Experience Decision Intelligence with AI and ML
Business intelligence cuts across all departments in an organization. The art of analytics available for a decision facilitator is now topped with the charm of decision intelligence. It is a latest trend in Business intelligence and is becoming an extended arm for decision-makers in formulating critical decisions.
On the flipside, organizations that make faster decisions are twice as likely to make high-quality decisions, as speed and the quality of decisions are interrelated.
Businesses should equip themselves with better decision-making models by incorporating intelligence to ease this crucial task and improve the quality of decisions.
- The speed and quality of decisions are closely associated with the organization’s performance. Furthermore, it quotes that organizations that make faster decisions are twice as likely to make high-quality decisions, as speed and the quality of decisions are interrelated.
Organizations that make faster decisions are twice as likely to make high-quality decisions, as speed and the quality of decisions are interrelated.
Unlock continuous, unlimited value for your decisions across your business with decision intelligence platform. It enhances your existing decision-making processes by supporting, augmenting, and automating business decisions.
Gartner predicts by 2023, more than 33% of organizations will have analysts practicing decision intelligence, including decision modeling.
What is Decision intelligence?
Decision Intelligence is the current and critical trend in data and analytics because of its excellent decision-making capabilities. It blends key technologies of data science, business intelligence, decision management, and decision modeling resulting in a decision delivering business outcome. It combines various decision-making techniques to design, model, monitor, align, execute, tune, and automate decision models and processes. It improves an organization’s business outcome by modeling decisions through a framework of integrating data, analytics, and AI.
It ties data, actions, and outcomes for a streamlined decision-making process leading to better decisions. Decision intelligence has 3 critical levels: supporting, augmenting, and automating business decisions.
The AI infusion in decision intelligence platforms supercharges your business vision with intelligent strategies and effectively predicts the business outcome. Machine Learning (ML), a key component of AI and data science, facilitates the forecasting and the predictive ability of decision intelligence.
In contrast, AI-powered intelligent applications capture and harvest data; delivering insights to tailor a personalized user experience. It also provides more context around business decisions, sievemassive amount of data for insights, and recommends effective decisions across your organization.
Why do businesses need decision intelligence?
An organization’s success depends on the effectiveness of the decisions it makes. Decision intelligence predicts the outcome of the decisions and prevents organizations from making wrong decisions.
Ever wondered what is the cost of bad decision?
- Research shows a 95% correlation between decision effectiveness and financial performance.
- McKinsey reports that, on average, organizations waste $250 million annually due to poor decisions.
Businesses need a centralized space for data to understand the context and history of data, detect potential setbacks, and get recommendations in real-time to ensure faster decisions and the highest success rate. This integrated platform includes data preparation with multi-source access capabilities, business analytics, and data science functionality.
In the traditional decision-making process, we collect data, visualize it, extract insights, and present it on a dashboard or a report. Based on the insights, the stakeholders make business decisions. This process is linear and is prone to human errors and poor decisions. In contrast, Decision intelligence will help you transform the traditional decision-making processes with advanced technologies like AI, ML, intelligent apps, and Natural Language Processing (NLP), transforming dashboards and reports into a comprehensive decision support platform.
Now, you can ask questions like how this decision will affect you next year? With advanced AI like NLQ (natural language query), BI tools can suggest the data that aligns with your question. You can get insights into all possible what-ifs, how, and why of your decision. AI can find connections and patterns in a massive pool of data. It uses this data and helps you apply human factors like creativity, experience, intuitive intelligence, and successfully navigate through nuances.
AI, ML, and Automation
For the unversed, the conversational experience powered by AI put forward a convenient option to gather data and feed them into data platforms. With AI and ML infusion in data platforms, generating insights has become the last step of achievement by these systems. Post that, it’s all manually driven by the facilitator and the key decision maker.
Measuring the effectiveness of a decision is far from reality as there lies a gap between the insights and execution.
On the automation side, Business Process Applications like robotic process automation, process discovery, and process mining focus more on the task execution and less on insights leading to decision.
Decision intelligence brings together the best of these technologies linking data with decisions, actions, and outcomes. On a single platform, organizations can
- Extract insights from the data
- Generate decisions
- Execute decisions
- Support the feedback process
It provides users recommendations to increase the user engagement, learns from the feedback of previous decisions, compares its predictions to the actual predictions and improves the effectiveness of decisions.
3A’s of Decision Intelligence
Decision Assistance – It provides support to human decision-making in the form of alerts, analytics, and data exploration. Humans entirely make the decisions here.
Decision Augmentation – Here, it plays a significant and more proactive role. It analyzes the data and generates recommendations and suggestions for human decision-makers to review and validate.
For example- it provides suggestions for you to buy or restock the products from Vendor A before a specific time and shows you how much money you can save. The decision-maker can go ahead and initiate the action or work closely with the machine to amend the suggestion.
Decision Automation – At this level, it further reduces human intervention by autonomously performing both the decision and execution steps.
- Decision step – they make autonomous decisions using a combination of tools such as rules, optimizations, and AI-based suggestions.
- Execution step – In this step, they implement the decisions without human involvement. However, humans have a high-level overview of the entire process. So, they can monitor the risks, find any unusual activity, and regularly review the outcome to improve the overall performance.
As an organization, by now, you must have had all the arms and ammunition to generate business insights. But with the introduction of Decision intelligence, organizations can now make faster, calculated, and less error-prone decisions. Critical business decisions with improved data-driven support, AI augmentation to scale, and accelerated decisions with automation streamline the business outcome to a different scale.
Now organizations can measure the effectiveness of a decision and tie it back to the outcome to fine-tune the process further, too. As an AI-focused company, Saxon helps organizations incorporate intelligence into their decision-making.
To start your journey towards Intelligent decision-making, Get in touch with us now.
Hari is a Digital Marketer and Digital transformation specialist. He is adept at cultivating strong executive and customer relationships, utilizing data across all interactions (customers, employees, services, products) to lead cross-functionally as a strategic thought partner to install discipline, process, and methodology into a scalable company-wide customer-centric model. He has 18+ year's experience in Customer Acquisition, Product Strategy, Sales & Pre-Sales Management, Customer Success, Operations Management He is a Mechanical Engineering Graduate with MBA in International Business and Information Technology.