Arguably, one of the things that nobody—not even the tech giants—could have imagined is the unprecedented speed and complexity with which AI is developing. The evolution of landline telephones to mobile phones occurred over decades, as did mainstream access to the internet, from fixed computers to devices we wear on our wrists and carry in our pockets. Conversely, AI seems to progress every minute, and keeping up is difficult!

While scientists, engineers, and policy-makers debate whether these changes are good or bad and the implications of widespread automation for society, businesses must stay ahead of the curve to survive and thrive in today’s rapidly and constantly changing landscape. With this goal in mind, we’ve developed a comprehensive guide to AI Literacy with a concise list of the essential AI tools for business that will help you automate routine tasks, reduce costs, improve customer engagement, and maximise your business potential.

PART ONE

AI Glossary & Key Terms

Before diving into specific tools, familiarising yourself with AI terminology is essential to navigating the landscape. 

Artificial General Intelligence (AGI): A type of AI that can perform any intellectual task that a human can do. Systems that can genuinely be classified as AGI have yet to be created. Still, unlike current AI systems, which specialise in specific tasks, AGI could understand, learn, use abstract thinking, and apply knowledge across various fields.  

Example: Imagine an AI system that not only drives a car but can also autonomously switch to managing a business, performing scientific research, and learning new skills as needed—all with human-level adaptability and reasoning. This versatility is what separates AGI from task-specific AI, such as self-driving cars

Artificial Intelligence (AI): The simulation of human intelligence by machines, especially computer systems. It allows machines to perform tasks like decision-making, problem-solving, and learning.  

Example: AI in an email service can automatically filter spam or suggest email responses.

Automation: Using AI or other technologies to perform tasks without human intervention.  

Examples: Automated grading systems in education technology, medication reminders for seniors, or irrigation systems in precision farming. 

Chatbot: A computer program that simulates conversations with human users, typically via text or voice.  

Example: Many websites have chatbots that help you with customer service, answering frequently asked questions like store hours or return policies.

Cognitive Computing: Systems that mimic human thought processes to solve complex problems. It focuses on reasoning and understanding language, similar to how humans do.  

Example: IBM Watson is a cognitive computing system that assists doctors by analysing medical data to suggest potential diagnoses.

Computer Vision: Allows machines to interpret and make decisions based on visual inputs like images or videos.  

Example: Computer vision in healthcare is crucial in identifying anomalies such as tumours and diagnosing diseases.

Conversational AI: The technology that enables computers to engage in natural, human-like conversations, often through voice or text interfaces.  

Example: Virtual assistants like Siri or Alexa use conversational AI to understand and respond to spoken questions or commands.

Data Mining: Analysing large datasets to find patterns, trends, or valuable information.  

Example: Online retailers use data mining to analyse customer purchase histories and recommend similar products.

Deep Learning: A subset of machine learning, deep learning uses neural networks with many layers (deep neural networks) to model complex patterns in large datasets.  

Example: Deep learning powers image recognition in photo apps that can identify objects like trees, people, and animals.

Ethical AI: The development and use of AI technologies responsibly and in ways that respect privacy, fairness, and transparency and avoid harm to individuals or society.  

Example: An ethical AI approach in facial recognition software would ensure it doesn’t exhibit bias or discriminate against specific demographics.

Generative AI: Creates new content such as text, images, or music by learning from existing data.  

Example: OpenAI’s GPT-4 generates human-like text, Leonardo.Ai generates images, creates images and brings ideas to life, and Soundful helps creatives generate royalty-free music for videos, podcasts, etc. 

Generative Pre-trained Transformer (GPT): A machine learning model that generates human-like text based on prompts.  

Example: GPT models, like GPT-4, create text for chatbots, write articles, or even generate code.

Machine Learning (ML): A subset of AI that allows computers to learn from data and improve their performance without being explicitly programmed for each task.  

Example: Netflix uses machine learning to recommend shows based on your viewing history.

Natural Language Generation (NLG): The process of generating human-like text from structured data.  

Example: Automated financial reports summarising business earnings data are often created using NLG technology.

Natural Language Processing (NLP) Allows AI to understand and process human language meaningfully and usefully.  

Example: Google’s search engine uses NLP to interpret and respond to user queries more effectively 

Neural Network: A machine learning model inspired by the human brain, consisting of layers of interconnected nodes (neurons) that process information.  

Example: Neural networks are used in facial recognition systems to identify people in photos.

No-Code AI: Enables users with little to no coding knowledge to build AI models through simple drag-and-drop interfaces.  

Example: Tools like Lobe or Runway allow non-programmers to create AI models for tasks like image classification or object detection.

Open AI: The development and sharing of AI technologies and research openly with the public to encourage responsible and collaborative advancements in AI.  

Example: OpenAI, the organisation, has made some of its language models, such as GPT, available for public use through APIs (application programming interfaces).

Predictive Analytics: Uses statistical techniques and AI to analyse current and historical data to predict future outcomes.  

Example: Retailers use predictive analytics to forecast future sales based on previous trends and seasonal demand.

Prompt: The initial input or question given to the AI system to generate a response.  

Example: Typing “Write an essay on the history of bread” is a prompt given to an AI text generator.

Quantum Computing: An advanced computing technology that uses quantum bits (qubits) to process information faster than traditional computers. It’s expected to revolutionise complex problem-solving tasks in AI.  

Example: Quantum computers could (in the future) solve problems in drug discovery and develop new vaccines or climate modelling (a complete simulation of the Earth’s climate system) that are too complex for today’s supercomputers.

Sentiment Analysis Uses AI to determine the emotional tone behind a body of text and categorise it as positive, negative, or neutral.  

Example: Social media monitoring tools use sentiment analysis to gauge public opinion about a brand or product by analysing tweets or comments.

Speech to Text: Converts spoken language into written text.  

Example: Dictation software or transcription tools use speech-to-text technology to transcribe meetings or voice memos.

Training Data: The dataset used to “teach” AI or machine learning models how to perform a task by learning patterns within the data.  

Example: A model trained to recognise cats in photos would be fed thousands of labelled images of cats as training data.

PART TWO

AI Tools for Business

Many entrepreneurs and small businesses need help figuring out where to begin with AI. Many fear it might be too costly, complex, or irrelevant to their small-scale operations. The list of AI tools for businesses below has been curated to address those fears: Each has varying pricing models, and many offer free or affordable plans for startups, making AI accessible even on a tight budget.

1. Customer Service & Communication

AI is a natural fit for customer service. It can be trained and deployed quickly and can work 24/7/365 without needing paid leave or sick days.

1. Drift: A conversational AI that can be used for various applications. It can help sales reps be more productive, convert visitors into customers, and assist customers with a direct route to their desired answers. 
2. CXone SmartAssist by Amelia: A chatbot that interacts with customers via text or voice and is intuitively capable of context switching, reacting appropriately when interrupted, and understanding human sentiment. 

2. Content Creation

A robust content creation strategy is a must-have for any business looking for a high-ranking internet presence, increased brand visibility, and a way to attract customers and drive more sales. However, for a small company that has its hands full managing day-to-day tasks, creating content takes time and effort to measure or track the effectiveness. In addition, outsourcing can get expensive, especially for high-quality content that can be published or posted regularly and consistently. These AI-generated content tools for businesses that are short-staffed and have a limited budget:

• Content ideas, outlines, and captions: Tools such as ChatGPT and Lex help with researching ideas and providing suggestions, writing whole pieces of content for websites, social media, and email campaigns, providing SEO copy,  and generating images from text.
• Graphic Design: Canva boasts a simple interface and a vast library of tools for expert-level posters, presentations, and infographics. DALL-E2 is a popular choice for businesses looking for automatically generated, high-quality images generated from text. 
• Transcription and Editing: If you want to produce a podcast or start a YouTube channel, a tool like Descript makes the process easy.

3. Human Resources (HR)

When applied to HR, AI tools for business help automate tedious tasks, write job advertisements and descriptions, analyse CVs and simplify recruiting, automate onboarding and training processes, and unlock insights into the employee-employer relationship.

• Leena AI: Helps HR departments optimise and automate planning (e.g. answer questions about company policy and handle time-off requests) and can also use online surveys to improve the employee experience (e.g. identify risk of burnout). 
• Fetcher.ai: Automates tasks related to recruiting and can provide shortlists of suitable candidates, send emails to worthy candidates, visualise gender and demographic data across the candidate pool to ensure businesses adhere to diversity requirements (if applicable), and even determine candidates’ interest in the job opportunity based on who opened and clicked on emails. 

4. AI for Meetings

An Atlassian study of 5,000 workers across four continents found that, according to employees, meetings are the No.1 barrier to productivity. 4 in 5 workers reported that they’re expected to attend so many meetings that it’s hard to get work done, over half said they left meetings with no clear idea of what to do next and regularly need to work overtime to make up for time spent in meetings. AI tools for business meetings are designed to optimise efficiency and effectiveness. 

• Otter.ai: Records audio during meetings, takes notes, and creates meeting summaries upon request.
• Duet AI for Google Workspace: Helps to improve audio and visual quality for remote meeting participants, generates translated captions in 18 languages, takes notes and summarises meetings, and even attends meetings on your behalf.

5. AI-Powered CRMs

Customer Relationship Management (CRM) tools are good for tracking contacts and sales, but when integrated with AI, they can offer customers personalised services and help make data-driven decisions that drive growth and sales. 

• HubSpot CRM: Free tools that are easy to use and help track and analyse sales activity. Ideally suited to sales, marketing, and service professionals. The platform also helps write and send prospects emails based on previous interactions.
• Zoho-Zia: A generative AI tool for business that streamlines time-consuming tasks (e.g. data entry) and automatically assigns tasks to team members based on their skills, availability, and workload. 

6. Accounting and Finance

Software solutions that use AI to create invoices, analyse financial data, and collate employee expenses.

• ClickUp Accounting: Designed to simplify processes and has free tools to manage accounts and create shareable reports and spreadsheets. The cloud-based software platform also uses NLP to help with everything from financial management and project detailing to keeping up with accounts receivable and payable. 
• Vic.ai: An invoice processing tool with high accuracy rates and advanced ML algorithms that can automate almost every aspect of billing without the need for templates or custom rules. 

7. AI for Marketing Automation

The more an AI marketing tool for businesses can automate mediums such as email, SMS/text, digital ads, and social media, the better it is for productivity. In addition, if it’s easy to use and integrate with other AI tools such as CRM and content management and also offers free subscriptions and/or inexpensive upgrades, businesses can further reduce costs and save money. Those that made our list include:

• Brevo: An affordable all-in-one solution (includes a CRM and meeting scheduler) that offers a free plan and upgrades from $8/month. It automates email, SMS, and WhatsApp, and while options for businesses looking for scalability are limited, it’s the ideal starter platform for those on a tight budget.
• HubSpot Marketing Hub: An extensive and powerful set of tools that are easy to use and integrate seamlessly with other tools. However, at $800/month for starter plans, it’s an expensive option for most startups and small businesses.
• Omnisend: An E-commerce, email marketing and SMS platform that significantly helps to improve sales without increasing workload. Small businesses can enjoy extensive marketing features and CRM integration even on the free forever plan. 

The list above highlights that AI tools for business need not be costly, require a significant investment, or require a considerable learning curve. Even by implementing one tool integrated with additional functions, companies can benefit from platforms that significantly help to save time, improve efficiency, and reduce operational costs.

PART THREE

A Step-by-Step Guide to Identifying AI Opportunities

You may be wondering how to determine which processes in your business can benefit from AI. Here’s a step-by-step guide to help you identify where AI can bring the most value:

Step 1: Assess Your Current Operations

Take stock of your daily operations and identify bottlenecks or repetitive tasks that consume too much time. Common areas include customer support, marketing, data entry, and inventory management.

Step 2: Pinpoint Tasks That Can Be Automated

Once you’ve identified areas of inefficiency, consider which tasks are rule-based or data-driven. 

• Rule-based tasks: Ideal for basic automation because they don’t require AI to analyse or learn. It just executes a set of rules consistently, e.g., sending automatic reminders.
• Data-driven tasks: Best suited for AI automation as they benefit from AI’s ability to analyse patterns, make predictions, and adapt. e.g., AI-enhanced AI that can identify employees at risk of leaving based on patterns in performance reviews, engagement survey scores, and attendance data.

Step 3: Evaluate the ROI

Consider the cost of AI tools and the potential return on investment (ROI). Will automating a task save significant time and resources? Will it improve accuracy or customer satisfaction?

For example, a small, local bakery that focuses on quality, handmade baked goods, serves a local customer base, and employs fewer than 10 people wouldn’t be a good AI for business candidate. The bakery’s small scale means it can easily monitor inventory manually or use simple inventory software, has a steady customer base (who probably prefer in-person communication), and doesn’t need predictive analytics. The time and cost of implementing AI tools are unlikely to result in gains that couldn’t have been achieved otherwise.

Step 4: Start Small

Begin with a single AI tool or process, test it, and gradually scale up your use of AI. This will help you manage costs and risks while giving your team time to adjust.

For example, A yoga studio could start with an AI-powered scheduling assistant. This tool could automate class bookings, cancellations, and reminders for clients, reducing time spent on administrative tasks and making it easier for clients to manage their bookings. Once the team and clients are comfortable with this tool, the studio could gradually add more AI features, like personalised class recommendations based on client preferences or attendance history.

Step 5: Measure Success

Establish KPIs (key performance indicators) to measure AI’s impact on your business, such as time saved, cost reductions, or customer satisfaction improvements.

PART FOUR

Risks of Using AI Tools for Business

Once you’ve identified AI opportunities, it’s crucial to remember that while AI is a powerful tool, it’s not a perfect one. If implemented without carefully considering the potential risks, businesses could face brand damage and even legal or financial complications.

Risk #1: Accuracy and Accountability

Many AI applications make serious errors in sourcing, accuracy, and accountability. In many cases, it’s impossible to know where information comes from and how to verify it. AI systems are prone to errors when generating content due to limitations in understanding context, nuances, and cultural references. In addition, AI can be vulnerable to manipulation, as seen in the spread of fake news. 

Other accuracy risks include ‘hallucinations’. This is when an AI system makes up facts and confidently states incorrect information. For example, when ChatGPT made up fictional legal cases that lawyers used for an aviation injury claim – and were subsequently fined $5000 by a US judge for using fake citations.

Risk #2: Cyber Security and Privacy

A recent McKinsey study listed cyber security as the second most important risk but noted that only 38% of the companies surveyed mitigated this risk. Bad actors can exploit AI to launch cyberattacks, clone voices, and generate fake identities to scam, hack, or steal people’s identities or compromise business privacy and security. 

Risk #3: Bias

Humans are biased. The AI systems we develop can inadvertently learn them due to the training data it’s given and the deep learning models underpinning its development. Unintended consequences with harmful outcomes can include applicant tracking systems that discriminate against race or gender, healthcare diagnostic systems with lower accuracy for underserved populations, and policing tools that disproportionately target marginalised communities.

Risk #4: Intellectual Property and Legal Risks

The risks listed above highlight the interrelated risks with AI, especially when identifying the origins of an AI error – or taking credit for its creativity. Who is held accountable when AI makes a mistake? Who ‘owns’ the intellectual property (IP) to content (e.g. images, videos, short stories) that AI generates? How is copyright infringement by AI regulated? What recourse do artists and creatives have when AI is trained using their original works and styles without licence or permission?

Risk #5: Environmental Harm

AI relies on energy-intensive computations with a significant carbon footprint. A report by the World Economic Forum on AI and Energy noted that generative AI systems already use around 33 times more energy to complete a task than task-specific software does, and the computational power needed to sustain AI’s growth doubles roughly every 100 days. In addition, simply training a single NLP model requires over 600,000 pounds of C02 (nearly 5 times the average emissions of a car over its lifetime), and the water consumption needed for cooling (AI’s data-centres generate considerable heat) is more than ‘considerable’. 

Risk #6: Job Loss

If it hasn’t already, AI will disrupt the job market and displace workers – especially those working in fields (e.g. invoicing and billing, data entry, customer service) that can be automated. However, whether this will result in massive unemployment or a growth opportunity in roles such as machine learning, robotics engineering, and digital transformation is a question of perspective. 

Another widely debated risk of AI is the ‘existential threat’ to humanity and the dangers of creating an intelligence that vastly surpasses ours. However, the abovementioned risks highlight that humans are far from becoming obsolete. For example, the risk of misinformation and inaccurate results will require humans to do the legwork and fact-check. The risk of being sued by an artist for copyright infringement means businesses are likelier to engage with artists directly – softening the fear many artists have of being replaced by AI. 

While it’s clear that AI holds much promise, it also comes with potential pitfalls, and businesses must make AI Governance a priority. 

PART FIVE

AI Best Practice

Understanding AI’s risks and taking proactive steps to minimise them is crucial to gaining a competitive edge in the world of AI. By developing and investing in these AI Best Practice Guidelines when implementing tools and operations, you’ll get off to a good start.

IBM® watsonx.governance™ is a single platform to speed responsible, transparent, and explainable AI and an ideal starting point for any business looking to implement AI Best Practices. Its strength lies in its ability to evaluate AI models from any vendor, assess their accuracy, and monitor fairness, bias, and other metrics. By seamlessly integrating with existing systems, it helps further automate workflows to save time, reduce costs, and comply with regulations.

Implement an AI Governance Strategy

Create practices that promote fairness (e.g. use AI systems trained on representative data sets), form diverse development teams, and incorporate human oversight through an AI Ethics review board or committee.

Outline an AI Safety and Security Strategy

This can include searching for security gaps in AI environments by undertaking regular risk assessments and threat modelling. In addition, investing in cyber response training to level up awareness and preparedness will boost security further.

For Example: Run a pilot project before rolling out AI across your entire business. This will help you identify potential issues early on and adjust as needed.

AI Policy for Employees

It’s increasingly tempting for employees to use generative AI capabilities for research projects, generate ideas, summarise lengthy articles, or create content. Employees not trained and given clear guidelines on appropriate use can spread incorrect information, violate data protection laws, make discriminatory decisions, publicly share company information, and infringe on third-party copyrights. 

Some companies, such as Apple, outright ban ChatGPT at work, while others, like Amazon, restrict the use of AI for coding. 

AI Sustainability Policy

Using AI poses a severe dilemma for companies actively engaged in becoming carbon-neutral and operating sustainably. Consider data centres and AI providers powered by renewable energy and smart grids, use energy-efficient training models (e.g., methods such as pruning and knowledge distillation), and define clear, measurable sustainability goals that align with your company’s overall environmental strategy.

Reskill and Upskill Employees

Invest in technology that enables your employees to focus on higher-value tasks that drive revenue growth. By seeing AI as a tool that augments human capabilities instead of replacing them, you can establish human-machine partnerships that enhance decision-making, problem-solving, and value creation.

PART SIX

The Future of AI for Small Businesses

AI is rapidly evolving, and small businesses have more opportunities than ever to harness its power. Those who successfully integrate AI into their operations will be better positioned to innovate, grow, and succeed. 

As AI evolves, compliance and regulation will too. While many governments, including the UK, have established frameworks for regulating AI, none have been codified into law – even though they accept that regulatory gaps exist and will need to be addressed. However, these frameworks are underpinned by core principles (i.e., safety, security and robustness, appropriate transparency and explainability, fairness, accountability and governance, contestability and redress). Businesses that implement these best practice frameworks will be even better positioned to comply with mandatory regulations if/when they come into force.

PART SEVEN

Conclusion: Taking the Next Steps Toward AI Mastery

AI is no longer just for large corporations with deep pockets. Startups and small businesses can leverage AI tools to improve efficiency, reduce costs, and grow faster. Start small, explore affordable AI solutions, and expand as you become more comfortable with the technology.

Your journey to mastering AI begins with understanding its potential and how to implement it effectively – while accepting and mitigating the present risks. By following this guide, you’ll be well on your way to using AI to drive efficiency and innovation in your business.

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