It’ll pay off to get serious about utilizing AI to boost your business. AI has the potential to reshape how you work – from improving customer service to streamlining operations. If anything, we’d call you behind the times if you’re not utilizing AI for your business. 

According to statistics, over 50% of companies with over 5,000 employees use AI, 60% of companies with over 10,000 employees use AI, and 42% of companies with only 1,000 employees use AI. Another study found that 56% use AI to improve and perfect businesses. And we’d say this is only the beginning of true AI adoption.

Read on to learn how to get serious about utilizing AI to boost your business.

Invest in The Right Tools and Technologies

The right tools and technologies are essential. Now that AI adoption is so massive, you’ll find endless solutions and tools available. Are they all viable for business use, or should we say good enough businesses? Absolutely not.

You’ll find solutions ranging from natural language processing tools through machine learning platforms to cognitive computing systems.

Small enterprises may opt for easy-to-use AI software packages designed for non-technical users. Trust us, AI technology can become complex. They could consider platforms like Google’s AutoML or IBM Watson, which offer powerful features but require minimal expertise to operate effectively. We’d say your average Joe is capable of using those.

If you have more complex requirements, it might be necessary to engage specialists who can provide tailor-made solutions and ongoing support during the implementation phase. Or look for tools like H20.ai, SAS Viya, and DataRobot. They’re more complex systems with incredible capabilities.

Data Quality is Essential

AI systems are highly dependent on data for accurate functioning.

Good-quality data should always be used when working with any form or level of artificial intelligence – it doesn’t matter how simple or complex the system is, it still won’t give you the correct output if you aren’t giving it the best data and inputs. Wrong predictions and insights are common – don’t expect AI to be perfect.

Ensure the cleanliness, accuracy, and proper organization of your data sets. Implement robust data governance measures to preserve high levels of data quality, including regular data audits and cleaning up processes.

We’d also recommend you invest in data management frameworks that can facilitate easier sorting and analysis capabilities over large volumes of information.

Train Your LLM Properly

You won’t get the most out of your LLM if you don’t train it properly. We’d go as far as to say there’s almost no point in using an LLM if you aren’t training it. Here’s how to train your own language model:

• Gather and Prepare Data: Collect all the relevant data containing useful information that could solve queries faster. Think internal server data, website data, performance metrics, KPIs, etc. Clean the data for duplicates, fix structural errors, use clear formatting, etc.
• Tokenization: Utilize tools such as Tiktoken from OpenAI or Python’s NLTK to transform cleaned data into tokens. Remove stop words, apply stemming, and lemmatize so the token dictionary is accurate.
• Model Architecture: Select what model(s) – encoder, decoder, or both – would be most appropriate for your purposes. Configure this model accordingly. Don’t forget to include an embedding layer to tokens into numerical representations.
• Vector Database: High-dimensional numerical representations of tokens should be stored in a vector database. It’ll ensure that information is ready for rapid retrieval and with accuracy during model inference.
• Implement Guardrails: Bias and hallucinations in model responses need to be prevented. Guardrails can be used for enforcing AI policies and guidelines so that output remains ethical and correct.

Measure and Optimize

It’s essential to measure how well your AI applications perform and manage improvements. Establish key performance indicators (KPIs) relevant to meeting your goals as an organization and track these metrics regularly.

Refine your AI strategies based on insights found during the measurement phase. One example would be monitoring conversion rates, customer engagement, or ROI (if using AI within marketing). If results fall short of expectations, try tweaking algorithms, changing data inputs, or experimenting with different, more efficient AI tools, etc.

Is it time to get serious about using AI to boost your business? There’s so much you can do with it – and this is only the beginning. AI will soon completely revolutionize business functions and growth.