Key ways to improve organizational decision-making using AI

Whenever you see a successful business, someone once made a courageous decision.” ― Peter F. Drucker

Ineffective decision-making costs companies more than half a million workdays a year, across business verticals. Reasons range from lack of empowerment, consensus-driven approaches and unclear organisational roles, to information overload and lack of real debate on critical decisions. It is also quite clear that organisations need to know that there is a trade-off between good decisions and fast decisions and those depend on circumstances at the time of decision-making.

Current trends show that businesses are increasingly turning to AI for help with organisational decision-making. Let's examine two kinds of decision types and how to improve them using decision engines AI.

The first type is big bet decisions, made typically by CXO teams and boards. Such decisions are infrequent yet carry high-risk and have an organisation-wide impact. Let us consider the example of a multi-country chip manufacturing company that wants to diversify into chip design and production for the automotive and agriculture sectors. The investment runs into billions of dollars. With cross-functional representations in such meetings, people typically only represent the area of business that they are familiar with. Siloed modes of capturing and presenting data can inhibit sound decision-making. A good debate around the decision to invest can be initiated with the help of AI, in multiple ways.

  • Create a cross-functional, cross-geography data gathering framework that collects, cleanses, prepares and analyses data across multiple dimensions representing critical functions of the business. These can be fed into AI-based engines to enable building of cross-functional data models that provide visibility and transparency to senior leadership across functions. This only sets the stage for gathering data insights, yet it is a critical input to the AI component.
  • Build cross-functional AI-driven models considering multiple data insight points, e.g. the market size for automotive chips in top four auto-manufacturing geographies coupled with cost of chip production, availability of skill and predict potential for growth, optimal investment and outcomes, represented in a single model. AI enables a well-rounded debate prior to decision making, considering multiple factors, non-linear outcomes and variances that can minimise human biases, examine risks and enable objective decision making.
  • Continuous improvement is a key feature of AI-enabled systems where they learn continuously from the business environment using heterogeneous data that can help with improving strategic decision-making around production planning, operations and manufacturing of chips, in addition to finding the best timing in case of product customisations and maintaining production efficiency

The second type of decision making is cross cutting, which happens frequently across various business groups of an organisation, calling for greater collaboration and involving high risks. Decision making involves heads of cross-functional groups such as operations planning, pricing and product development. AI can help with providing timely cross-functional predictive and non-linear insights to:

  • Quickly launch new products by providing insights in the product development workflow from concept to cash. Business leaders from various functions can take quick decisions around impact of product marketing campaigns and most-wanted features to change tack quickly during product development to incorporate features based on near-real time market data and adapt pricing to market trends.
  • Provide customer-related communication based on monitoring near real-time customer feedback to respond based on preferences and behaviour as micro trends, to predicting customer patterns and trends and adapt messaging to capitalise on a macro trend.
  • Reduce time for performance management cycles by minimising human biases and errors to speed up employee evaluation and decide on learning and upskilling paths that can help assess and take accurate decisions regarding overall company performance and growth.

When AI is included in various business workflows that have structured data, decisions can be delegated to AI engines. AI can be trained on data and insights from cross-functional sources to build thousands of groupings and identify numerous patterns. While costs can certainly be reduced, to enable quick decision making with AI, leveraging both AI and people in business workflows brings about good decision-making. Data driven decision making is already present in workflows and bringing AI into them is the logical next step in organisational evolution. AI and humans will complement each other and companies embracing AI by natively bringing in AI into their workflows, will result in more efficient organisations.

*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future.

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