AI for Decision-Making: Make Better Decisions and Transform Business Strategy 

Risking sounding like a broken record, we won’t tire of stressing, again and again, that AI’s impact on analytics can change the game for any business. The models can handle the whole process, simplifying or automating everything from data collection to preparation, implementation, extracting insights, breaking them down, and incorporating them to improve KPIs. The market data is staggering, too. FedEx uses AI to cut logistics costs by 15% and improve delivery times by 20%. Amazon’s AI recommendations drive 35% of its sales. That’s billions generated annually. Countless examples, across every industry. Goldman Sachs estimates that global businesses will invest nearly $1 trillion in AI infrastructure in the next few years. 

We’ll discuss decision intelligence and AI’s broader role in it. We’ll explain how AI-driven analytics differ from traditional tools and why it’s crucial to have seasoned expertise on your side to empower LoB users and businesses with AI-supported capabilities. Strap in. 

AI in Decision Making: Key Principles 

First off, when we say AI, we’re being general. There’s a range of models – supervised, unsupervised, reinforced, Bayesian, and more – that can be applied to different analytical contexts based on the setting’s dynamics, data dimensions, and other factors. The key is picking the right algorithm for the exact use case, where the company has – or can acquire – the necessary data.  

Secondly, it’s also important to distinguish between terms often thrown out interchangeably but actually meaning different things: decision intelligence (DI) and AI-enabled support.  

DI is about using technology – mainly AI, but also other tools and techniques – to improve decision-making at all levels. It focuses on a holistic approach, where modeling, design, automation, and continuous monitoring are all part of the decision process. It also incorporates feedback loops for ongoing refinement.  

Take pricing optimization as an example. A company leveraging DI would likely use a whole stack of tools to predict demand (time-series analysis), assess supply chain constraints (optimization, simulation, and inventory platforms), analyze competitors’ pricing dynamics (agent-based and competitive analysis platforms), and measure the impact of marketing campaigns (A/B testing and attribution solutions). All of these systems would generate outputs that, pieced together, would serve as a basis for an AI recommendation engine suggesting optimal pricing strategies. And the accuracy of the latter will also be tracked to refine predictions over time.  

That’s DI in a nutshell. As you can see, to truly benefit from it, companies need expertise not only in AI/ML, but also in engineering, data visualization, statistics, and even decision theory. Or they can just partner with a vendor that does. 

AI-enabled support, on the other hand, is a part of the broader DI framework. It refers to enhancing human capabilities by providing AI insights and action suggestions within specific workflows. For example, an AI trained on medical data might help a healthcare professional by flagging potential abnormalities. The scope is limited, but the effect on how fast the professional works could be immense. 

AI-powered analytics is another key element of DI. It is distinct from traditional analytics in that it uses ML and neural networks to extract meaning from data. And that, due to AI’s inherent properties, leads to differences in scope, mechanics, assumptions, adaptability, and application. Here’s what we mean: 

Traditional analytics relies on established statistical methods – like linear regression – and operates on structured data, such as spreadsheets and databases. Rules and hypotheses are defined upfront and tested against historical data. It always assumes that past patterns predict the future and relies on human input to frame problems.  

It’s rigid. When new variables appear – like a market shift or unexpected customer behavior – the models must be updated manually. This limits traditional analytics to variables and timeframes that were manually encoded, unless specific forecasting tools are added. But even then, it operates on the assumption of static conditions – steady demand, consistent seasonality, etc. 

AI analytics, by contrast, bypass predefined rules. The models can learn patterns from datasets on their own, which allows them to handle messier, less-predictable problems (like customer churn or client feedback sentiment). They can also process unstructured data directly – CNNs classify images, and NLP models parse social media comments or tweets.  

These algorithms can shift from historical to real-time data when needed, providing prescriptive and predictive insights. This makes the AI analytics tool vastly better suited for volatile environments. Of course, this depends on whether the training data is relevant to possible future states. 

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The Impact of AI on Business Decision-Making 

Now that we’ve outlined the distinctions, let’s focus on how AI-empowered decision-making adds value. It would take a book to cover every benefit, so for brevity, we’ll focus on four key areas. 

Accelerating Decisions 

Models can process millions of data points – sales reports, client interactions, market signals, feedback – within seconds, comprehensively. A human analyst would spend hours combing through spreadsheets to find a single trend. And humans get tired. When faced with large amounts of data – reports, metrics, emails – it becomes extremely hard for them to separate actionable insights from noise. AI fights this information overload by filtering and prioritizing data, turning raw inputs into valuable insights. 

Testing the What-Ifs 

Models simulate possibilities, letting organizations experiment without risk. Tools like Monte Carlo or generative adversarial networks can, for example, measure the impact of a supply chain disruption or a new marketing campaign with various inputs – cost, timing, consumer response, and more. A financial institution might use AI to assess portfolio risks under different economic conditions, detecting vulnerabilities and blind spots. They can run thousands of scenarios without real stakes, quantify uncertainty, and mitigate vulnerabilities. The key is ensuring the training data isn’t too optimistic and that no important variables are left out. Done right, this lets companies plan proactively, with more nuance and precision than traditional tools could ever allow. 

Increased Productivity 

AI handles routine tasks, like flagging anomalies and parsing logs, freeing up human bandwidth and boosting operational responsiveness. Imagine running a warehouse. AI tools optimize picking routes and restocking schedules in real time, adapting to order spikes, while manual planning with static rules falls short. AI also allows for quick pivots – rerouting deliveries when a vital highway is blocked, for example. It’s easier to adapt when you aren’t relying on rigid processes. 

Furthermore, the models prevent inconsistencies and delays caused by siloed teams and manual work and reduce burnout. When analytical experts make the same choices over and over, focus slips. AI takes over the routine decisions, giving staff more mental energy for high-stakes, complex issues. 

Democratization 

As we’ve covered in detail in our article on GenBI, AI models – especially generative ones – bring the analytical power to the Line-of-Business. With these tools, a clerk can forecast daily stock needs without knowing regression equations. All that’s required is to specify the insights and visualizations they want in a natural-language prompt. Afterward, AI can be prompted to explain how it reached a conclusion, and it will guide the LoB user through the entire process in understandable terms. This eliminates the “black-box” problem of traditional methods. 

The Main Elements of Decision Intelligence 

DI encompasses layered processes with many moving parts. Algorithms, data streams from DWHs and other sources, analytical techniques, and automation – all must align for a company to get valuable insights and, subsequently, high-impact outcomes. Let’s break down the main pieces: 

• AI Models 
  Everything from decision trees to transformers. AI models are the engine of decision intelligence and companies typically use many at once, for different data and situations. Through their ability to learn and adapt, they are the core computational components that process data and generate insights. 

• Data Inputs 
  As we’ve discussed, ML models can handle any data – from spreadsheets to unstructured sensor readings or customer emails. A retailer, for instance, can feed web clicks, transaction logs, and weather data into AI to predict product demand. Data inputs are the raw material that fuels the AI models, providing the necessary context and information for analysis. But this only works when data is plentiful and the architecture is selected properly. 

• Feedback Loops 
  Sticking with the retailer example, after predictions are made, the model loops back real-world sales data, adjusting its parameters to improve the next round of predictions. Feedback loops are what power continuous improvement. It allows the AI to fix its mistakes and learn from them. 

• Data Warehouses 
  These are the central stores from which models draw data. They hold terabytes of historical and real-time data, keeping models fresh and well-rounded. Without an up-to-date dataset, the model, even a highly advanced one, will become useless over time. That’s why DWS and data lakes, are often crucial for AI-powered analytics. This is often glossed over, but setting up, integrating, and migrating data to a sophisticated data storage system, like Symphony did for GOAT, is the central element of any data management or data science initiative. Without it, organizations will struggle with basic counting, let alone predictive analysis. 

• Cloud Computing 
  Cloud platforms provide the infrastructure and resources to run, update, and scale AI analytics. 

• Workflow Automation and Orchestration Tools 
  These solutions help embed automation into analytics processes – from data ingestion to model deployment to incorporating insights. When the workflows leading up to insights generation are uniform, streamlined, and optimized, there’s consistency and efficiency in the decision-making process. 

• A/B Testing 
  Model outputs should be tested in a live environment. A/B testing tools help companies identify the best-performing algorithms for each case, making them essential to the DI projects as well. 

The specific types of tools that will be selected for DI will vary for each organization, based on industry regulations, requirements, current level of data literacy, budget, and more. 

In terms of popular approaches, though, in predictive analytics – statistical and ML methods are the go-tos. These methods find trends in data and project them forward. They’re typically used for tasks such as risk estimation (think patient readmissions based on medical history and vitals). 

Prescriptive analytics will typically go a step further and throw in optimization, simulation, and decision modeling algorithms. These not only find trends but also determine the best actions to take. 

We should also give a special mention to reinforcement learning. This is a technique of teaching the algorithm through trial and error, i.e., giving rewards for getting closer to the goal and penalizing it when it slips. Picture a tool that learns to balance energy use in a smart grid and a person, or another algorithm, guiding it with a carrot and a stick. 

RL is typically deployed in dynamic analytics applications. While predictive and prescriptive analytics mine existing data to forecast and recommend, RL is about the AI getting better through continuous interaction with the issue and receiving feedback. It’s an ideal ML technique for situations needing real-time adaptation and optimization. 

Use Cases and Common Applications 

We’re seeing AI analytics being used in both broad and very industry-specific, niche applications, with equal success. The range of uses is truly large, and it will keep expanding as the models grow more powerful and accessible. However, here are a few common examples for reference. 

General  

Fraud Detection 

The PayPals and Stripes of this world, as well as large banks, have long relied on AI to spot and flag suspicious transactions. Their anomaly detection models, exceptionally advanced at this point, can sniff out anything unusual within milliseconds. And then, they also cross-reference the questionable transactions with user norms and fraud patterns in real-time. 

Content Recommendation 

Even non-tech-savvy people are well aware that the recommendation engines on Netflix, Spotify, TikTok, and others are completely powered by AI. These models use collaborative filtering, and their neural networks determine exactly what features each user prefers, so they can provide compelling suggestions for what users should check out next. Currently, entertainment use cases are the most talked-about, but companies actively apply these algorithms to enhance upselling, support, and other functions and services across industries. 

Industry-Specific  

Healthcare 

At the world-renowned Mayo Clinic, AI assists medical professionals with both diagnostic and treatment tasks. Namely, the tools analyze patient records, symptoms, and imaging data, also taking into account genetics and the latest research data to help doctors prioritize options. 

Manufacturing 

If you look at any forward-looking big-name manufacturer, they use AI-powered analytics in some form. General Electric, for instance, uses the models to run through machinery sensor data, historical data, and live metrics and help predict failures. This enables GE to schedule repairs proactively, which, in turn, leads to substantially reduced downtime. 

Retail 

As is the case with Netflix, Amazon’s inventive and slick use of AI has amazed people for years now. In every corner of the globe, marketplaces are trying to emulate their dynamic pricing. The company uses predictive algorithms to adjust prices on millions of items daily. While we can’t know all the parameters, the models certainly factor in competitor prices, inventory levels, and demand elasticity. According to reports, AI suggestions drive about 35% of Amazon’s sales, which amounts to billions of dollars a year. 

Transportation 

In the transportation world, notable uses include the AI implementation of UPS. It has a proprietary AI system – On-road Integrated Optimization and Navigation (ORION) – which mines data from customers, vehicles, drivers, as well as traffic, package priorities, and fuel costs to optimize each trip, shaving off miles and saving the company money. In UPS’s case, AI-enabled predictive analytics results in millions saved annually.  

Energy 

Grid management is exactly the type of dynamic setting where AI algorithms thrive. National Grid ESO uses AI to balance electricity loads. Specifically, the company applies an ML model to effectively forecast solar power generation, which is notoriously hard to predict. 

Ins and Outs of AI Implementation

Incorporating AI into processes isn’t easy. In fact, that’s where most companies fail. The integration requires both a phased, thought-out approach and proficiency in specialized tools. While different companies may approach it in their unique ways, we, at Symphony, have refined a meticulous procedure and rules that we adhere to. We’ve built it over years of delivering AI solutions. Here’s a condensed description of what’s involved. 

Tools

Custom platforms. Tailored solutions, which we craft using languages like Python and frameworks like LangChain or PySpark, could offer analytical precision even for complex, industry-specific tasks. The end system could be designed to help retailers predict demand, or a tailored recommendation platform built specifically for iGaming, like our Opti X. Building the tool from scratch could be a winning strategic approach, but it requires expertise and data.

No-code/low-code tools for decision modeling. These systems, as the name suggests, enable the average person to create and design decision support systems without any coding or analytical skills, just by using drag and drop. They might be a viable option when a small marketing team, for example, is looking for a basic solution to get a general idea of potential churn rates. But their accuracy will not be high. The accessibility comes at the cost of the absence of depth.

Continuous learning systems. There is a range of solutions like Google Cloud AutoML or Azure Machine Learning, which can adapt and adjust as new data comes in. These tools improve their outputs over time. In dynamic environments (customer support, pricing, grid management), they could be of extreme value.

Picking or building the tools is just the beginning, though. To help with analytics, they must also be properly integrated with a company’s ERPs, CRMs, and BPMs. This means AI must be able to access data from profiles, campaigns, inventory, etc., freely to then also send insights back. To achieve this, APIs or other integration methods can be leveraged, depending on the exact requirements.

Our Process

We kick things off with discovery. This is where consultations and thorough analysis happen to determine the explicit pain points and scope the project. We will determine the issue (accelerating approvals, reducing stock, etc.) and map the available data (sales logs, feedback, etc.) to define the next steps. Gaps, silos or incomplete data, if found, will be addressed too.

Next is the model building. We will first craft a prototype solution or fine-tune a third-party model (e.g., OpenAI) on your data. For a custom platform, we may train a neural network; for a no-code solution, we can cleanse and augment data and set up a pre-built template. Then, we’ll test it with historical data to ensure the model outputs predictions with the needed degree of accuracy.

Then, it’s time to deploy. We will roll out the AI solution and test it in production end-to-end. This is the step where AI gets embedded into your workflows, and we monitor its performance. We may incorporate automation logic here, too.

Iteration. At the next – final stage – we will compare the predictions vs. actual results and loop the real data back into the model so it refines its weights and biases for subsequent inferences. Continuous learning systems can pretty much do this automatically, but classical models will require manual updates.

Scalability and Flexibility

Unlike static and rigid tools, AI decision platforms are adjustable; they can grow and change as your business needs evolve.

• Scalability. A scalable AI-decision system can be initially used by and then gradually become an enterprise-wide system. An AI utilized at a single warehouse, for instance, can gradually expand to dozens of facilities – handling increasingly more data and users – and then migrate to other functions.

• Flexibility. This implies that models can be adaptable by design, i.e., you can modify them to serve new needs without a substantial rebuild. Continuous learning systems can do it themselves as the market changes and new data comes in, while rigid tools may need some recoding/retraining.

Challenges

Finally, after we’ve discussed all the perks, let’s talk about challenges.

Data quality. With AI, the main principle is garbage in garbage out. If your data is spotty, biased, unstructured, spanning multiple systems and formats, the model performance will be subpar at best. Or it will just spit out nonsense. This is why at Symphony Solutions we not only help clients with building models, but also augment and cleanse their datasets as well as set up collection infrastructure to avoid similar issues in the future.

Expertise. You need experienced data science and AI development experts. No way around this. Even the no-code tools, whose features are already limited, won’t be of use without basic data literacy. So, if you’re looking to partner with a vendor, choose one that offers end-to-end assistance throughout the ML model-building and analytics embedding lifecycle.

Integration. This is even more delicate. You want to be able to plug in your new model into existing solutions, which likely handle sensitive data, without interrupting or compromising any processes. Issues like mismatched formats and slow APIs should all be handled without risks, and that, again, calls for significant expertise.

Cost overruns. If your in-house team isn’t experienced enough to accurately evaluate the scope, or you’re collaborating with inexperienced developers, miscalculations and incomplete prognoses are bound to happen. And they often result in ballooning costs, especially if the pilot or prototype stage doesn’t go as intended.

Overcomplexity. Lastly, the tendency of some companies to chase after the latest, untested, and only theoretically beneficial architectures typically ends with them throwing huge amounts of money into an overengineered mess of a system that doesn’t bring any tangible value.

A forecasting tool or an analytics engine doesn’t necessarily require a thousand-layered neural network architecture. It takes experience to know which type of model will fit the specific use case.

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Summing Up 

AI models, if implemented and integrated properly, can automate a large chunk of work pertaining to discovering what’s meaningful in data. They can mine structured and unstructured data streams – public, proprietary, third-party, etc. – and turn them into comprehensible visualizations or simple and actionable responses. Thus, they can empower line-of-business users and elevate companies’ analytical capabilities overall. 

The success of such projects depends on the experience of the team, however. There’s quite a bit of nuance in how to prepare the data, build or customize the architecture, train and test the model, integrate it with the existing stack, and refine it, so its inputs get better over time and change as the business evolves. Symphony Solutions’ AI and analytics services encompass all of these phases. 

If you’d like to get more sophisticated, AI-enabled analytical capabilities for your organization, contact us right now for a free consultation.