If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Bottom Line: Understanding which pricing strategies cause buyers to progress through buying processes in a downturn still isn't completely understood, but AI-based pricing can help remove blind spots in how pricing drives more sales during recessionary times. Even in stable, healthy economic conditions, just 42% of sales professionals are making quota based on Salesforce's State of Sales Report. Only 16% will be over 100% of quota in a given year. In an economic downturn, these numbers shrink, making the struggle very real to make quota in a recession. Here's what it's like to compete on pricing during a downturn: CROs say that sales cycles vary by industry, with automotive being the slowest and medical device manufacturing, medical plastics including PPE production, and consumer packaged goods manufacturers being the fastest.
Newly emerging AI technologies pervade every industry: game-changing products and services like voice-controlled devices, autonomous vehicles, and even cures for illness, are here or on the horizon. Organizations that are AI-driven are making their products more intelligent and optimizing processes like operations and decisionmaking. These capabilities are transforming industries and revolutionizing business. Deep Learning (DL) is at the epicenter of this revolution. It is based on complex neural network models that mimic the human brain. The development of such DL models is extremely compute-intensive and has been enabled, in a great measure, by new hardware accelerators that satisfy the need for massive processing power.
Many important decisions in societies such as school admissions, hiring, or elections are based on the selection of top-ranking individuals from a larger pool of candidates. This process is often subject to biases, which typically manifest as an under-representation of certain groups among the selected or accepted individuals. The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute. Cases include quotas for women on corporate boards or ethnic quotas in elections. This, however, has the potential to induce changes in representation with respect to other attributes. For the case of two correlated binary attributes we show that quota-based debiasing based on a single attribute can worsen the representation of already underrepresented groups and decrease overall fairness of selection. We use several data sets from a broad range of domains from recidivism risk assessments to scientific citations to assess this effect in real-world settings. Our results demonstrate the importance of including all relevant attributes in debiasing procedures and that more efforts need to be put into eliminating the root causes of inequalities as purely numerical solutions such as quota-based debiasing might lead to unintended consequences.
In the past few years, several new matching models have been proposed and studied that take into account complex distributional constraints. Relevant lines of work include (1) school choice with diversity constraints where students have (possibly overlapping) types and (2) hospital-doctor matching where various regional quotas are imposed. In this paper, we present a polynomial-time reduction to transform an instance of (1) to an instance of (2) and we show how the feasibility and stability of corresponding matchings are preserved under the reduction. Our reduction provides a formal connection between two important strands of work on matching with distributional constraints. We then apply the reduction in two ways. Firstly, we show that it is NP-complete to check whether a feasible and stable outcome for (1) exists. Due to our reduction, these NP-completeness results carry over to setting (2). In view of this, we help unify some of the results that have been presented in the literature. Secondly, if we have positive results for (2), then we have corresponding results for (1). One key conclusion of our results is that further developments on axiomatic and algorithmic aspects of hospital-doctor matching with regional quotas will result in corresponding results for school choice with diversity constraints.
Setting sales targets has always been an inexact science, with serious consequences if done poorly. Setting the right sales targets for employees is a difficult balancing act, with long-term consequences on growth and morale. Setting a target too low, making it easily achievable, might cause an an employee to not put in the effort. Setting a target too high can be equally problematic. "Then there is no chance of meeting it," says Doug J. Chung, MBA Class of 1962 Associate Professor of Business Administration in the Marketing unit at Harvard Business School.
Using AI-based advanced analytics might be the answer, argues Doug Chung. Setting the right sales targets for employees is a difficult balancing act, with long-term consequences on growth and morale. Setting a target too low, making it easily achievable, might cause an an employee to not put in the effort. Setting a target too high can be equally problematic. "Then there is no chance of meeting it," says Doug J. Chung, MBA Class of 1962 Associate Professor of Business Administration in the Marketing unit at Harvard Business School.
In this five part series - Sales Tools, Made Smarter - we'll explore how advances in sales tool technology are driving today's planning and performance management for leading sales professionals. Part one examines new considerations and requirements for successful sales forecasting. When Q1 hits, the pressure is on for sales teams to work toward their goals. It is on sales operations and leadership to use historical and current data to establish reasonable quotas. There's a science to creating goals that are realistic, yet motivating, especially as market trends emerge and demand fluctuates more rapidly.
Setting the right sales targets for employees is a difficult balancing act, with long-term consequences on growth and morale. Setting a target too low, making it easily achievable, might cause an an employee to not put in the effort. Setting a target too high can be equally problematic. "Then there is no chance of meeting it," says Doug J. Chung, MBA Class of 1962 Associate Professor of Business Administration in the Marketing unit at Harvard Business School. "The salesperson will be discouraged, and just as unlikely to work to their full potential."
Artificial Intelligence (AI) is the capability of a machine to achieve cognitive functions that are usually associated with people. These cognitive functions include perception, problem-solving, learning, reasoning, environmental interactions, and creativity. The convergence of several technologies including big data, algorithmic advancements, and improvement in computer hardware have led to the growth of AI from hype to reality, and in becoming a useful part of business today. AI has specifically benefited B2B sales and B2B marketing in terms of personalization and efficiency. For example, 64% of marketers that are already using AI state that it has significantly increased their overall marketing efficiency.
For years, sales teams have been inundated with new apps and tools, each promising to help them navigate radical changes in how customers research and buy products. Many sales people are fatigued by tech overload – they're disillusioned by past claims of countless new tools being the next'quick fix', so it's fair that they see artificial intelligence (AI) as yet another tool in their overflowing toolboxes. And, if they listen to some analysts, it's even fair that they fear AI will take their jobs away entirely. But new data from a global study of sales professionals, the third annual State of Sales report, shows AI is being used by high-performers to address major challenges that have been building in the sales profession for years – high-performing sales teams (the top 24 per cent that have significantly increased year-over-year revenue) are nearly five times more likely than underperformers to be using AI. Ai is enabling and helping, rather than replacing, salespeople across the organisation, including inside and outside reps.