TrueState is a technology company focused on empowering organisations to solve their most important problems with machine learning and artificial intelligence.
Our developer platform provides teams with access to cutting-edge algorithms, high-leverage APIs and an easy-to-use interface, while our implementation solutions help organisations take their first steps with AI with confidence and peace of mind.
In the ever-evolving landscape of AI, businesses often struggle to move beyond toy demos to tangible results. In this post, I’ll cover some recipes I’ve used to create hundreds of millions of dollars in value for clients at McKinsey & Company’s AI division QuantumBlack, in Private Equity operations, and more recently for customers at TrueState.
Creating value with AI first requires the right frame of mind, so let’s step away from specific AI systems, like ChatGPT, and define intelligence as the competence of a system (either biological or artificial) over a broad range of task types (e.g., creative writing, forecasting, categorisation, optimal decision making). A system is generally considered more intelligent if it can competently perform a wide range of tasks.
While there is a broad range of tasks in business, any one individual algorithm is unlikely to have state-of-the-art performance across all of these tasks, especially those tasks typically aligned closest to business impact.
This is because different AI systems have different strengths and weaknesses across different domains. This is clear from model benchmarks, where you can see certain conversational systems performing better in math, problem-solving, or writing tasks.
Stepping away from conversational/multimodal models, there are also a swathe of models far more capable of event prediction, time series forecasting, record classification, search, and more. These alternative systems include tree-based classification and regression models, deep recurrent neural nets, as well as embedding, classification, and scoring models for natural language and many more.
By deploying a range of AI algorithms, each suited to their specific use-case, executives give themselves the best chance at creating real impact. The disillusionment many are currently experiencing with AI is due to the opposite case: deploying the wrong type of AI to solve low-impact problems, leading to failure and lacklustre results.
To effectively harness AI, it’s crucial to ask the right questions. For executives grappling with how to navigate this landscape, I encourage you to reframe your thinking with the following questions:
What are the decisions in your organisation that impact the bottom line the most?
What would be the most helpful information for you or others to make those decisions better?
Given your data, what is the best algorithm/combination of algorithms to use to achieve state-of-the-art results?
By getting specific on these questions, you can set yourself up to select the right type of AI for your use-case and begin creating real impact with AI.
Let’s explore these questions.
Question 1 – What are your high-impact decisions?
Here, you need to have a clear view of your value chain and what decisions are critical. Where do you make money? Where do you lose money? If you could have a crystal ball, where would you use it in your organisation?
Decisions that are made at high frequency and are directly tied to earnings are a great fit for AI. Here are a few examples and how you might think of the impact AI could have:
Sales engagement: If I could predict the best way to engage with my customers in a personalised way, I could improve conversions and net profit.
Retention: If I could predict which customers were likely to churn and what I could do to stop them churning, I could improve retention and net profit.
Supply chain optimisation: If I could predict demand for my products and understand the impact of my operations team’s actions on available stock, I could prevent excessive capital outlay and improve cash flow.
Pricing: If I could set the right price for all of my products, I could sustainably maximise profit over the long term.
If you’re stuck, look at your income statement and dive deep. Look for areas of high cost, sources of revenue, and consider the key processes surrounding those items.
Question 2 – What kind of insights do you need?
If you could wave a magic wand, what information would your dream AI system provide you with? What would the format of that information be? For example, would you just want to predict key events, or would you also want actionable recommendations to achieve the desired result?
For best results, consider conducting workshops with key stakeholders to brainstorm and prioritise the types of insights that would be most helpful.
Answering this question correctly is critical in identifying the right AI systems to build.
Question 3 – Given your data and the type of output you want, what type of AI should you use?
You’ve identified several high-impact areas and the type of outputs you want. Now it’s time to see if some form of AI can help. Assuming you’ve got at least 6-12 months of operational data related to those areas, you’ll likely have enough data to train your own AI solutions that can beat human-level performance, if not get close to state-of-the-art.
Here are a few algorithm combinations associated with some different formats of decision support:
Predict key events before they happen (e.g., which customers am I likely to convert this month? Will I run out of stock for a given product next week? Which of my machines are most likely to require maintenance this week?):
For numerical data, tabular classification algorithms such as decision trees are a solid choice. For text-heavy data, natural language classifiers are a good option. Ensure these algorithms are configured to predict the occurrence of the event in question in a short-term timeframe. For example, train a model to predict if each customer in your pipeline will convert in the next 30 days based on historical conversion behaviour.
Recommend the next best action to maximise performance (e.g., how can I prevent high-risk customers from churning? What price should I set for my products to maximise volume?):
Here, great results come first by building an event prediction algorithm (as above) that understands the impact of your organisation’s actions on the key event. For example, did calling customers improve their chance of conversion? Did certain conversation topics work well?
Once you have that model trained, you can synthetically generate a large number of future scenarios where you might, for example, engage your customers in different ways. Apply your prediction model to identify the likely outcome of your actions in each case and run an optimisation algorithm to choose the best actions.
There are more complex approaches, like reinforcement learning; however, it’s rare to find a business problem constrained enough and with enough data for RL to work well.
Needle-in-a-haystack style search (e.g., picking the best founders/companies to invest in, identifying the best candidates for a new role):
Here you’ve got two solid options.
First, if you know what good looks like and you have detailed data about the entities you’re searching over (e.g., detailed professional profiles), your best bet is a class of models known as embeddings models. These take natural language (e.g., your ideal founder profile and your database of founder profiles) and convert it into a format where you can compare different records numerically to get similarity scores. Combine this with some natural language classification to give you filters for each entity to further refine your results.
If your records are too similar to one another and the results from the first option aren’t meeting expectations, you can train a natural language scoring algorithm to assign quality scores to each record based on expert-generated preferences (i.e., a hiring manager first ranks a small set of candidates which your model learns from). This takes more time given the requirement for data labelling but can produce good results.
We’ll explore more use-cases in future posts, but for now, I hope this demonstrates that there are different solutions for different task types.
Key takeaways
In this post, we’ve explored a three-step approach to delivering impact with AI.
Step 1: Identify high-impact opportunities directly tied to bottom-line value.
Step 2: Be clear about the decision support you need. Brainstorm with your team and identify the kind of insights that will be helpful.
Step 3: Pick the right tools for the job. We’ve explored a small number of the potential options, however if you need support on this, feel free to reach out to us at info@truestate.io.
Remember, the key to creating real impact with AI lies in targeting high-value decisions with the right algorithms. Stay focused, stay specific, and the value will follow.