AI Productivity Scorecard

Organizations face challenges in this AI era, including justifying each AI-enhanced project, measuring the results' effectiveness, and determining where they are on their overall AI productivity journey. While the big picture regarding the productivity race is evident at the national level, we are participating in increasing productivity to create gains in wages, better corporate profits, and raising living standards. See the big productivity picture by clicking here.

Why an AI Productivity Scorecard?

Organizations must understand where they are in unlocking AI's full benefits and increasing optimal productivity. While each organization's AI journey is unique, knowing where organizations are regarding their full AI productivity potential is essential. The scorecard can act as a radar screen to show where organizations or individuals are in terms of full AI potential. Last year, I published a rough guide for AI progress that identified three significant eras for AI. Click here for the three major eras. While it is helpful to know where an organization utilizes AI, a more complete and multi-dimensional productivity scorecard is needed to score how AI is being leveraged for optimal AI productivity over time. See Figure 1 for the AI Productivity Scorecard.

Figure 1 AI Productivity Scorecard

AI Productivity Scorecard Explained

Ideally, an organization has pushed its productivity to the uttermost limits of possibility; in reality, today, few organizations have pushed the boundaries to optimal because of the investment in methods, skills, and techniques that will take time to mature and prove themselves to be very effective. Most organizations start small and grow to complete potential over time. The scorecard aims to measure the progress on the path to optimal productivity. The early AI efforts will start at the center of the radar screen (spider diagram) and move to the edges over time. The scoring from 1 to 5 will be a judgment based on the state of AI at a specific point in time. Remember that AI will grow and evolve; the target could be a moving goal line. To that end, I described what to look for on each scale (vector). While it isn't perfect, it will give business leaders a relative way to measure progress over time. Remember that the scorecard can be used to measure projects and efforts first. However, aggregate efforts can be overlaid for an overall score for an organization, be it a division of the entire enterprise.

Work Impacts Scale: (AKA productivity in work complexity)

AI is excellent at automating repetitive tasks, and there are lots of organizational opportunities to automate totally, assist humans, or collaborate with other AI components. See the Top 20 AI Technologies for 2024 by clicking here. The challenge is having AI agents/bots assist with or make decisions independently within guardrails of goals and boundaries. In an AI-heavy usage scenario, AI makes plans without human collaboration and acts on them with measurement later. It is essential for instantaneous and emergent situations.

Paradigm Impacts Scale: (AKA productivity in problem difficulty)

AI is excellent at optimization as it makes fewer mistakes than its human counterparts. This means that AI clearly sees creating more optimal outcomes while goals shift faster. It assumes that the data it consumes is reasonable, but AI can sometimes sense out-of-whack data. AI can suggest alternative approaches and enhance existing optimizations with new paths or alternative solutions. It involves the creativity of a team of generative AI and humans, initially leading to more AI-driven approaches. In some cases, AI can develop breakthrough views and approaches that can be implanted and optimized on the fly.

Context Impacts: (AKA productivity in scale increase)

AI can help with personal productivity by simplifying each task with more advanced research. However, thought needs to be given to the overall journey a person as a customer, employee, or partner is on towards individual goals that may need to be incorporated with team or organizational goals. Teams often have different skills that must be collaborated on one work impact (explained above). AI assists these teams in incorporating stovepipe skills into optimal team results. Organizations leverage individuals and teams that may or may not have conflicting goals to create the overall organizational goals. AI is great at seeing the big picture and tuning individual and team goals to dynamically support overall organizational and cross-legal entity goals.

Problem Impacts: (AKA productivity in change)

Problems known and static are easy for AI to help with, but AI shines where change is evolving the goals and governance targets. AI is built for change and thrives where things grow on trend lines. There are, however, situations that evolve beyond plans and anticipated scenarios. These are known as emergent problems, which used to be quite rare but are happening more and more. AI deals with changing dynamically and recognizes new scenarios that may require replan and adjustment.

Speed Impacts: (AKA productivity in acceleration)

When work is done regularly, it is much more apt to be automated in a normal and preprogrammed way. As business change velocities increase, AI plays a role in adapting to itself in new ways. In fact, AI agents and bots are great at sitting on the edge and acting instantaneously to dynamic optimization and governance goals. The faster the need for response, the more AI will likely play a key role.

I'd now like to demonstrate the use of scorecards through three examples. The first example is AI in automation. The second example is holistic and dynamic management with AI, and finally, the third example is emergent optimization.

Example 1: AI & Automation (see Figure 2 AI For Automation Scorecard)

Automation is the usual spot where organizations will apply AI successfully. In the scorecard, I depicted a typical AI automation project or program. Typically, these kinds of efforts are aimed at intelligent actions that focus on optimizing results with known problems at expected frequencies but range from personal to organizational impacts. A few example use cases include:

• Smart Chatbots,
• Straight Through Processing
• Knowledge Assists
• Recruiting
• Quality Inspections and Control

Figure 2: AI for Automation Scorecard

Example 2: AI & Management (see Figure 3 AI for Management Scorecard)

Dynamic management at the speed of change while detecting evolving conditions and suggesting tactical or strategic decisions is where AI shines. While the scope can vary from organizational to individual, the example below is aimed at the organizational level. A few example use cases are:

• Supply Chain Management
• Management Cockpits
• Production Line Management
• Warehouse Management
• Logistics and Delivery Management

Figure 3: AI for Management Scorecard

Example 3: AI & Service Team Deployment (See Figure 4 for Service Teams)

Infrastructure servicing is a problem that must be optimized and enhanced over time. Let's use an above-ground pipeline that spans thousands of miles over various terrains, where drones fly over to look for issues and deploy service teams to remote areas when a potential leak is sensed. The drone images will be scoured for known problems and analyzed for evolving conditions, considering local weather, material decomposition, and position norm contexts. Speenorms because of the safety and environmental concerns; however, false alarms are incredibly costly.

 

Figure 4: AI for Service Teams Scorecard

Net; Net:

Progress in AI adoption and resulting productivity needs to be measured, even though scientific precision might not be attainable. Though I have searched long and hard for a way to measure AI's progress, I am still looking for something useful. To that end, I have cobbled together something that will help individuals and organizations have a rough measurement of progress toward greater productivity with AI. I hope this helps others. However, comments for improvement will be appreciated.

Additional Reading:

AI Tech Types

AI For Good

Afraid of AI?

AI Must Increase Productivity or Else

The theme for the coming years will be a significant increase in productivity. According to my favorite definition from a Google search, “Productivity is a measure of performance that compares the output of a product with the input, or resources, required to produce it. The input may be labor, equipment, or money.”

AI must be a key driver to not only innovation but also a way to increase baseline productivity measures. It is a must at the macro level, by country and industry, and at the micro level, with AI-enabled projects. This is true for all on a personal basis and an organizational basis. It is not a zero-sum game where organizations win, and individuals lose. It balances organizational outcomes gained with personal satisfaction without time synchs forced on individuals inside or outside an organization. The good news is that we have high productivity rates in mature economies. See the GDP Per Hour Worked by Region in Figure 1. The bad news is that the productivity increase rate is not what it could be, averaging only a meager 2.1 percent on average since 1947 and a shallow level of 1.6 percent recently. See Productivity Change Rates by period in Figure 2.

                                          Figure 1 GDP Per Hour Worked by Region


The additional good news is that AI is capable and is growing in its influence and impact by the minute. As long as AI is applied in a goal-driven fashion governed by reasonable boundaries, the possibilities are endless. The individuals who control these goals and constraints will lead the way to greater productivity. We can’t sit still and must apply AI aggressively on a local basis, constantly looking to the global impact incrementally. Every country can benefit from AI and potentially leap up in productivity.

                                      Figure 2 Productivity Change Rates by Time Period

Net; Net:

AI has the potential to decrease the hours worked for all of us. An accurate "more with less" enabled by AI. This is true for those who complete repetitive simple tasks, those who make decisions at the tactical level for optimization of work while taking great care of customer, employee, and partner journeys, and those who set strategy or create response scenarios in an ever-changing set of markets, industries, regions, and legal frameworks. While AI is exciting in its potential for productivity, it carries the fear of change and control. Let’s manage this once-in-a-lifetime opportunity AI gives us. This is the first post in a series on the productivity and application of AI. Watch this space.

Art for the 2nd Quarter 2024

I continued experiments with Gen AI, leveraging Kaiber to create more music videos. These videos coincided with my new album, "Ready or Not." Please click here for an album summary. For a quick preview of each song, click here. I generated more AI videos for multiple songs. I started with a storyboard to tell the story of each song. Click here for my song videos so far. Please comment or subscribe as I deliver more in the future. I also created a few more fractals this quarter, as shown below. Visit my art website by Clicking here

Decisions

Layers

Vapors

What Have People Been Reading in the 2nd Quarter 2024?

 Again, AI and Customer Journey topics dominated the activity. However, Results-oriented communications /collaboration is rising fast as a topic of interest. Surprisingly, Processes made the leaderboard for the first time in a while. 

                                        Blog hits in the 2nd Quarter of 2024 

                                                Offshore Activity in the 2nd Quarter 2024

AI: For Good or Evil?

AI has significant momentum now and contributes positively to businesses and everyday life. Today, industry and government leaders warn of the dangers of unbridled AI. So, let's peel back the onion on AI for Good and AI for Evil. Next, let's see what it takes to steer AI positively with as few side effects as possible. We all know all advancements come with good and bad effects. Look at the automobile, for instance. Autos take us to many places, but driving them unsafely without following the rules of the road leads to injury and even death.

AI Brings Good for Many Industries.

· Healthcare uses AI for preventive medicine, advanced diagnosis, personalized treatment plans, and drug discovery.

· Education uses AI for lifelong learning, adapting to changing career changes, shifting to new opportunities, and personal interests with virtual tutors and dynamic personalization.

· Transportation uses AI to optimize the planning and operation of smart cities, support various levels of autonomous vehicles, and optimize eco outcomes within the need for goal-directed efficiencies.

· Finance uses AI for investment management, wealth management, and fraud detection.

· Customer Service uses AI for hyper-personalization, sentiment analysis, and virtual assistants.

· Agriculture uses AI for sustainable farming by optimizing resource uses, automated or not, reducing waste, developing crops for climate change, and practicing sustainable soil management.

· Environmental Protection uses AI to design and implement effective climate change mitigation strategies, biodiversity monitoring, and resource management.

· Manufacturing uses AI in smart factories for optimization, efficient supply chains, and innovative material discovery.

· Entertainment uses AI for immersive experiences, innovative content creation, and audience engagement.

· Accessibility uses AI for inclusive design, enhanced communication across language barriers, and assistive technologies for the less capable.


AI Brings Good for Businesses

Businesses, in general, are using AI for enhanced decision-making in both a proactive and reactive manner. They are Improving the customer experience with AI while increasing their operational efficiency in a balanced fashion with AI. Marketing and sales are expanding their reach through better targeting and predictive forecasting. Businesses are creating new products and services with AI while better supporting their existing portfolio of products and services. AI helps with human resources with better engagement and automated recruitment. AI helps with fraud detection, compliance, and speedier governance. Businesses leverage AI for better expense management and investment strategies. Organizations can predict customer churn and measure customer sentiment in real time. Speaking of real-time, threat detection and vulnerability management can take advantage of AI. Businesses can increase their productivity, revenue, and costs while leading in sustainability through resource optimization and sustainability. Companies can take great advantage of various types of AI.

AI Brings Good for the Consumer

Consumers are experiencing many benefits of AI today, and AI is leading to even more benefits. Personalization provides tailored recommendations, customized voice/language engagement, and satisfaction with 24/7 availability and quick responses to overall needs, not just transactions. The shopping experience is improving with virtual try-ons and augmented/enhanced reality. Health assistants with links to telemedicine will help with preliminary notice of symptoms and diagnosis. Financial advice will be provided to optimize customer goals, both long and short-term. Recommendations for targets in the tsunami of content emerging to help the viewing/listening experiences. Home management and security will benefit from AI as well. Consumers will benefit from sustainability suggestions as well. Civic engagement, cultural preservation, and mental well-being are also benefits.

It's hard to argue that AI is not used for good and that the expectations for more AI benefits are sky-high. Yet, at the same time, some horror is dribbling from under AI. There are bad actors out there trying to do evil with AI. Without all the rules of the AI road laid out yet, there is an opportunity for these bad actors. Some individuals use AI to gain advantage, leverage, and illegal financial gain. Some use AI to subvert power, weaponizing AI for offensive purposes.

AI Enables Bad Actors

· An early evil use of AI is for cyberattacks that target critical infrastructure, financial systems, or government systems for monetary gain, espionage, or sabotage.

· AI can bring social engineering and manipulation by using social engineering techniques to manipulate individuals, influence public opinions, and spread misinformation for financial or ideological gains.

· AI can perform mass surveillance, tracking individual movements, communications, and activities without their knowledge. AI can infringe on privacy rights and civil liberties.

· AI can power autonomous weapons and other forms of lethal weapon systems without human intervention. Drones or robotic soldiers are examples of weapons that could act alone or swarm to escalate conflicts or undermine international stability and security.

· AI can be used by adversaries, including cybercriminals, state actors, and terrorist organizations, to create a movement against established society.

· AI can inadvertently exhibit unintended behaviors or consequences that manifest as errors, biases, or impacts on individuals, organizations, or society at large.

· AI can be harnessed to perform financial crimes and fraud toward individuals or organizations.

· AI can be used to create deepfakes and misinformation to gain financial advantage or take down the reputations of individuals or organizations.

· AI can affect biases and exacerbate inequalities by targeting individuals or groups to displace people from jobs or make it hard to thrive.

· In the worst-case scenario, AI could pose existential risks to humanity if misused or developed with inadequate safeguards.

AI Enables Power Plays

Geopolitical competition will drive rivalries among nations, pushing them to vie for dominance and try to leverage AI for innovation for economic gain and technological and military advantages. While not all this is bad, it can be taken to extremes, leading to new global pressure points and chess matches. Some of it could lead to arms proliferation and a new arms race. The amount of cyberwarfare and spying is bound to increase. The real risk is the need for clear regulations, guidelines, and treaties. There is likely to be a blurring of AI for military and civilian uses that will also muddy the mix. There is also a real danger of a supervillain or group leveraging AI to extort or unduly influence others.

Net; Net:

The fear of Evil AI will not drown out Good AI for now. The control of autonomous weapons, cyber warfare, aggressive intelligence, and psychological operations should be planned for the future. Establishing international regulations, norms, and treaties is the best way forward. Agreeing on what ethical AI development is and monitoring it with transparency. A move towards more “human-in-the-loop” for lethal actions is needed. Establishing robust oversight and governance and promoting peace and diplomacy with AI can work for us. The proactive measures of establishing robust international regulations, enforcing ethical guidelines, promoting transparency, and fostering a culture of peace and diplomacy can mitigate risks and bad behavior from bad actors. The alternative is mutually assured destruction, as we have with nuclear powers.

Additional Reading:

Definition of AI

Future of AI

Fear of AI 

AI Inside Out  

AI Tributaries

Stepping Up the Music Game

 Most of my friends and family know about our new album, "Ready or Not," but its reach has surprised all of us. The numbers are encouraging, and the comments about the quality really excited us. We reached a worldwide audience and 62K streams on Spotify alone. See the charts below for the last 28 days' worth of activity on Spotify. Just search for Jim Sinur on your favorite streaming service. If you don't stream much, click here for the new album. The most popular songs are Forgive, Mercy Me, and Kinder, according to the numbers (see below). My favorites are Cry for Creation and Restoration. 

The AI Gen videos are also drawing folks into the music. In fact, we have had some fortune with longer songs because of the compelling nature of the videos. Click here for all the videos completed to date. The two newest are Mercy Me and Restoration. My favorites are Cry for Creation and Forgive. The core team of Ethan Foxx, Jimmy Caterine, Pete Crane,  and I hope you enjoy the creativity, core stories, and professional engineering. Primarily, we hope to see you dancing to some of these tunes someday. 

When AI Goes Inside Out

AI is progressing well in many industries, assisting people or independently completing tasks. These uses of AI are often operational and can usually be embedded in business processes, software, or devices. We all, except for Luddites, expect the continued success of AI on focused tasks at the operational level. In fact, AI is progressing so well that it is catching up with humans for specific skills and combinations of skills (see Figure 1) from Stamford University below. It all sounds good, but the story could be different as AI ventures out to handle tactical management and executive strategy. AI will break out of processes and devices to combine various types of AI to assist and eventually automatically manage critical adjustments for businesses. It will happen as AI bootstraps success, leaving us with new challenges and driving us into AI fear zones. It is exciting and points to higher benefit levels for AI, but is this a Pandora’s Box?

Sample AI Operational Successes

· Automated Data Analysis: AI algorithms can analyze large volumes of data or content of various types to extract valuable insights and trends, enabling businesses to make data-driven decisions more efficiently.

· Process Automation: AI-powered robotic process automation (Smart RPA) can automate repetitive tasks such as data entry, invoice processing, and customer support inquiries, freeing up employees to focus on higher-value activities.

· Predictive Maintenance: AI can predict equipment failures by analyzing sensor data and historical maintenance records. It enables businesses to schedule maintenance proactively to minimize downtime.

· Dynamic Pricing: AI algorithms: AI algorithms can analyze market conditions, competitor pricing, and customer behavior to optimize pricing dynamically, maximizing revenue and profitability.

· Inventory Management: AI can optimize inventory levels by forecasting demand, identifying slow-moving items, automating replenishment processes, and reducing stockouts and excess inventory costs.

· Customer Service Enhancements: AI-powered chatbots and virtual assistants can handle customer inquiries, provide personalized recommendations, and assist with problem-solving, improving the overall customer experience.

· Fraud Detection: AI algorithms can detect fraudulent activities, such as payment fraud, identity theft, and account takeover, by analyzing patterns and anomalies in transaction data, reducing losses and risk.


We all know that AI is progressing steadily towards an even brighter future for business applicability. AI is picking up skills fast and will equal human capabilities in any individual skill. See Figure 1 for a sample set of skills that AI is progressing.

                                            Figure 1 AI Skill Levels Over Time

This progress is impressive, and when combined with algorithms, goals, and boundaries, AI will go broader, deeper, more complicated, more complex, and more independent. AI will go from task to function while taking on tactics and strategy. Instead of just inside known and established processes, AI will break and challenge coordination and management tasks at the tactical level, eventually working its way into shaping strategy. Thereby putting AI in a position to respond to situations as AI deals well with emergence (complexity); this is an inside-out moment for AI that will start in the coming months and years. I expect the "inside out" trend to begin with processes, as AI can quickly move from tasks to management. The inside-out processes will likely start with monitoring, leading to notification and then to suggestions for action. Eventually, AI will take action with or without permission. 

The transition of AI from inside operational processes to outside processes typically involves the evolution of AI applications from narrow, task-specific implementations to broader, tactical, or strategic capabilities that impact various aspects of the business that cross traditional organizational boundaries. It includes the following:


· Scaling AI Across the Organization

· Integrating with Enterprise Systems

· Cross-Functional Collaboration

· Strategic Alignment and Executive Sponsorship

· Data Governance and Quality Assurance

· Continuous Learning and Improvement

· Partnerships and Ecosystem Collaboration

AI at the Tactical Level

At the tactical level, AI can contribute to essential cross-functional efforts and processes that require constant monitoring and adjustments that are tied to goals (static or emergent). Examples include:

· Customer Relationship Management: Besides the usual inquiry aid, AI can segment customers and proactively predict customer churn.

· Sales and Marketing: AI can drive better lead identification and suggest products/services to those leads. By analyzing activity, AI can target offers individually or with campaigns.

· Supply Chain Management: AI can forecast demand and market trends and tune logistics optimization dynamically while optimizing transportation costs.

· Operations and Manufacturing: AI can optimize production schedules, suggest improvements, and manage energy efficiency.

· Human Resources: AI can streamline recruitment and analyze employee performance for career development.

AI at the Strategic Level

At the strategic level, AI can contribute to the organization's executive level as it monitors the attainment of conflicting goals while maintaining profitability and reputation as a good community member locally and a great place to work while appealing for future investment. It is where emerging conditions must be monitored and intercepted and, where appropriate, changes. AI can start with being a sentinel, but bigger toles may be possible regarding the freedom to act independently. Examples include:

· Market Analysis and Competitive Intelligence: AI algorithms can analyze vast amounts of data from diverse resources to provide insights into market dynamics while identifying opportunities and threats from the competition.

· Forecasting and Planning: AI-powered predictive analytics can forecast future trends, demand patterns, and potential business outcomes, which might mean adjusting capital and resource allocation, inventory management, and production/service planning to optimize efficiency and reduce risk.

· Risk Management and Mitigation: AI can analyze various event patterns and data to identify potential risks and vulnerabilities, such as fraud, cybersecurity threats, and market fluctuations. It allows for proactive risk mitigation and safeguarded assets.

· Strategic Decisions: While AI might not make the decisions initially, AI-powered decision support systems can simulate various scenarios and the likelihood of them happening and have a plan of action. Whether it is a new market or investment, AI can help.

· Product and Service Innovation: AI technologies can be baked into offerings to create new products and services. Examples include computer vision, machine learning, voice-driven sentiment analysis, and intelligent service bots.

Net; Net:

AI will be going inside out and will have more influence on business outcomes at our organizations' operational, tactical, and strategic levels. The question is, what level of freedom will AI be given to act independently, especially if we get into an AI arms race in individual industries or between countries with very different value systems? It is inevitable unless AI has some overall meltdown. I have yet to see AI taking over from humans at the highest level of risk. The question for me is, "Will AI only be used for GOOD once it is given freedom, or Will it also be used for EVIL?" That is a topic for another day. AI will be used successfully as it has proven helpful in many use cases, with more coming. Will the winds of change rip the inside-out umbrella of AI out of our hands?