Anticipatory intelligence & crowdsourcing

Anticipatory intelligence is transforming how we navigate the future, turning uncertainty into opportunity. It allows organizations to prepare for what’s ahead by analyzing patterns, predicting trends, and crafting actionable insights. This approach also addresses key marketing challenges, helping businesses predict consumer behavior, adapt strategies, and stay competitive in dynamic markets. When combined with crowdsourcing, it becomes even more impactful, leveraging the collective wisdom of diverse individuals to refine predictions and uncover innovative solutions.

Whether it’s predicting market demands, optimizing operations, or tackling global challenges, this powerful duo offers businesses a way to stay ahead of the curve while fostering collaboration and creativity. Let’s dive into how these tools are shaping the future and how you can use them to your advantage.

Understanding predictive intelligence

Businesses have been trying to predict future outcomes for centuries. With predictive intelligence, key past event variables that triggered historic outcomes are used to determine the probability of recurrence. The underlying assumption is that past events recur cyclically. This now outdated method was an improvement over previous predictive models.

However, predictive intelligence doesn’t factor in the full scope of past event scenarios to predict future outcomes. Among other things, predictive intelligence fails to harness recent advances in big data analytic tools. Consider this problematic dynamic as it applies to marketing/advertising: the challenge here is two-fold–multiple variables impact markets in complex ways and consumers evolve different subjective filters over time, resulting in unanticipated responses.

Failure to anticipate change agents can have disastrous consequences. Resource-rich corporations like GM, Sears, and IBM are among those that ignored clear signs of developing external shifts. Why? They were locked into outmoded business/market assumptions. IBM, for example, had the wherewithal in the 1980s to be a major competitor in the PC business but stubbornly stuck with mainframe development. Similarly, GM chose to ignore the growing demand for small fuel-efficient cars in the late 1960s resulting in their subsequently losing 30% of their market share.

Anticipatory intelligence: advancing beyond predictive models

Anticipatory intelligence is a major advancement over the predictive intelligence model. By contrast with predictive intelligence, anticipatory intelligence factors in context, changing consumer intent and, most importantly, different conceivable scenarios ranging from probable to outlier outcomes.

Anticipatory intelligence analysis is now easy to implement because of the exponential growth in computer efficiency, bandwidth, and AI tools. Predictive intelligence analysis outlines the most probable outcomes—after weighing the likelihood of different consumer responses to new products, etc. Each scenario is then integrated into a comprehensive strategic analytic model, applied either to the entire company or, in this case, marketing. This allows companies to deftly shift from one strategy to another as conditions change.

An important aspect of anticipatory intelligence is a strong emphasis on emotional intelligence (EI)—, especially empathy. EI makes it possible for us collaborate with other humans in expanding strategic insights and fleshing out possible future scenarios. Research demonstrates that leaders with high EI are much more effective managers. In fact, as AI continues to advance, those with higher level interpersonal skills will have an increasing job market advantage. –This is because AI is not likely to develop communication capabilities comparable to us humans for a very long time, if ever.

Real-world applications of anticipatory intelligence

Anticipatory intelligence isn’t just a futuristic concept—it’s already transforming various industries. For example, in disaster management, it enables governments to predict natural calamities like hurricanes or earthquakes by analyzing weather patterns and historical data. This proactive approach helps authorities plan evacuations and allocate resources before disasters strike, saving lives and reducing economic damage.

Similarly, in marketing, anticipatory intelligence empowers businesses to predict customer behavior and preferences, shaping the future of marketing with hyper-personalized campaigns that resonate deeply with their audience. Urban planners, too, are leveraging this intelligence to design smarter cities that anticipate population growth, traffic congestion, and resource needs.

Anticipatory management decision making

Anticipatory intelligence requires sophisticated data and intelligence gathering techniques, a structured decision-making process and the assignment of roles/tasks. When companies fail to put these things in place, they sooner or later will be blind-sided by external forces. Fortunately, emerging threats, when recognized, can be transformed into opportunities–opening the door to greater growth and a better ROI with comprehensive data analysis and careful strategic planning

A multi-stage process-

• Identify emerging issues
  The first step in the anticipatory management process is to identify emerging issues that can affect the organization. When an organization fails to recognize emerging social trends, they can end up on the wrong side of political issues. Resulting legislation can eventually lead to damaging litigation for organizations failing to adapt to change.

• Monitor issues
  A strategic intelligence system incorporates environment scanning and monitoring to process information about emerging trends and potential events. Once trends are identified, product development, marketing, and other organizational sectors can proactively strategize responses.

• Discern and question underlying assumptions
  Deeply ingrained, implicit assumptions (aka confirmation biases) are part of human psychology. Bottom line—new information at variance with such assumptions is almost always ignored or discounted at a subconscious level. Effective leaders, however, understand that they must identify and challenge their underlying beliefs about the external world as well as their internal business culture to understand future threats and opportunities. One case-in-point: some of the erroneous assumptions that significantly reduced U.S. auto industry market share in the 1970s included–
  • The American car market is insulated from the rest of the world.
  • Foreign competition will never gain a significant portion of the domestic market.
  • Continued energy supplies are assured, and fuel prices will remain stable.
  • Cars are primarily status symbols so styling, not quality, is the most important factor for buyers
  • Rapid obsolescence is desirable.

  As we know from history after the 1973 oil embargo oil prices escalated and remained higher because of OPEC oversight. Consumers began to put quality and gas mileage as the most important factors when buying a new car. Also, families were growing smaller, making large cars less desirable for a growing segment of the population.

• Assign in-house responsibilities
  The division of labor for scenario analysis should include top-level managers as well as lower ranking members from business sectors/departments most affected. C-level executives, the strategic planning office, and a Steering Committee (which evaluates scenario reports, e.g.) are assigned complementary functions

• Assess vulnerabilities
  Performing a vulnerabilities audit reveals a much more comprehensive range of threats, including subtle factors overlooked in predictive analysis. This approach forces managers to see trends as experienced both by supportive and hostile players. An organization can also often gain valuable information about risk factors from organizations that have already implemented policies to deal with the same issue. Common vulnerabilities include disruptive competitive forces, governmental intervention, special interest group initiatives, scientific discoveries, or damaging media disclosures.Assets that must be protected and strengthened include–
  • Quality and integrity of products and services
  • Required talent/skills pool
  • Key resources
  • Technology infrastructure
  • Customer support
  • Cost competitiveness

• Define potential scenarios
  Mapping out hypothetical alternative outcomes is the heart of anticipatory intelligence. Proactive action plans for each can then be developed along with identifying the resources required to dodge and weave potential threats.

• Prioritize issues
  Scenarios can be categorized as (1) high priority requiring immediate action. (2) those not requiring immediate action (beyond creating contingency plans for certain sectors of the organization), and (3) those not likely to materialize soon that require long-term monitoring.

• Category I (high priority) scenario action planning.
  Managing Category I scenario preparation requires–
  • Assigning an “issue owner.”
  • Creating an issue action team
  • Performing a situational assessment
  • Performing an impact analysis
  • Performing a stakeholder assessment
  • Clarifying stakeholder objectives
  • Determining technical objectives
  • Creating a contingency action plan

• Prepare category one scenario reports
  A scenario report is an action plan for a specific scenario. It includes a description of the outcome; the trends, external forces and stakeholders influencing it; weighing its probability, and assessing its potential impact on the organization.

• Evaluate performance and reach consensus on action plan
  The performance evaluation is a review of how the action plan was implemented. This is the responsibility of the strategic planning office (sometimes the public affairs office or issue management office) that oversees anticipatory management. Reaching a consensus on how a scenario would impact the organization requires a final evaluation of the proposed action plan’s strategic viability.

• Category II scenario contingency planning
  Category II issues focus primarily on internal response and compliance. This may involve, for example, implementing a new government program in progressive stages. Such adjustments usually involve those organizational divisions that are most affected.

The following segment is a recap of a January Insights article on the most promising predictive tool to date. I include it again in this post because I believe it syncs beautifully with anticipatory intelligence methodology.

Crowdsourcing synergy: harnessing collective intelligence

Crowdsourcing is more than just a buzzword; it’s a powerful way to supercharge anticipatory intelligence by weaving together diverse perspectives and real-time insights. Imagine tapping into the collective brainpower of thousands of individuals—each bringing unique experiences, skills, and ideas to the table. Platforms like Kaggle and Zindi exemplify this concept, hosting global challenges that invite data scientists, analysts, and problem-solvers to tackle predictive problems collaboratively.

For example, a retail company aiming to forecast the next big product trend could crowdsource innovative solutions by involving not only seasoned market analysts but also everyday consumers and emerging data enthusiasts.

This approach isn’t just about crunching numbers; it’s about creating a dialogue between diverse contributors, sparking creativity, and uncovering insights that might otherwise be missed. By leveraging the collective intelligence of a crowd, organizations can refine predictions, explore unconventional strategies, and stay ahead of market shifts.

What makes this even more exciting is the democratization of innovation. Crowdsourcing levels the playing field, enabling fresh voices to contribute to cutting-edge solutions, often leading to breakthroughs that wouldn’t emerge in a traditional top-down model. It’s a reminder that great ideas can come from anywhere—and when we work together, the results can be transformative.

Start leveraging anticipatory intelligence and crowdsourcing today to revolutionize your digital market research strategies!

Crowdsourcing, AKA swarm intelligence decision making

Swarm intelligence (SI) “is the collective behavior of decentralized, self-organized systems, natural or artificial.” It’s a key component of artificial intelligence developments, proven especially effective in critical business decisions.

Individual ants, bees, and termites are hapless creatures, but brilliant collectively at using scent paths and other complex signaling to collectively locate the best food sources, switching roles for a better division of labor, and creating/maintaining magnificent nest structures. We, humans, are also social animals, but comparatively ineffective in groups. In fact, collectively we often exhibit poor decision-making and destructive spontaneous behavior– as exemplified by mob rioting. We also easily buy into and act upon all kinds of disinformation.

The main problem is we’re lousy at making real-time predictions of likely outcomes. When polled, we usually make decisions based on highly subjective confirmation bias derived from flimsy, first-impression assumptions.

Of course, most folks are unaware of these downsides to conventional human decision-making. Besides, few things scare us as much as sci-fi scenarios of ‘human hive intelligence,’ e.g., Invasion of the Body Snatchers and Star Trek’s Borg. –Fortunately, radical improvements in Big Data analysis are replicating many of the benefits of hive intelligence without our having to lose any sense of our human identity or autonomy.

Recent applications of SI include –

• Data mining – The UNU collective platform uses SI technologies in real time to combine the thoughts and feelings of groups to answer questions and make predictions. Amazingly, testing to date shows that ‘human swarms’ easily out-predict individuals in predicting the outcomes of things like election outcomes (better, e.g., than 2016 polling). Another example– in predicting Academy Award winners, swarm feedback improved the accuracy last year of who would win from 40% for individuals to 70% for the group. The process requires real-time communication among participants that help expand the base of available information before the outcome.

• Warfare – A new generation of SI-driven drones that can make independent targeting decisions, including facial recognition allows for the killing of enemy combatants. This is a deeply disturbing scenario, and it’s all but certain that some combatant will begin using this technology in the future without human oversight.

• Advanced Tech Manufacturing/Assembly – The European Space Agency is developing swarm technology for the assembly of satellites in space thinking about an orbital swarm for self-assembly with magnetic wave communication between components.

Three principles have been cited to explain SI’s success in business decision-making 

• Quick adaptation to changing environments;
• Work role interchangeability – i.e., when an individual can’t complete a task, another quickly takes over; and
• Self-organization, i.e., activity is governed by group consensus, not hierarchical command.

It’s the self-organization principle that executives resist the most. What they overlook is how complex and unpredictable human behavior can be, even when based on a few simple rules. Most importantly, without self-organization, the other two principles of SI don’t work.

SI’s recent record of private sector success is impressive –

• Southwest Airlines was experiencing bottlenecks in its cargo routing system a few years ago. Their previous solution had been the intuitive one of loading freight in the first plane going in the right direction. However, when they used SI analysis to the problem, they discovered that sending cargo in the wrong direction sometimes worked best. Hard to believe? You can’t argue with their results—an 80% improvement in freight transfer rates at their busiest airports and decreased workloads for cargo/baggage handlers by 20%. This new strategy also reduced the need for storage facilities and helped reduce employee wage costs.

• SI analysis has yielded similar results for a wide range of industries with improved scheduling and division of labor in book publishing, telecom, manufacturing, and credit card organizations. For example, Hewlett-Packard uses ‘virtual software agents’ that roam its telecom calling routes to immediately locate congested networks, automatically redirecting them to those with low traffic. Because variances of network traffic volume are difficult to predict, this solves a big problem: it not only accelerates the speed of calls but also ‘decongests’ busy networks.

• Factory efficiency has been improved using variations of the ant-foraging algorithm. Variations on an ant-foraging algorithm have created faster, automatic ways to deploy equipment within required parameters to allocate resources to jobs with the result that all priorities and schedules are met, efficiently adapting to breakdowns. Some critics have questioned the applicability of insect behavior to human interaction. So far, the evidence doesn’t support their concerns.

• Warehousing efficiency has been vastly improved by several companies that have junked the ‘zone approach.’ The zone approach is the process whereby one employee is responsible for processing specific book order or other product categories. It results in an inefficient division of labor because it underuses fast employees while stressing out slower ones with high volume. With the zone approach, even if all zone-assigned employees worked at the same speed, some employees get more orders to process because of fluctuating demand for some book categories over others. The solution, after a careful SI computer simulation—station the slowest workers at the start of the line for ALL products and the fastest at the end. In one warehouse, this improved productivity by 30%.

• SI principles have been applied in the IT industry. In one case, introducing SI strategies resulted in a radical reduction in employee attrition rate (consistent with increased work satisfaction) to 4%, compared with 20% for the industry-at-large.

The future of anticipatory intelligence

As we move further into the digital age, anticipatory intelligence is poised to play an even more significant role. Integration with quantum computing could exponentially increase the speed and accuracy of predictions, while advancements in artificial general intelligence (AGI) promise more intuitive and human-like decision-making capabilities.

his technology also holds transformative potential for global brand building, enabling businesses to predict market trends and craft strategies that resonate across diverse audiences. Additionally, global collaboration platforms will enable organizations from different sectors to share data and insights, fostering a more interconnected approach to solving complex problems. The future isn’t just about predicting what’s next—it’s about shaping it.

Would this work for all organizations? Certainly not. However, those organizations that move in this direction, with careful metrics to monitor the progress of different SI strategies have a distinct advantage over their competition. SI strategies create better internal efficiency and much faster adaptation to rapidly-changing external market forces.

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