being explicitly programmed to perform specific tasks.
• Neural Networks: Neural networks are inspired by the human brain's structure and function. They are a key element in deep learning, a subfield of ML. Neural networks consist of interconnected layers of artificial neurons that can process vast amounts of data, enabling tasks like image and speech recognition.
• Natural Language Processing (NLP): NLP is a subfield of AI that focuses on making computers understand, interpret, and generate human language. It plays a pivotal role in applications like chatbots and language translation.
• Robotics: AI-driven robots use sensors, actuators, and computer algorithms to navigate their environment and perform tasks autonomously. They are used in industries such as manufacturing, healthcare, and agriculture.

AI is applied across various domains, including:

• Healthcare: AI is used for diagnosing diseases, drug discovery, and personalized treatment plans.
• Finance: AI algorithms are employed for fraud detection, algorithmic trading, and credit risk assessment. (Read our AI for Fraud Protection Article here!)
• Autonomous Vehicles: Self-driving cars use AI for navigation and collision avoidance.
• Gaming: AI enhances the realism and challenge in video games by creating intelligent NPCs (non-playable characters).

There are so many more applications of AI - many of which we’ll touch on in this blog post - but these are merely a few common examples of how AI is currently being leveraged.

Sources:

The above information was summarized from the following sources, where you can dive much deeper into what AI is, the building blocks of AI, and its applications.

• "Artificial Intelligence" - Stanford University. https://plato.stanford.edu/archives/win2019/entries/artificial-intelligence/
• "What Is Machine Learning?" - TensorFlow. https://www.tensorflow.org/learn/what-is-machine-learning
• "Neural Networks and Deep Learning" - DeepLearning.ai. https://www.deeplearning.ai/neural-networks-intro/
• "The 10 Most Important NLP Algorithms" - Towards Data Science. https://towardsdatascience.com/the-10-most-important-nlp-algorithms-4a5f68e56b2f
• "The Role of Robotics in Transforming Healthcare" - World Economic Forum. https://www.weforum.org/agenda/2021/02/robotics-transforming-healthcare-aitechnology/
• Russell, S. J., & Norvig, P. (2009). Artificial Intelligence: A Modern Approach. Pearson.
• McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Retrieved from http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

Implementing AI in your business can yield numerous benefits, making it a strategic move for many organizations. Let's explore the reasons why your business should consider incorporating AI into its operations, citing relevant sources where applicable.

1. Enhanced Efficiency and Productivity: AI systems can automate repetitive and time-consuming tasks, allowing employees to focus on more strategic, creative, and value-added activities. According to a report by McKinsey, automation through AI can increase business productivity by up to 20-25% in various industries.
Source: McKinsey & Company - "Notes from the AI frontier: Modeling the impact of AI on the world economy."

2. Data-Driven Decision-Making: AI can process and analyze vast amounts of data in real-time, enabling data-driven decision-making. This results in more accurate and timely insights, which is essential for staying competitive in today's business landscape.
Source: Deloitte - "From data deluge to intelligent insights: Understanding the impact of AI."

3. Improved Customer Experience: AI-powered chatbots, virtual assistants, and personalization algorithms enhance customer interactions. AI can provide 24/7 support, promptly answer inquiries, and offer personalized recommendations, increasing customer satisfaction.
Source: Gartner - "Customer Experience Versus Customer Service: Critical Capabilities for AI Support."

4. Cost Savings: By automating tasks and streamlining operations, businesses can significantly reduce operational costs. According to PwC, AI has the potential to save businesses up to $15.7 trillion in productivity improvements.
Source: PwC - "Sizing the prize: What's the real value of AI for your business and how can you capitalize?"

5. Competitive Advantage: Implementing AI can give your business a competitive edge. As AI adoption continues to grow, organizations that embrace it can innovate faster, deliver better products or services, and meet customer demands more effectively.
Source: Harvard Business Review - "Competing in the Age of AI."

6. Predictive Analytics: AI algorithms can predict future trends, customer behavior, and market changes. This allows businesses to proactively address challenges and capitalize on opportunities, leading to better decision-making and planning.
Source: Forbes - "The Big Promise of Big Data: Analytics That Predict Future Outcomes."

7. Personalization: AI can create highly personalized customer experiences, tailoring product recommendations, marketing messages, and content to individual preferences. Personalization can boost customer engagement and drive higher conversion rates.
Source: Accenture - "Hyper-Personalization: The New Frontier for Customer Experience."

8. Enhanced Security: AI can help protect your business from cybersecurity threats by detecting and responding to anomalies in real-time. It provides an added layer of defense against cyberattacks.
Source: Cisco - "Using Artificial Intelligence for a Proactive Approach to Cybersecurity."

9. Scalability: AI-driven processes can scale easily to accommodate growing business needs, allowing companies to adapt to changing demands without a significant increase in human resources.
Source: McKinsey & Company - "Notes from the AI frontier: Applications and value of deep learning."

10. Insights and Competitive Intelligence: AI can analyze market data, competitor strategies, and customer sentiment, providing valuable insights that can inform business strategies and market positioning.
Source: Nielsen - "AI and Machine Learning Are Helping CPG Companies Serve Consumers Better."

In conclusion, implementing AI in your business offers many advantages, from increased efficiency and cost savings to improved decision-making and competitive advantage. As AI technology continues to evolve, businesses that embrace it can position themselves for long-term success and growth.

According to a recent KPMG report, it's clear that CEOs view generative AI as a priority for their organizations, but they are simultaneously hesitant due to several key factors. This hesitancy is reflected in their approach to investing in AI. Let's break down the main reasons companies might be hesitant to implement AI, using the insights provided by the KPMG report:

1. Ethical and Regulatory Concerns
2. Return on Investment (ROI) Timeline
3. Cost and Technical Skills
4. Cybersecurity Concerns
5. Lack of Industry-Specific Regulation
6. Ethical Challenges
7. Financial Uncertainty

For more on each topic and why it’s of concern read more here.

In conclusion, while CEOs recognize the potential of generative AI for their organizations, their hesitancy is primarily driven by concerns related to ethics, regulation, ROI, cost, technical skills, and cybersecurity. Addressing these concerns and uncertainties is essential for businesses to confidently and responsibly adopt AI technologies in their operations. These challenges reflect the complex nature of AI adoption, which requires careful planning, governance, and ethical considerations.

That said, the cost of not implementing AI is greater. It's crucial to understand that waiting until the last minute to evaluate and implement AI can be detrimental to your business's future. (More on why you should start now here)

Getting started with implementing AI in your business can be a daunting task, but with careful planning and a strategic approach, you can successfully integrate AI into your operations.

Synergise AI offers several free guides to help you evaluate your AI opportunities and preparedness, including:

• AI Business Evaluation - Discover how to strategically evaluate artificial intelligence (AI) and machine learning (ML) readiness for your business to gain a competitive advantage. Our customized approach is designed to meet the needs of business leaders at any level of AI expertise, empowering them to seize growth opportunities.
• AI Data Evaluation - Determine if your data is prepared for AI or if more work needs to be done to make implementation easier.
• Creating an AI Roadmap - This nearly 20-page document details all the questions and considerations you should map out before beginning AI implementation. From evaluating your team’s specialties to getting bottom-up feedback - this comprehensive guide covers it all.

You should also consider creating a proof of concept to ensure everything has been thought through end-to-end and when implemented on a larger scale, it will work.

Additionally, should you need support getting started cleaning up your data or evaluating your business’s preparedness for AI implementation, you can always book a free consultation with our team of AI experts by contacting us here.

The type of team needed depends on the type of project, which is why having an implementation partner with diverse AI experience can help guide you most efficiently and cost-effectively. When it comes to assessing whether or not you should DIY the implementation or hire experts, check out our article here.

To assess what type of team you need, we recommend evaluating the tech, steps, and roles that might be needed for a project. Once you evaluate what’s needed, you can decide if you maybe just need one or two highly specialized AI experts, or if you need a full-stack AI team (more on what that is here).

If you’d like help assessing your AI needs for a project, grab some time with us here!

“Ethical challenges are the most cited obstacle to successfully implementing generative AI—along with cost and technical skills—and 77% of CEOs said the degree of regulation regarding generative AI should mirror that of climate commitments,” according to a recent KPMG study of U.S. CEOs.

So, this begs the question - What should CEOs and other AI leaders in companies do to harness the power of AI to drive growth for their companies, while steering clear of pitfalls? ‍

Our team of experts, who have extensive experience working in AI at companies like Microsoft, Facebook, Amazon, AmEx, Capital One, etc. recommend considering some (or all!) the following action items listed below for best results.
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1. Educate on Capabilities, Limitations, and Consequences of Misuse: Implement training programs on AI ethics and compliance for employees at all levels. Knowledge is power, and ensuring your workforce has the information about how your company is using AI assists in alleviating AI fears, guards against misuse, and strengthens your team’s ability to harness its capabilities.
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2. Establish an AI Ethics Committee: Create a hyper-focused team of individuals from across the organization to better assess AI projects, provide guidance on ethical issues, and ensure compliance with regulations.
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3. Get Involved: Don’t just wait for AI regulation to happen. Engage in dialogue with relevant regulatory authorities and participate in developing AI-related regulations. Proactive involvement helps shape regulations that are favorable to your industry and align with ethical standards.

4. Develop and Adhere to Ethical AI Frameworks: There are many great guidelines that can help govern how AI is developed, deployed, and used within your organization. Ensure transparency, fairness, and accountability in AI systems.

5. Adopt Principles of Responsible AI: Organizations like the Partnership on AI (PAI) or the IEEE, which emphasize fairness, transparency, accountability, and robustness in AI systems, offer great principles!
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6. Get Outside Opinions: Consider third-party audits or reviews of your AI systems to ensure they follow ethical and regulatory standards.

7. Prioritize Transparency and Explainability: Being clear and explaining your thought process to others around the AI decision-making process helps with ethical concerns, but also fosters trust with stakeholders.

8. Promote Diversity in AI Development Teams: Many research studies have consistently demonstrated that the absence of diversity in AI implementation leads to bias within the models. To steer clear of the potential pitfalls associated with bias, it is imperative to foster a team that encompasses a rich tapestry of experiences, backgrounds, and perspectives.

9. Do a Listening Tour, Regularly: Engage with stakeholders, including customers, employees, and the community, to understand their concerns and incorporate their feedback into your AI strategy.

10. Develop a Risk Management Strategy: Preparing for the worst while you’re building for the best strengthens your business plan for risk mitigation and crisis management, should one ever occur. Remember - failing to prepare is preparing to fail.

11. Think about the Big Picture: Harmonize AI investments with a forward-thinking strategy that embraces ethical and regulatory factors. Strive for equilibrium between short-term benefits and the enduring sustainability of your vision.

12. Promote Widespread Communication: Clearly articulate your dedication to upholding ethical AI practices and adhering to regulatory compliance, both within your organization and to external stakeholders. This proactive communication fosters a foundation of trust and credibility.

If your core offering is an AI product or platform and your revenue source is focused on usage, then partnering with an AI implementation company (like Synergise AI) may be a cost-effective solution to onboard new clients, remove roadblocks, and drive usage (and ultimately additional revenue!) for your business.

By partnering with AI implementation experts, you can ensure your team’s focus stays on improving and growing the product to stay competitive in the marketplace, delight current customers, and continue to grow your business. Your AI implementation partner can be white-labeled under your business, operating with internal emails, or be a blended solution depending on your needs.

But no two partnerships are the same, and it's important to ensure you’re getting what you need out of your implementation partner. We’ve created an extensive list of key considerations for when you choose your partner. More on those here.

Keeping AI projects on track and under budget frustrates many business and technical leaders. From how the project is run, to differences between Artificial Intelligence (AI) and software development projects, there are plenty of factors that contribute to time and cost overruns. And sadly, overrun projects often lead to project failures. According to Forbes, 60-80% of AI projects fail.  

Given the odds,  it's important to be aware of potential pitfalls and blockers that can arise during the process. We offer a free resource - Agile for AI: The AI Expert’s Guide to Keeping Artificial Intelligence Projects On Track. The guide offers a methodology created by Synergise AI that’s custom-tailored to apply the principles of Agile to AI projects while incorporating the realities of AI workflows to help your AI project succeed. Synergise’s “Agile for AI” is a blended approach specifically designed for AI projects that focuses on providing visibility to the key stakeholders to enable them to make timely and informed decisions about scope, resources, and timelines.

However, if you’re not looking for a methodology to help keep your project on track, here are key considerations and challenges to keep in mind:

1. Data Quality and Availability
  - Pitfall: Poor data quality can undermine the success of AI projects. Inaccurate, incomplete, or biased data can lead to flawed AI models.
  - Solution: Invest in data quality assessment and cleaning processes. Ensure data availability and create a data strategy. (Evaluate your data)

2. Lack of Expertise
  - Pitfall: Implementing AI without the necessary expertise can lead to ineffective solutions and wasted resources.
  - Solution: Hire or upskill your team, and consider working with AI experts or consultants. (Consider a full-stack AI team)

3. Ethical and Regulatory Concerns
  - Pitfall: Ethical issues related to AI, such as bias, privacy, and fairness, can result in negative publicity or legal challenges.
  - Solution: Develop clear ethical guidelines and ensure compliance with relevant regulations in your industry.

4. Unclear Objectives
  - Pitfall: Not defining clear business objectives for AI projects can lead to aimless efforts and wasted resources.
  - Solution: Clearly define what you aim to achieve with AI, and set measurable KPIs.

5. Integration Challenges
  - Pitfall: Integrating AI into existing systems and processes can be difficult, causing delays and compatibility issues.
  - Solution: Plan integration carefully and consider using APIs and microservices to streamline the process.

6. Scalability Issues
  - Pitfall: AI solutions that work well at a small scale may struggle to perform as the workload increases.
  - Solution: Plan for scalability from the outset, and consider cloud-based solutions for flexible scaling.

7. Overfitting and Model Performance
  - Pitfall: AI models can be overfit to training data, resulting in poor performance on new data.
  - Solution: Employ proper validation and testing techniques and continuously monitor model performance.

8. Cost Overruns
  - Pitfall: AI projects can become more costly than initially anticipated, impacting the ROI.
  - Solution: Create a realistic budget, monitor expenses, and be prepared for potential cost overruns.

9. Change Management
  - Pitfall: Employees may resist AI implementation, viewing it as a threat to their jobs or as a disruptive change.
  - Solution: Communicate the benefits of AI, provide training, and involve employees in the process to alleviate concerns.

10. Incomplete Data Strategy
  - Pitfall: Lacking a comprehensive data strategy can lead to inefficiencies and missed opportunities.
  - Solution: Develop a data strategy that includes data collection, storage, access, and analysis.

11. Lack of Long-Term Planning
  - Pitfall: Focusing solely on short-term gains can hinder the long-term success of AI initiatives.
  - Solution: Develop a comprehensive AI strategy that considers the future and evolves with changing business needs.

12. Measuring Success
  - Pitfall: Failing to define and measure success can make it challenging to assess the value of AI initiatives.
  - Solution: Establish clear KPIs and regularly evaluate performance against these metrics.

13. Security Risks
  - Pitfall: AI systems can introduce new security vulnerabilities and risks.
  - Solution: Implement robust cybersecurity measures and stay informed about evolving threats.

14. Vendor Lock-In
  - Pitfall: Relying on a single AI vendor may lead to dependency and limited flexibility.
  - Solution: Consider open-source AI tools and ensure that vendor contracts allow for data portability.

15. Unproven ROI
  - Pitfall: Demonstrating a clear return on investment for AI initiatives can be challenging.
  - Solution: Develop ROI measurement methods, and be patient, as some AI projects may take time to show results.

Addressing these pitfalls and blockers requires careful planning, flexibility, and a commitment to continuous learning and adaptation. AI implementation is a journey that often involves overcoming obstacles and learning from experience. Therefore, a well-defined strategy and ongoing assessment are crucial for success.

After implementing an AI solution, it's crucial to take several post-implementation steps to ensure its ongoing success and effectiveness. While I cannot provide specific sources for this general knowledge, I can offer a summary of best practices that are widely recognized in the field:

1. Continuous Monitoring and Maintenance
  - Continuously monitor the AI system's performance to ensure it meets its objectives and maintains accuracy. Regularly update and retrain the AI models as needed to adapt to changing data and conditions.

2. User Training and Education
  - Provide training and resources to the end-users and stakeholders who will interact with the AI system. Ensuring that they understand how to use the system optimally is essential.

3. Data Quality Assurance
  - Maintain and improve data quality, as data is the lifeblood of AI. Regularly review and clean data, and ensure data sources remain relevant and reliable.

4. Cybersecurity Measures
  - Implement robust cybersecurity measures to protect AI systems from threats and vulnerabilities. Regularly update and patch software to address security risks.

5. Ethical and Regulatory Compliance
  - Continue to adhere to ethical guidelines and regulatory requirements, ensuring the responsible and ethical use of AI. As AI regulations evolve, adapt your practices accordingly.

6. Feedback Loops
  - Create feedback mechanisms to collect input from users and stakeholders. Use this feedback to make necessary improvements and adjustments to the AI system.

7. Scaling and Integration
  - If applicable, plan for the scaling of AI solutions as the business grows. Ensure that AI systems can integrate with new tools, processes, and platforms.

8. Measuring and Reporting
  - Continuously measure the AI system's impact on the organization. Develop reports and key performance indicators (KPIs) to evaluate its success in achieving business goals.

9. Documentation
  - Maintain comprehensive documentation of the AI system, including data sources, model details, and any custom code. This documentation helps with troubleshooting and future development.

10. User Support
   - Provide ongoing support and helpdesk services for users who have questions or encounter issues while using the AI system.

11. Budget and Resource Management
   - Review the budget and resource allocation for AI projects regularly. Ensure that costs are managed and that the AI system provides a good return on investment.

12. Scalability and Innovation
   - Explore opportunities to innovate and expand the use of AI in your organization as new capabilities or needs arise.

Please note that the specific steps and actions after implementing an AI solution can vary depending on the nature of the AI project, the industry, and business goals. It's essential to create a post-implementation plan tailored to your unique circumstances and regularly assess its effectiveness.

The cost of implementing AI can vary significantly based on several factors, including the complexity of the project, the type of AI technology used, the scale of implementation, and the specific goals of the project. Here are some key cost considerations:

1. Development Costs
  - These costs include hiring or training AI experts, data scientists, and software engineers. Salaries, benefits, and training expenses for the team are part of this category.

2. Data Costs
  - Data acquisition, preparation, and storage can be a substantial cost, especially if you need to gather and process large datasets. Costs may also include data labeling and annotation.

3. Software and Hardware Costs
  - Depending on the AI project, you may need to invest in software tools, cloud services, and hardware resources. Cloud-based AI services may have subscription fees, while building your own infrastructure may involve upfront hardware and software costs.

4. Consulting and Vendor Costs
  - If you work with AI consulting firms or vendors, there will be fees associated with their services and solutions.

5. Training and Education Costs
  - Training your workforce on AI technologies and methodologies may require educational resources, courses, or hiring trainers.

6. Testing and Validation Costs
  - Validating AI models and testing their performance may require time, resources, and possibly external validation services.

7. Integration Costs
  - Integrating AI solutions into existing systems and processes can be expensive, particularly if it involves substantial changes to your technology stack.

8. Regulatory and Compliance Costs
  - Ensuring that your AI solutions comply with industry and government regulations may involve legal and compliance costs.

9. Operational Costs
  - Ongoing operational expenses include maintenance, monitoring, and management of AI systems.

10. Scaling Costs
   - If you plan to scale your AI solutions as your business grows, this may involve additional costs for expanding infrastructure and resources.

11. Security Costs
   - Implementing robust cybersecurity measures to protect AI systems from threats and vulnerabilities can have associated costs.

12. Ethical and Fairness Costs
   - Addressing ethical considerations and ensuring fairness in AI may require investments in tools and practices to avoid bias and discrimination.

13. ROI and Business Value
   - While costs are a significant factor, it's crucial to weigh them against the potential return on investment and the added value that AI can bring to your organization.

The cost of AI implementation can range from thousands to millions of dollars, depending on the project's scope and complexity. Smaller, more focused AI projects may have lower costs, while large-scale AI initiatives, such as developing autonomous vehicles or advanced healthcare systems, may require substantial investments.

It's essential to conduct a detailed cost analysis and create a budget specific to your AI project, taking into account the unique requirements and objectives of the implementation. Additionally, regularly review the budget and expenses as AI projects progress, as costs may evolve throughout the project lifecycle.

The AI implementation landscape is highly competitive, and the effectiveness of a company's services can vary depending on your specific needs and industry. It's essential to thoroughly evaluate potential partners, consider their expertise, and assess their past AI projects and client references when choosing a company for AI implementation.

Here are some of the leading AI implementation companies:

1. IBM: IBM is known for its AI and machine learning solutions, including IBM Watson, which is used for various applications, such as natural language processing, data analytics, and healthcare.

2. Google Cloud: Google Cloud offers a range of AI and machine learning services, making it a top choice for businesses looking to implement AI solutions in the cloud.

3. Amazon Web Services (AWS): AWS provides a suite of AI and machine learning services, allowing organizations to develop, train, and deploy AI models.

4. Microsoft Azure: Microsoft Azure offers AI and machine learning tools, including Azure Machine Learning, which is used for data science and AI model development.

5. Accenture: Accenture is a global consulting and professional services firm that assists organizations in implementing AI solutions across various industries.

6. Deloitte: Deloitte provides AI implementation services and helps businesses leverage AI to improve their operations, customer experiences, and more.

7. Capgemini: Capgemini is a consulting and technology services company with expertise in AI implementation, data analytics, and digital transformation.

8. Cognizant: Cognizant offers AI and analytics services to help businesses implement AI solutions for improved decision-making and efficiency.

9. Infosys: Infosys is an IT services and consulting company that offers AI and automation solutions to assist organizations in their digital transformation journeys.

10. Wipro: Wipro provides AI and cognitive computing solutions, including chatbots, data analytics, and machine learning services.

11. Tata Consultancy Services (TCS): TCS offers AI and automation solutions to enable businesses to become more data-driven and efficient.

12. NVIDIA: While NVIDIA is primarily known for its graphics processing units (GPUs), the company's hardware and software solutions play a crucial role in AI implementation, particularly in deep learning and high-performance computing.

13. Do it yourself: Some companies opt to hire in-house talent to implement AI for themselves. While this can be a great option to help implement with deep company-specific knowledge, in-house engineers might not have every skill set needed to finish a complex challenging product, and that’s where consultants can help.

Implementing AI is a multi-faceted process with numerous considerations. It involves defining objectives, building AI awareness, assessing data quality, identifying use cases, acquiring or developing skills, selecting the right tools, collecting and preparing data, developing and training models, evaluating and testing, deploying, monitoring, and addressing ethical and regulatory concerns.

After implementation, it's essential to engage in continuous monitoring, user training, data quality assurance, cybersecurity, and compliance efforts. A clear feedback mechanism, scalability planning, and ongoing assessment are necessary, all while ensuring ethical AI usage and maintaining a robust budget and ROI analysis.

AI implementation costs can vary widely, influenced by factors such as development, data, software, consulting, and integration. Numerous reputable companies offer AI implementation services, however, the effectiveness of these services can depend on specific project needs and evolving market dynamics. Thus, selecting an AI implementation company requires careful evaluation.

At Synergise AI, we always focus on implementing the right custom solution for you, based on industry best practices and guided by experts with experience levering AI at Microsoft, Facebook, Amazon, American Express, and other major enterprise brands. You can find a slew of free resources that we’re continuing to build out on our resources page here, and we’re always available for a free consultation here.