2024 could be regarded as the year of Artificial Intelligence in business. AI has fundamentally changed how businesses operate, impacting entire business processes, including marketing strategy, revenue growth, daily operations, customer relationships, employee engagement, and even creating new business models.
A 2024 McKinsey State of AI survey reveals that 72% of companies have already adopted Artificial Intelligence in at least some areas, with applications spanning marketing and sales, product development, and IT.
With constant news of AI breakthroughs and many use cases, it's easy to feel both excited and uncertain. Is Artificial Intelligence right for your business?
Gartner’s Hype Cycle for AI in 2024 highlights that AI technology (mainly Generative AI) has passed the "peak of inflated expectations," where over-enthusiasm can lead to disappointment if unrealistic expectations aren't met, yet the hype continues.
While some may be numbed to the rapid pace of development, AI is maturing and moving beyond the hype cycle. Simply using AI tools in isolated instances won't unlock its full potential. The key lies in understanding your needs and aligning AI systems with your business strategy. You must identify scalable business functions across your organization to enhance your business strategy with AI.
In this guide, we’ll equip you with strategies for integrating AI into your marketing strategy, project management, and other business processes. You’ll learn about the different types of AI in business, real-world use cases, costs, and other considerations for implementation.
A look at the key types of AI in business
What is AI, really? Artificial Intelligence refers to the evolving field of computer systems that simulate human cognitive abilities, such as learning, problem-solving, and decision-making.
Knowing the variations helps you recognize potential applications and determine the most beneficial Artificial Intelligence for your business function now and in the future. It also helps in planning a comprehensive AI adoption strategy roadmap.
Different types of AI exist, but the most common ones with practical business applications are Limited Memory AI, Reactive Machines, and Narrow AI. In case you’re wondering, Generative AI (GenAI) is an exciting evolution of Narrow AI.
Limited Memory AI
Limited Memory AI systems can remember past data and make decisions based on it, just like how the human brain processes and recalls information. This type of Artificial Intelligence forms the backbone of most current AI applications.
For instance, self-driving cars use Limited Memory AI to make real-time decisions based on past data from sensors and cameras. Similarly, virtual chatbots and facial recognition technology are built on this model. These systems are trained using vast amounts of data through machine learning, allowing them to recognize patterns and make informed predictions.
A marketing team can use this Artificial Intelligence for data analysis to understand past customer behavior and predict future buying trends. This AI can segment customers based on their previous interactions and tailor personalized recommendations, marketing campaigns, and engagement, enhancing customer satisfaction and conversion rates.
Limited Memory AI could transform predictive analytics in business as data accumulation and processing capabilities continue to grow. Leveraging massive amounts of data could mean more precise demand forecasting, better inventory management, and highly personalized customer experiences. The future may also see advancements in healthcare, where Artificial Intelligence could predict patient outcomes based on historical data and improve treatment plans.
Reactive Machines
Reactive Machines are the simplest form of Artificial Intelligence. They operate purely based on the present input and do not store past experiences or learn from them. Their function is to respond to a specific set of inputs with pre-determined outputs.
A famous example of a Reactive Machine is IBM's Deep Blue, the chess-playing supercomputer that defeated Grandmaster Garry Kasparov. Deep Blue could evaluate numerous positions and make optimal moves without learning or memory capabilities.
A project management tool that uses a Reactive Machine might help schedule tasks based on current project data. It could suggest immediate next steps but wouldn't adapt or improve its suggestions over time.
Manufacturing robots could become even more reliable in the future, performing repetitive tasks with minimal errors. Reactive Machines could also handle routine inquiries in customer service, freeing up human workers for more complex interactions.
Narrow AI
Narrow AI, or Weak AI, is designed to perform a specific task. Unlike human intelligence, Narrow AI cannot generalize from one task to another. Its uses include facial recognition, speech recognition, recommendation systems, process automation, and powering virtual assistants to provide personalized interactions.
Narrow AI has seen incredible advancements recently, especially with developments in Large Language Models (LLMs)and Generative AI powered by machine learning and deep learning models. These systems now excel at various tasks with impressive precision and efficiency, though performance can vary based on the specific model and task.
Generative AI
Generative AI includes technologies that generate new and diverse outputs based on learned data. This category covers tools and models that can create unique content in various media formats. In contrast to Narrow AI, which is task-specific, Generative AI can produce inventive and creative results, opening up new possibilities in art, music, and beyond.
Here are some examples of Generative AI:
- OpenAI's DALL-E and Midjourney can create detailed images from textual descriptions, allowing you to visualize concepts and ideas in a unique and creative way.
- Jukedeck and AIVA specialize in music composition, enabling creators to generate original music tracks tailored to specific moods or themes, adding a new dimension to content creation in entertainment and media.
- DeepArt and RunwayML can transform images and videos into artwork, using neural networks to mimic specific artistic styles or generate entirely new visual content.
Large Language Models (LLMs)
Large Language Models (LLMs) are a subset of Generative AI designed to generate human-like text. They are trained on extensive datasets, allowing them to understand and produce coherent, contextually appropriate text for various applications.
The prominent examples of these LLMs include:
- Google's Gemini LLM, OpenAI's GPT-4, and Meta’s LLAMA2 all fall under this category. They are powerful tools for tasks like Natural Language Processing (NLP - understanding and responding to human language), text generation (creating new text content), and machine translation (converting text from one language to another).
- RAG (Retrieval-Augmented Generation) is a machine learning technique that helps Narrow AI systems like LLMs be even more effective. RAG allows the Artificial Intelligence to "search" for relevant information from external sources (like the internet or vector databases) before generating a response. This empowers the AI to provide more comprehensive and informative answers.
- Vector databases (like Pinecone) are specialized databases that store and manage information to facilitate fast and efficient searches based on similarity. This is particularly helpful for Narrow AI systems dealing with unstructured data (like text) or semi-structured data (like social media posts).
In one of our tutorials, you can see how we improved our chatbot with RAG by combining Gemini, Pinecone, and Langchain (blockchain for language data) to give the chatbot better access to our website information for accurate answers.
Top use cases of artificial intelligence in business
Although the whole article is sprinkled with the various use cases of Artificial Intelligence in business, this section will drill down to seven core real-world use cases for marketing strategies and project management.
1. Predictive analytics and decision-making
AI can analyze data and identify patterns to forecast future trends and help businesses make informed decisions.
Netflix uses predictive analytics to recommend shows and movies. This involves analyzing vast amounts of viewing data to find patterns and predict what you will enjoy next.
2. Customer service automation
How do you ensure every customer gets a quick and helpful response, no matter the time of day? AI-powered chatbots can handle this by providing instant responses to common queries, freeing up human agents to tackle more complex issues.
Bank of America uses an AI-powered virtual assistant named Erica to help customers with tasks like checking balances, paying bills, and even offering financial advice.
3. Personalized marketing
Through customer data analysis, AI can segment audiences and customize marketing messages based on individual preferences, boosting engagement and conversion rates.
Did you know Sephora uses AI to provide personalized beauty recommendations? They analyze purchase history and preferences to suggest products their customers will love.
4. Fraud detection and prevention
Identifying fraudulent transactions among millions of legitimate ones can be quite a challenge. AI excels at detecting unusual patterns and flagging potential fraud in real time. This capability is indispensable for financial institutions looking to protect their customers and assets.
PayPal employs AI to detect and prevent fraudulent activities. Their AI systems analyze transactions in real-time to identify suspicious patterns and flag potential fraud.
5. Supply chain optimization
How do global companies ensure their supply chains run smoothly? AI helps by predicting disruptions, optimizing routes, and managing inventory levels. This leads to cost savings and more efficient operations.
Do you know how Amazon manages to deliver products so quickly? Their sophisticated Artificial Intelligence systems optimize the entire supply chain from warehouse to doorstep.
6. Financial forecasting
AI can analyze market trends, economic indicators, and internal financial data to provide accurate forecasts. This helps companies make informed financial decisions and mitigate risks.
JP Morgan Chase uses AI to improve its financial forecasting. Their AI systems analyze vast market data and economic indicators to provide more accurate financial predictions.
7. Enhancing product development
Finally, AI can analyze customer feedback, market trends, and competitor products to provide insights for product development. This leads to products that are more likely to succeed in the market.
Tesla uses AI to gather and analyze vehicle data to improve autonomous driving capabilities and develop new features that meet customer needs.
6 key considerations before adopting AI in business
An effective Artificial Intelligence strategy ensures the right technology framework is in place and can adapt to new industry trends and breakthroughs. Without a clear plan, you might solve non-existent problems, build unnecessary data infrastructure, or manage AI projects using outdated methods. Please pay attention to these six considerations:
1. Define clear goals.
A robust strategy begins with well-defined objectives by identifying the business needs that artificial intelligence can address. Is it enhancing customer satisfaction? Gaining deeper insights into the customer journey? Streamlining operations? Or exploring new revenue opportunities? These goals will be the compass for your AI integration roadmap and gauging its success.
Artificial Intelligence projects may fail to deliver tangible business benefits if strategic objectives are unclear or overly ambitious. Misaligned goals can lead to wasted resources and missed opportunities to improve operations or customer experiences.
2. Ensure quality and accessibility of data.
The strength and success of AI depends on the quality and accessibility of data. Inaccurate or incomplete data can undermine AI accuracy and decision-making, resulting in flawed insights and ineffective business strategies. Poor data quality may also amplify biases in Artificial Intelligence algorithms, leading to discriminatory outcomes.
A good example is Microsoft's Tay chatbot, which parroted offensive views after learning from social media. Amazon's automated hiring tool discriminated against women because it was trained on resumes biased toward men.
Establish a data strategy focused on sourcing, managing, and maintaining clean, reliable data. This safeguards your Artificial Intelligence from biases and inaccuracies.
3. Choosing the right tools and infrastructure.
Businesses need infrastructure to handle various data types (structured and unstructured data) and volumes throughout the AI lifecycle (from data collection to processing, training, and prediction). This includes high-performance storage and compute power for tasks like training deep learning models. Public cloud and container platforms offer scalable and agile solutions, while chip choices depend on specific Artificial Intelligence applications.
For illustration, our tutorial on adding conversation to an RAG chatbot might seem simple but involves many considerations. This includes storing the information, retrieving the conversation history, and deciding how much of the conversation to consider, especially when dealing with limited context for LLMs.
Poor technology choices or inadequate infrastructure can result in compatibility issues, system downtime, and increased maintenance costs. This can hinder scalability and limit your organization's ability to effectively leverage AI advancements.
4. Establish governance.
Effective governance minimizes potential risks and maximizes return on investment (ROI). Develop clear policies, control access to AI systems, and evaluate potential risks. You will want to avoid using Artificial Intelligence models that can be easily manipulated to reveal sensitive business data.
Treat governance with the same rigor as financial planning and team management. Organizations risk data breaches, regulatory fines, and reputational damage without robust governance. Lack of transparency and accountability can erode trust among stakeholders and lead to legal challenges.
5. Time and cost management.
Artificial Intelligence deployment requires significant investments in time and resources. Unfamiliarity with AI tools can drive up costs—factor in technology acquisition, system updates, and ongoing maintenance expenses. Efficient deployment relies on well-structured systems and expertise across diverse teams.
Here's a breakdown of the cost estimation for AI projects:
In total, a small AI project can cost $400,000+ per year.
Note that these costs depend on the scope of the project and the use case. Talk to us for a tailored cost estimation to integrate AI into your business.
Overestimating budget allocations or underestimating implementation costs can strain financial resources.
Poor resource management may delay project timelines, hinder scalability, and limit your organization's ability to capitalize on AI ROI.
6. Continuous improvement is essential.
Inadequate monitoring and evaluation of Artificial Intelligence performance can result in missed opportunities for improvement and innovation. Failure to scale successful AI initiatives may limit their impact and diminish competitive advantage in rapidly evolving markets. Do these:
- Maintain an agile AI strategy that adapts and evolves in automating routine tasks and other critical business functions.
- Implement systems to monitor AI performance against established goals.
- Apply valuable insights from operational AI systems to continually enhance their effectiveness.
- Remain flexible to adjust strategies, scale successful initiatives, and phase out less productive ones.
- Adopting a proactive management measure ensures your AI strategy aligns with business objectives and market dynamics.
Consider leveraging external help
Implementing Artificial Intelligence to tackle business challenges requires a good strategy and careful consideration of data quality, technology, governance, and resources. This might seem intimidating, but partnering with an experienced AI Agency like Monogram can streamline the process, ensuring success and mitigating risks.
Our expertise covers many facets of AI implementation to optimize for a competitive edge. We are the pioneers of the AI-first web, and you can read about some of our Artificial Intelligence projects and use cases here: