The applied intelligence Strategy Formation Process
The six steps to applied intelligence (6ai) is a process driven by a conversational Q&A dialogue-based IP, which is human-centric, with an empathetic approach to gathering information and ideas for grasping authentic meaning. 6ai uses Focused Language Model Templates (FLM-Ts), part of the core IP, to do this. The FLM-T approach is better, significantly less expensive, and easier to manage than traditional LLMs. Focus allows our system to optimally tap into understanding the user, furthering the system’s capacity to retrieve highly relevant information from the user’s inputs. This process begins with identifying the right strategic question to be better informed and engaged with the whitespace of opportunity and risk.
Unlike ChatGPT, Gemini, and more from the LLM world, 6ai is not trying to spit out quick, one-dimensional answers to show how fast and smart it is. Instead, the system focuses on well-considered and thoughtful responses to understand and extract context. It takes time, effort and iteration to establish authentic understanding and begin thinking about the best strategic direction for the user’s idea. Our IP: Conversational Contextualization Questioning Framework (CCQF) and Open-ended Probing Clarifying and Socratic questioning (OPCS) works to capture a higher dimensional level of useful information retrieval from relevant and reliable sources that can be empirically warrantable.
Step one (ai1) looks for an in-depth understanding of what the user is talking about so the system can find the corresponding relevant information to build a fact-based case to further the development of the idea. CCQF/OPCS forms a basis for contextualizing and prioritizing the prompting functionality, flow and sequence. The aim is to create an empathetic virtual space and team environment where users can share their thoughts, ideas, and perspectives through open-ended questioning, helping to generate more thoughtful responses both ways.
This process is highly focused on insight generation and significantly lowers the probability of hallucination, a core flaw of LLMs. Unlike LLM’s enormously broad search parameters, FLM-T presents the opposite, focusing on relevance and prioritization of information for more precise output responses. FLMs are not trying to replace human intelligence; they only utilize generative AI’s narrow intelligence practically, purposefully, and responsibly to augment human intelligence capacity, capabilities, and ingenuity.
The fragile argument LLM promoters keep making is that LLMs can be highly useful but need more time and data to scale to intelligence perfection. But that hasn’t happened, and it won’t! The “scaling” narrative is a fallacy; the idea that just adding more data and more computing, without fundamental architecture and core technology changes, is one for suckers. The objective truth is that “deep learning” is an unproven hypothesis. The scaling narrative is desperation, over-hype, and exhaustive promotion bolster a concept that is not panning out as hyped-up. Scaling can’t go on forever, its promoters can’t continue kicking the ball down the road and saying that AGI is just around the corner. Eventually, it needs to find a game and prove itself. Alchemy might have had its day, but chemistry eventually replaced it, and no gold was ever created, so too, reality and truth will prevail, and the AGI fantasy will fade away.
Getting users’ ideas to fruition through a unique scientific process that focuses on practicality, not abstraction, is fundamentally what 6ai is about. Going back and forth with iteration serves to polish, clarify, and confirm the idea’s feasibility and viability against the reality of the real world. The process seeks to connect the dots and build a fact-based case for assessing the idea’s real market potential. This moves us closer to determining whether or not the idea is worth pursuing or leaving it behind. Just because an idea sounds great doesn’t mean it can be confirmed in reality in a marketplace. Ideas need corroborating evidence, at a minimum, that it has relevance to the realities of the broader marketplace. And the environment must also be right or conducive to the idea’s intrinsic value potential.
This process comprises AI-powered research capabilities combined with computational engines to identify emerging trends and innovations supportive of process-driven idea evaluation. And trying to implement ideas outside the realm of marketplace reality is an exercise in futility. Hence, idea analysis must align with what is objectively true in the real-world environment. Because what one thinks or believes to be true may not have any basis in reality. So, we can save a lot of time and money by determining this upfront, given the number of variables at play in decisioning, and also taking into account the variables that we don’t know about or that the human mind can’t think of. It is best to produce a limited set of possible worlds against which to assess the quality of our ideas. We utilize the six-step applied intelligence (6ai) evaluation process. This process creates and analyzes possible world scenarios and outcomes to determine where the idea can get the best chance of success.
To represent a given possible world, we must define its characteristics. A characteristic is composed of a value pair tied to a particular world; it asserts that a property can have a specific value within that world. Given a set of worlds, each one will differ by at least one characteristic from every other world, and we never favour any particular world based on our own point of view. We represent a future world as a set of properties extracted from general categories and then determine the requirements of the idea. These requirements will be a set of world characteristics that maximize the idea’s chance of success against the set backdrop.
The idea’s requirement will be divided into two parts: the characteristic’s numerical value and the characteristic’s importance. Next, we employ a method to measure each world’s ability to satisfy the idea. To do so, we use an artificial construct in mathematics called a Utility. A utility is a numerical value used to represent the amount of the benefit that is achieved through the implementation of an idea. A world identified as better suited for a particular idea will allow that idea to yield a higher utility value than one that does not meet the idea’s requirements. All ideas have a corresponding world where their implementation is best suited. Nevertheless, the probability of those worlds existing is independent of the idea.
The overriding goal is to select the idea with the highest utility across the most probable worlds and with the optimal conditions necessary for its success because we don’t know how the future will unfold. Multiple functions are involved and happen simultaneously, and computing for probability is based on the likelihood of the inputted characteristics being true. Establishing a set of possible worlds for analysis is logical and allows us to evaluate the facts about each situation. We use an equation to calculate the chances of each world’s characteristics occurring according to the defined variables. Nevertheless, it still all comes down to human decisioning in the end, we’ll have to make some predictions from the available data or use predictions from a trusted source.
We move on to compute the utility of an idea in a particular world as a function of the requirement and its associated world characteristics weighted to the probability of that world occurring. Using mathematics to determine the maximum utility of the idea within each particular world, we create a function that can compare the requirements to the characteristics of the world and return a numerical value based on how well the world satisfies the requirements. Each requirement is weighted by importance such that the utility reflects the idea’s hierarchy of needs.
Using the information flowing from each step and combining it for maximum comprehension, there is a higher likelihood, for example, that each requirement of the idea “A” will be met in the most probable world and yield a higher utility than “B” given that the world necessary for B to be successful has a lower probability of being true. The higher the utility, the higher the idea’s intrinsic value. The following figure shows the representation that depicts the utility of the idea as the interest value rate changes:
We find the expected value of the idea’s utility by summating all the utilities from each possible world. After calculating the utility of each, we determine the expected utility, which will give us the expected performance of the idea based on the possible worlds that may occur. This is an important step because we do not know which world will be realized. By calculating each expected utility, we will determine the best idea to select based on the range of possible futures most likely to occur. Below is a visual representation describing worlds to evaluate.
Looking at an actual strategy generated through this mathematical process described above helps us better understand the benefit of strategy development through 6ai. So, we look at a small Caribbean Island state seeking to build its country’s growth strategy amid the COVID-19 pandemic. The 6ai recommended strategy for this developing nation was that it needed an entirely new growth paradigm, one that is conducive to inclusive capitalism and maximum participation for the shared prosperity of citizens. And one where politicians are no longer put in charge of investment decisions.
The strategy called for professional people to lead idea generation with data and applied intelligence-driven performance-based systems and investment committees responsible for developing economic and investment agendas and strategy execution. Separating politicians from involvement in investment decisions and creating a transparency mechanism accessible to all citizens was identified as having a maximum utility rating. The evidence and analysis were overwhelming about the importance of eradicating corruption and how central that was for the country to have any realistic future growth prospects. This applied to the entire region as well because evidence and analysis were conclusive about the crippling effects of corruption on the state. Corruption, mismanagement, bad self-interest, and political expediency deals repeatedly ranked highest among the many factors holding back sustainable growth objectives.
Accordingly, the 6-step ai process identified opportunities and viable paths for growth, along with dynamic risk assessments based on the most probable world unfolding. The strategy called for looking for new revenue growth opportunities outside of Tourism, providing evidence that Tourism has never proven itself as a sustainable growth industry nor a driver of a middle-class and export-driven growth economy. An over-emphasis on Tourism was easy for politicians but not in the interest of transformative growth. Tourism was identified and correlated with low-income, low-education, and low-skilled economies with minimum to no export growth opportunities.
The 6ai strategy development process identifies the Whitespace of market opportunity: Understanding the Landscape, Confirming the POV, Identifying Opportunity Areas and Risks, and the Decisioning Point. Small island Caribbean states and the region must diversify and deepen their economic structures to become relevant in an ever-increasing globalized world.
Getting rid of UN-type advisors and consultants was also identified as a priority! A significant barrier to growth—big consulting firms only serve to weaken industries and businesses, infantilize governments, and warp Caribbean economies. The analysis revealed that consultants serve neither citizens nor consumers and producers, only themselves and their Western countries’ self-serving neo-liberalism ideals. But not the unique development challenges these Caribbean states face. These economies have become dependent on consulting firms, and consultants have become a central driver of corruption and dysfunction! They have become a big part of the problem! Stunting innovation, obfuscating corporate and political responsibility and accountability, even becoming an impediment to fighting climate change. The overuse of consultants over time has significantly weakened small island states’ economies and social and political structures. The power these consultancies wield via extensive contracts and networks—as advisors, legitimators, and outsourcers—creates the illusion that they are objective sources of expertise and creativity. But they’ve created an ecosystem of destruction.
In conclusion, the case study (strategy) puts forward intelligent technology ecosystems and platform architecture supportive of digital acceleration for all sectors of Caribbean economies, particularly identifying Renewable Energy Production and AgTech as having optimal Maximum Utility for growth. The process also flashed warning signs showing the region remains at a critical juncture of opportunity and risk, and if the region misses this transition opportunity, again, like in other technology transitionary periods of the past. It will likely set the region back behind the prosperity curve for generations.
The analysis underlined that as countries reach a certain point, the activities that were once used or believed to create growth can no longer drive future value. These are called strategic inflection points, crucial to the country’s future economic growth prospects. At each inflection point, countries must reinvent themselves to remain competitive and drive growth through new products and services. Failure to do so can lead to stagnation or a decline in growth and sustained poverty.
Nothing is inevitable or predetermined; what matters most is the next decision and its sequence of decisioning and priorities. The critical decision still facing the region remains whether or not it will adhere to reality and adjust to globalization, not sticking with the old regurgitated and failed policies of the past. Historically, it was found that the future wealth of any nation is directly correlated to the objective choices made. Productivity gains derived through technology investment drive innovation, and the more resilient economies were identified as the increasingly knowledge-based ones.
The final decisions still rest with humans when it comes down to it, so strategy development applications must be human-centric and conducive to helping humans make well-informed decisions, which can also enhance their value to the enterprise. The 6ai process facilitates good decisioning and is a reliable augmenting source to help real people make sound strategy decisions.
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