GenAI as a personal problem solver: a case study

In a day not too far into the future, your teams and you will routinely use AI tools as problem-solving assistants.

AI tools will help you identify solutions to "known-knowns", problems whose perimeter and solutions are well understood by someone, somewhere. They will do so by helping retrieve existing, but not easy-to-find knowledge (think: how to get workers to optimize productivity in hybrid work).

They will help make progress on "known unknowns", the thorny problems whose boundaries are understood but whose solutions often need better collaboration across fields (think: process improvements for risk management processes in the presence of faster and more unpredictable risks, such as those stemming from climate change).

And they will likely put a dent into solving "unknown unknowns", the hairballs where the current solutions, and even the definition of the solutions, is currently suboptimal (think: establishing trust in AI-assisted social networks" or "mitigate the AI-induced labor unrest).

Here's a glimpse of that future, that is feasible today.

AI-augmenting innovation and problem-solving processes

My teams, colleagues, and I, at process and technology firm Genpact (NYSE: "G") and at MIT's Center for Collective Intelligence (see for instance the now-public MIT Ideator) have been working on making generative AI an effective enabler of problem-solving and innovation for since 2020. I have written about the big picture - engaging machines to ideate harder, beyond the current typical results (eloquent but bland) to typical queries, here and here .

In the last months, together with luca taroni we pushed the envelope further, built an early prototype of a versatile problem-solving tool, and we have been experimenting with a wide variety of problems.

To illustrate the potential of this new type of tool, I took two use cases similar to what many of us struggle with these days: preparing workforces for the impact of generative AI, and finding appropriate use cases for AI that are likely to lead to enterprise AI adoption .

Below are examples of simple, straightforward solution-identification workflows that took 10 minutes each to complete. (You find the outputs at the bottom. The prompts and code for chaining the workflow are not displayed here, as they're part of the tool.).

The tool is a Problem & Solution "Explorer", a type of AI-assisted "idea collider ". Its high-level flow is simple and follows a typical problem-exploration / solution-exploration path, illustrated below.

Even when implemented simply, the value of properly configured AI-assisted workflows is that they amplify what humans can do in isolation, hence opening up a larger space for exploration, conveniently, inexpensively, and scalably.

AI-assisted ideation process: two examples

Imagine that you have one of the two following challenges that you want to find solutions to:

A. How to use AI in your business process. Perhaps you're not convinced that you're using AI enough to improve your procurement function to its full extent. Sounds mundane? Procurement improvements go directly to an organization's bottom line, for instance; and procurement is heavily scrutinized for environmental and social sustainability purposes. A good example of a "known-unknowns" problem.

B. How to prepare your organization, and its people, for the dislocation that Generative AI will create. This is a more strategic task, typically approached with external consultants and involving senior leaders in the company. It is also a multi-faceted, unwieldy problem. In many respects, an "unknown-unknown" challenge.

You could read about the subject, involve some in your team, or get some experts in a meeting - but before doing that, and even while doing those things, you want to know enough to be able to push the thinking.

The tool takes the user through various steps, like

• defining the problem better, for instance looking at it end-to-end
• isolating its components, and abstracting them into more general categories
• finding relevant and interesting analogies for inspiration
• inventorying the user personas, from procurement to legal, within and outside of the company, and their struggles
• analyzing the problem through specialized lenses (such as Lean 5-whys and Fishbone, or others such as HR-specific ones)
• generating AI and other technology-based options
• scoring the results based on feasibility and novelty criteria among others

At each point, the human in the loop can redirect the machine, for instance by adding context or asking it to use different lenses borrowed from various disciplines (strategy, operations, org design, innovation).

Clear value already emerges, and this is just a start

The tool's value, in this instance, is not in having found earth-shattering new solutions to an unclear problem (though one can use such a tool for that too, and some of the solutions below were not obvious). In this case, I wanted to put myself in the shoes of the average practitioner who needs a broad understanding of the problem and possible opportunities, before or while engaging with domain experts. That scenario is very prevalent and immensely important because true transformation typically requires professionals from multiple disciplines to collaborate and overcome their respective knowledge gaps. The inability to understand and support each other in understanding the opportunity (the "what") and creating and assessing the desirability/viability of solutions (the "how), hinders improvements.

While the results are interesting at many levels, I consistently find this type of system particularly helpful in exploring and structuring the problem space. This is not something that humans typically like to spend a lot of time doing, as most professionals want to quickly move to the "solving" part of the process. However, innovation experts do know that better solutions typically stem from well- and more creatively-defined problems. (Einstein allegedly said "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions" and design pros swear by "falling in love with the problem before falling in love with solutions"). A tool like this does help: it is patient, it doesn't mind doing lots of mental gymnastics, and it is pretty solid in structuring the results.

To be fair, I could have run the tool a lot harder too, even in its current incarnation. I could have taken the machine into partial sidetracks as the tool we built easily caters to a variety of lenses from strategy to operations, and innovation. I could have added more of my own reflections. I could have even launched an autonomous agent that looks at a variety of lenses and then summarizes its own findings for me. I could have done this with a team, during a formal workshop. Instead of 10 minutes, if this was a real project, I could have allocated a few hours - but very likely, it would have made me shrink the time-to-value by a factor of 2 or more, as it does feel like you get done in a few minutes as much as what often would take an hour, with less friction.

There are clear additional functionalities that one could build into the workflow. Things like adding more external data by feeding the system with the latest on something (e.g. new papers talking about generative AI's use cases); and adding more internal data, proprietary to the company, without complex fine-tuning (Retrieval Augmented Generation, or "RAG").

Importantly, today's models (in this case, I used Anthropic's Claude) have a good context window, which enables the tool to "keep in mind" much or all of the thread and build on it later in the process. What's really exciting, is that these attention windows will continue to grow in the future, and the workflow will be able to use previous or additional outputs even more liberally, for example by deliberately recombining some of them and exploring them as specific threads, until a final filtering and recombination. (More on this in the article here .)

Overall, the workflow shown here isn't something that any user can get out of the box - but it can be built quite inexpensively as long as we stay clear of some of the technical pitfalls luca taroni and I encountered when building it.

The main point is: generative AI is ready to help you with in these use cases today. In the words of Ethan Mollick - this is already well within the "jagged frontier" - and within your organization's strike zone.

If you need more detail, help, or guidance, get in touch.

Exhibit: Explorer Workflows

1/2. Addressing generative AI's impact on the workforce.

Problem statement "I am the CHRO of a large company with global operations. With the advent of generative AI, many jobs will be displaced by automation, or reshaped by AI augmentation. We need to plan a thorough workforce management strategy and solutions. Help me find solutions, both in terms of interventions, as well as developing organizational plans and processes to get there"

ai_msg: Define problem in more detail

Answer: The problem is that advances in AI and automation technologies are rapidly making many jobs redundant or requiring new skills. As CHRO, I need to plan and implement strategies to reskill and redeploy employees whose jobs are displaced, while retaining organizational knowledge. This will involve changes to hiring, training, job design and potentially reductions in force. Key challenges will include managing morale, retaining key talent, budget constraints, legal compliance, and cultural resistance. We will need data to understand the types of roles and skills at risk, and involve stakeholders across the organization to develop solutions. More broadly, the advent of AI requires rethinking how humans and machines best complement each other in the workplace.

user_input: Also keep explicitly into account that given the global nature of our workforce, our approach will need local nuance, not just a global approach.

Answer: The problem is that advances in AI and automation technologies are rapidly making many jobs redundant or requiring new skills. As CHRO of a global company, I need to plan and implement localized strategies to reskill and redeploy employees whose jobs are displaced, while retaining organizational knowledge. This will involve changes to hiring, training, job design and potentially reductions in force. Key challenges will include managing morale, retaining key talent, budget constraints, legal compliance, and cultural resistance. We will need data to understand the types of roles and skills at risk globally and locally, and involve stakeholders across the organization to develop nuanced solutions for each region. More broadly, the advent of AI requires rethinking how humans and machines best complement each other in the workplace.

user_input: proceed to the next step

ai_msg: Break down the problem into its individual components

Answer: The key components of the problem are:

1. Understanding which roles are likely to be displaced by AI/automation and which skills will be in demand
2. Assessing the capability and willingness of existing employees to reskill and take on new roles
3. Designing and implementing reskilling/upskilling programs and training
4. Redeploying displaced employees into new roles aligned with their reskilling
5. Hiring new talent with required skills if reskilling existing employees is not feasible
6. Redesigning jobs and processes to incorporate AI augmentation and human-machine collaboration
7. Managing layoffs and redundancies if workforce reduction is necessary
8. Change management and communication to maintain morale and retention
9. Budgeting and securing funding for reskilling initiatives
10. Ensuring legal compliance around hiring/firing, data privacy, etc.
11. Getting buy-in from business leaders and managers on workforce changes
12. Developing localized solutions tailored for each region/country

user_input: MoreDetail

Answer: Here is additional detail on some of the key components:

1. Understanding displaced roles and skills in demand will require data analysis of trends in automation, job profiles, and skills assessments of employees. This needs to be an ongoing process as technology evolves.
2. Assessing capability and willingness to reskill will require skills assessments, training needs analysis, surveys and interviews to understand motivations.
3. Reskilling programs must consider delivery methods (online, in-person, on the job), content tailored to role transitions, and schedules that work for employees.
4. Redeployment needs to match employees to new roles based on reskilling, retention risk, business needs.
5. Hiring for new skills may involve partnerships with education institutes, contractor networks, or acquisitions.
6. Job redesign should focus on human-machine collaboration, playing to strengths of each.
7. Layoffs should be treated with sensitivity and support. Clear criteria can help avoid discrimination claims.
8. Change management requires early stakeholder engagement, transparency, and two-way communication.
9. Budgets need to factor in program development, delivery costs, and potential severance.
10. Legal compliance spans areas like data privacy, fair hiring practices, and employment law.
11. Getting buy-in from managers on changing roles will require evidence of benefits and consultation.
12. Local solutions should account for variations in demographics, culture, labor regulations.

user_input: We also need to think about the change of work practices, AKA "future of work", as they need to be optimized in tandem with the workforce planning practices, in order to maximize engagement and productivity, as well as preserve fairness and ethics.

Answer: Here is additional detail on some of the key components:

1. Understanding displaced roles and skills in demand will require data analysis of trends in automation, job profiles, and skills assessments of employees. This needs to be an ongoing process as technology evolves.
2. Assessing capability and willingness to reskill will require skills assessments, training needs analysis, surveys and interviews to understand motivations.
3. Reskilling programs must consider delivery methods (online, in-person, on the job), content tailored to role transitions, and schedules that work for employees.
4. Redeployment needs to match employees to new roles based on reskilling, retention risk, business needs.
5. Hiring for new skills may involve partnerships with education institutes, contractor networks, or acquisitions.
6. Job redesign should focus on human-machine collaboration, playing to strengths of each.
7. Layoffs should be treated with sensitivity and support. Clear criteria can help avoid discrimination claims.
8. Change management requires early stakeholder engagement, transparency, and two-way communication.
9. Budgets need to factor in program development, delivery costs, and potential severance.
10. Legal compliance spans areas like data privacy, fair hiring practices, and employment law.
11. Getting buy-in from managers on changing roles will require evidence of benefits and consultation.
12. Local solutions should account for variations in demographics, culture, labor regulations.
13. We also need to optimize work practices and policies around remote work, flexibility, performance management, etc. to maximize engagement, productivity, fairness and ethics.

user_input: proceed to the next step

ai_msg: Identify analogies to this problem

Answer: Here are some potential analogies for this workforce displacement problem:

• The transition from agricultural to industrial economies - As machinery displaced farming jobs, workers had to shift to factory and city jobs. This required large-scale retraining and migration.
• Electrification of industry - As factories switched from steam to electrical power, many roles needed reskilling to operate new machines and processes.
• Introduction of computers in the workplace - As mainframes, PCs, software changed work, employees had to learn new technical skills to remain relevant.
• Offshoring and automation in manufacturing - As factories automated and moved overseas, manufacturers had to retrain workers for new roles or downsize.
• Disruption of retail industry by ecommerce - As online shopping grew, retail employees needed training for omni-channel operations and digital skills.
• Adoption of self-driving vehicles - As autonomous trucking and taxis scale up, drivers will need reskilling for new types of fleet management/logistics roles.
• Pandemic acceleration of remote work - As workplaces went remote, training was needed to adapt management, collaboration, digital skills for the new normal.
• Age of sail to steam power - As steamships displaced sailing ships, sailors reskilled for engine room and coal handling roles.

The key is to proactively reskill and transition workers, like in these past disruptions. But the pace of change today is much faster.

user_input: proceed to the next step

ai_msg: Zoom out to identify the broader category of problems related to it

Answer: Zooming out, this problem belongs to the broad category of managing human capital through technological and economic change.

Some related problem categories include:

• Workforce planning - Anticipating hiring needs, capability development, and resource allocation as business strategies evolve.
• Organizational change management - Leading people through transitions while minimizing productivity loss and resistance.
• Skills gap analysis - Identifying current and future skills mismatches and developing talent strategies to address them.
• Displaced worker assistance - Supporting those laid off due to macroeconomic shifts find new opportunities.
• Education and training reform - Adapting educational systems to equip students and adult learners with relevant future-proof skills.
• Labor market economics - Understanding the dynamics between technological shifts, employment patterns, wages, and related government policies.
• Business model innovation - Developing new value propositions, structures and processes to adapt to the future of work.
• Ethics of automation - Ensuring human dignity and agency as automation changes the workforce makeup.

The advent of AI spans elements of all of these problem areas. Taking a broad perspective is needed to develop systemic solutions.

user_input: proceed to the next step

ai_msg: Identify root causes of the problem (5 Why's)

Answer: Here is a 5 whys root cause analysis:

1. Why are jobs being displaced by AI?
2. Because AI/automation can perform many routine and rules-based tasks more efficiently than humans.
3. Why are companies adopting these technologies?
4. To reduce operational costs and improve productivity and efficiency.
5. Why do companies prioritize cost reductions and efficiency?
6. To maximize profits and remain competitive in their industries.
7. Why don't companies invest more in retaining and retraining existing workers?
8. Because training and transition programs are expensive and ROI is unclear. Lack of regulations.
9. Why don't regulations mandate workforce transition support?
10. Because rapid technological change makes implications unclear. Vested interests lobby against additional costs.

Root causes seem to be around short-term profit seeking behavior and misaligned incentives between companies and workers. Also lack of vision to see long term societal risks.

user_input: An additional cause is that individual workers and managers can't fully adjust without a companywide plan, which prompts a central support interventions.

Answer: Here is an updated 5 whys root cause analysis:

1. Why are jobs being displaced by AI?
2. Because AI/automation can perform many routine and rules-based tasks more efficiently than humans.
3. Why are companies adopting these technologies?
4. To reduce operational costs and improve productivity and efficiency.
5. Why do companies prioritize cost reductions and efficiency?
6. To maximize profits and remain competitive in their industries.
7. Why don't companies invest more in retaining and retraining existing workers?
8. Because training and transition programs are expensive and ROI is unclear. Lack of regulations.
9. Why don't regulations mandate workforce transition support?
10. Because rapid technological change makes implications unclear. Vested interests lobby against additional costs.
11. Why can't individual workers adjust on their own?
12. Because reskilling requires company-wide coordination and support to be effective.

Root causes seem to be short-term profit seeking, misaligned incentives between companies and workers, lack of vision on societal risks, and the need for centralized coordination.

user_input: DoAgain

Answer: Here is an alternative 5 whys root cause analysis:

1. Why are jobs being displaced by AI?
2. Because AI can automate repetitive tasks and lack creativity of human workers.
3. Why can't human workers adapt their skills?
4. Because continuous learning and creativity are not sufficiently valued in the workplace.
5. Why aren't human skills like creativity and learning valued?
6. Because business metrics focus on efficiency and cost reduction.
7. Why don't companies value human potential?
8. Because short-term shareholder interests dominate over long-term societal impacts.
9. Why does shareholder primacy rule business decisions?
10. Because of an overemphasis on economic metrics versus humanistic values.

Root causes seem to be an over-reliance on technology/efficiency, undervaluing human creativity, lack of lifelong learning culture, and shareholder primacy overriding purpose. Solutions should focus on human-centric business metrics, continuous education, and balancing profit with social impact.

user_input: proceed to the next step

ai_msg: Identify problems upstream and downstream from this problem, looking at the problem end to end

Answer: Here are some problems upstream and downstream of the workforce displacement issue:

Upstream: - Lack of collaboration between industry and education in developing future-proof curricula and skills - Insufficient government investment in promoting STEM and digital skills for the future workforce - Companies' short-term focus on automation for cost savings versus long-term societal impact - Workers lacking awareness and motivation for proactive reskilling

Downstream: - Workers struggling to find new jobs after redundancy, resulting in long-term unemployment - Skills gaps leading to unfilled positions, hurting business growth - Economic impacts like loss of consumer spending if unemployment rises - Rise of mental health issues, substance abuse for displaced workers - Increase in income inequality as mid-level jobs are automated away - Social instability and populism if significant segments of society are left behind

Solutions need to take a holistic view spanning from education to policy to support for displaced workers. An end-to-end workforce strategy is required, engaging all stakeholders.

user_input: proceed to the next step

ai_msg: Identify the personas involved in the problem, including their motivations and characteristics.

ai_msg: Here are some key personas involved in this workforce displacement problem:

• Displaced employees: Motivated to retain income/livelihood. Fear job loss but open to reskilling. Vary in ability to learn new skills.
• Retained employees: Want career growth. Concerned about changing roles. Motivated to reskill for job security.
• Leadership/Executives: Goal to cut costs, boost efficiency and profits. Under pressure from shareholders. Risk-averse to major changes.
• HR/Learning & Development: Motivated to retain talent. Concerned about morale and managing change. Stretched on resources.
• Hiring managers: Need to fill open roles. Biased toward external hiring over redeployment. Focused on short-term needs.
• Policy makers: Motivated toward full employment. Under pressure from corporate interests. Concerned about automation's societal impacts.
• Educators: Want to develop relevant skills in students and adults. Constrained by slow-changing curricula. Open to partnerships.
• Unions: Prioritize protecting members' jobs and benefits. Distrusts corporations' motivations. Willing to negotiate on gradual change.
• External candidates: Looking for job opportunities. Have relevant skills that employees may lack. Adaptable to changing needs.

The key is aligning the motivations of these personas for a successful workforce transition.

user_input: please add investors

ai_msg: Here are some key personas involved in this workforce displacement problem:

• Displaced employees: Motivated to retain income/livelihood. Fear job loss but open to reskilling. Vary in ability to learn new skills.
• Retained employees: Want career growth. Concerned about changing roles. Motivated to reskill for job security.
• Leadership/Executives: Goal to cut costs, boost efficiency and profits. Under pressure from shareholders. Risk-averse to major changes.
• HR/Learning & Development: Motivated to retain talent. Concerned about morale and managing change. Stretched on resources.
• Hiring managers: Need to fill open roles. Biased toward external hiring over redeployment. Focused on short-term needs.
• Policy makers: Motivated toward full employment. Under pressure from corporate interests. Concerned about automation's societal impacts.
• Educators: Want to develop relevant skills in students and adults. Constrained by slow-changing curricula. Open to partnerships.
• Unions: Prioritize protecting members' jobs and benefits. Distrusts corporations' motivations. Willing to negotiate on gradual change.
• External candidates: Looking for job opportunities. Have relevant skills that employees may lack. Adaptable to changing needs.
• Investors: Motivated by return on investment. Prioritize short-term profits over long-term social impacts. Push leadership for cost cutting.

The key is aligning the motivations of these personas for a successful workforce transition.

user_input: proceed to the next step

ai_msg: Summarize orthogonal (non overlapping) concepts explored in the Explore problem phase Explore solutions phase:**

Answer: Here is a summary of orthogonal concepts from the Explore Problem phase:

• Defining the workforce displacement problem in detail, including challenges like morale, budget constraints, and change resistance
• Breaking the problem into components like assessing displacement risk, reskilling, redeployment, hiring, job redesign, layoffs, and change management
• Identifying historical analogies like the transition from agriculture to industry and introduction of computing
• Relating the problem to broader categories like organizational change, skills gap analysis, and education reform
• Conducting root cause analysis using the 5 whys technique
• Mapping upstream issues like education-industry misalignment and downstream impacts like unemployment
• Outlining key personas like displaced workers, executives, policy makers, educators, and their motivations

These perspectives provide a comprehensive understanding of the workforce displacement problem and context for developing solutions.

user_input: proceed to the next step

ai_msg: Keeping into account the previous ideas, provide a list of novel solutions. Novel solutions are those that are not common today. They may be based on recombinations of ideas currently existing, or could be totally new.

Answer: Here are some potential novel solutions to the workforce displacement problem:

• A specialized "Talent Exchange" platform that matches displaced workers to new roles across industries based on transferable skills. Uses AI to identify non-obvious job transitions.
• Mandatory "future-skilling" training for all employees - 1 day a month is dedicated to learning emerging skills needed for the future.
• Introduction of a "human creativity" quotient metric to complement productivity metrics when assessing human vs. AI roles.
• Companies share a portion of automation efficiency gains with society by funding universal basic income programs.
• Displaced employee collectives that allow groups to start their own small businesses or social ventures with seed funding and training from their former employers.
• Encouraging sabbaticals and adult gap years focused on re-education and reskilling through tax benefits and subsidies.
• Technology vendors must contribute to workforce training funds proportional to the automation job displacement potential of their products.
• Gamified predictive assessments that identify employees' innate capabilities and match them to emerging roles.
• Integration of human-centered ethics and philosophy courses into STEM education to develop wisdom alongside technical prowess.

user_input: DoAgain

Answer: Here are some alternative novel solutions:

• Companies adopt a 4-day work week to allow more time for employees to pursue education and self-directed reskilling.
• Introduction of Chief Automation Ethics Officer role responsible for managing job displacement impacts.
• Tax incentives for companies that retrain and redeploy over laying off workers.
• Encourage employee ownership models and profit sharing to distribute automation gains.
• National program for mid-career professionals to temporarily work as teachers and share experience.
• AI assistants are provided to augment human capabilities rather than replace jobs.
• Launch innovation competitions for displaced workers to submit business ideas and receive funding.
• Companies sponsor community volunteering sabbaticals for reskilling employees aligned to social causes.
• Industry consortiums coordinate to create an open jobs platform and skills taxonomy to enable mobility.
• Change recruiting mindsets to value transferable skills over narrow job experience.

The focus is on human-centered solutions that build worker agency, skills, and new opportunities.

user_input: Say more about future skilling, and related processes

ai_msg: Here are some additional details on future-skilling programs as a solution:

• Future-skilling involves proactively training employees on skills needed for future roles vs. just current roles. This develops strategic capability.
• Companies could dedicate regular time (e.g. Fridays) for employees to take online or in-person future-skill training courses.
• Training content could cover both hard skills like AI, analytics, cloud computing and soft skills like design thinking, empathy, and change management.
• L&D teams must continuously research and predict the skills that will be in-demand in 3-5 years and build those into training curricula.
• Future-skilling needs to happen at all levels - entry-level, managers, senior leaders, to build an adaptive culture.
• Training should balance teaching new technologies with helping apply those tools to enhance uniquely human strengths.
• Reskilling for displaced workers could follow a similar continuous learning model - both during transition and in their new roles.
• Companies should provide learning time, content, and incentives for future-skilling and partner with educational institutions to design programs.
• Industry associations can also coordinate to build shared future-skilling platforms on emerging skills.

The goal is to make continuous future-skilling a core element of workplace culture rather than a one-time program.

user_input: what about understanding the skill gap, in aggregate as well as for each individual?

ai_msg: You're right, assessing skill gaps is an important component of future-skilling programs:

• Companies need skills gap analyses to understand where current capabilities fall short of future needs, both at an aggregate and individual level.
• This requires creating skills profiles for each role mapping capabilities to activities. Profiles should be forward looking.
• For individuals, assessments can combine self-evaluations, manager inputs, and assessments/testing to build skills profiles.
• Gaps found can feed into personalized reskilling roadmaps and development plans for each employee.
• Aggregate gap analysis can shape overall workforce planning, hiring priorities, and training curriculum design.
• Ongoing skills assessments are needed as jobs evolve and new skills emerge. Regular check-ins ensure development stays on track.
• External partnerships can supplement internal skills benchmarking. Industry associations can compile anonymized profiles to identify wider capability gaps.
• Reskilling programs should start by diagnosing skill gaps, tailored support to fill those gaps, and follow-up to verify capabilities.

You're absolutely right - robust skills gap assessment is crucial for implementing effective future-skilling initiatives. Thank you for raising this.

user_input: proceed to the next step

ai_msg: Based on the previous analysis, provide a list of relatively easy to implement solutions. These are solutions that can be readily implemented today. They may be based on recombinations of ideas currently existing.

Answer: Here are some workforce displacement solutions that could be relatively easy to implement today:

• Create internal talent marketplaces that allow employees to apply for redeployment into suitable open roles in other departments.
• Offer training sabbaticals where employees can take 1-3 months to immerse in reskilling courses while still employed.
• Implement job shadowing and stretch assignments to help employees gain experience in adjacent roles they could transition into.
• Launch an AI Ethics review board to assess automation initiatives for workforce impacts and oversee responsible implementation.
• Increase learning & development budgets and expand online training content and channels.
• Provide tuition reimbursement for employees pursuing degrees and certifications in high-demand skills.
• Partner with online education platforms like Coursera, Udacity, and edX to supplement internal training.
• Offer clear reskilling incentives like bonuses, future promotion opportunities, flexible work options.
• Create alumni networks and talent databases of former employees open to returning in new roles.
• Purchase predictive workforce analytics tools to model displacement risks and future skills gaps.

These solutions focus on expanding development opportunities and enabling internal mobility through upskilling.

user_input: proceed to the next step

ai_msg: Now build on this idea, by identifying some relevant technologies that could strengthen those solutions. Be specific and give examples.

ai_msg: Here are some examples of relevant technologies that could strengthen the workforce displacement solutions:

• Talent marketplace platforms powered by AI-matching algorithms that analyze employee skills, interests, personalities to suggest optimal job moves. Example: Eightfold's Talent Intelligence Platform.
• Virtual reality training simulations that create immersive on-the-job reskilling environments. Example: Strivr for frontline worker training.
• Digital credentialing technologies like blockchain to validate skills learned through online education platforms. Example: Learning Machine's BlockCerts.
• Machine learning-driven skills gap analysis that benchmarks employee capabilities to emerging skill demands. Example: Degreed Skills Insights.
• Career guidance chatbots that interact with employees to provide personalized reskilling recommendations. Example: MeetCleo AI career coach.
• Collaboration tools like MS Teams, Slack, and Asana to enable remote on-the-job training through digital expert coaching.
• Data integration platforms linking HR systems, skills databases, and learning platforms to track development. Example: Visier data analytics.
• Online assessments powered by computer adaptive testing to accurately evaluate capabilities. Example: SHL Talent Measurement Solutions.
• Mobile learning apps that provide bite-sized future-skilling content accessible on any device. Example: Axonify microlearning platform.

The goal is to leverage technology to personalize, accelerate, measure and integrate workforce reskilling at scale across the organization.

user_input: What could professional network platforms like LinkedIn do?

ai_msg: You're right, professional networking platforms like LinkedIn could also play a role:

• LinkedIn could partner with employers to provide discounted or free access to their online learning courses for reskilling employees.
• Their data on skills demand could help identify high growth skills to guide reskilling efforts.
• Their Alumni and Learning tools could be used to showcase reskilling programs and employee skills.
• Users could get endorsements and recommendations for skills learned through employer reskilling programs.
• LinkedIn's network could connect employees to mentors employed in in-demand roles they want to transition into.
• Features like Open to Work and expanded profiles could help displaced workers market new skills to prospective employers.
• Talent Insights analytics could help employers map internal skills to benchmark against wider industry demand.
• LinkedIn Learning paths could be customized to create guided reskilling journeys for specific role transitions.

Overall, LinkedIn's professional data, networks, and learning content assets can be leveraged to complement employer-led reskilling initiatives.

user_input: Try harder: LinkedIn has data about career transitions, for hundreds of millions of people across thousands of fields. Could that be used?

ai_msg: You're absolutely right, LinkedIn's unmatched professional data presents huge opportunities here:

• LinkedIn's graph of millions of member profiles contains rich historical data on career transitions that can power predictive models identifying viable job change paths.
• Advanced analytics on their member career trajectories data could uncover non-obvious job pivots and skills bridges between roles that can inform reskilling roadmaps.
• This data could be used to build an AI-powered career guidance tool that gives personalized recommendations to users on viable job transitions based on their current profile and goals.
• Employers could tap into aggregate insights from this data to model the skills, experiences, and trajectories of high performers who have successfully transitioned roles in the past. This can shape reskilling programs.
• Members could search LinkedIn profiles to find others who have made similar career shifts as role models. The platform can connect them.

You're spot on that LinkedIn sits on a goldmine of data that if tapped effectively, can significantly improve career transitions and workforce reskilling at scale. I appreciate you pushing me to think deeper here.

Conclusions**:

ai_msg: Here is a summary of orthogonal concepts covered in the Explore Solutions phase:

• Proposed novel solutions like talent exchange platforms, mandatory future-skilling programs, human creativity metrics, and ethics training.
• Easy to implement solutions like internal talent marketplaces, training sabbaticals, job shadowing, budget increases, and alumni networks.
• Discussed technologies like AI-powered skills gap analysis, VR training simulations, credentialing blockchains, career chatbots, and mobile microlearning apps.
• Explored how platforms like LinkedIn could provide data, networks, and learning to aid reskilling, based on their career transition insights.
• Emphasized the need for personalized, scalable solutions focused on expanding human capabilities versus just automation efficiency.
• Presented examples of real companies offering relevant technologies and platforms.

The key focus was on using human-centric design, leveraging technology, and enabling transitional support mechanisms to facilitate smooth workforce reskilling.

user_input: proceed to the next step

ai_msg: Score prevoius orthogonal solutions from the Solutions phase based on technical, organizational, legal feasibility. Do so on a scale of 0-5, with 0 being the worst, and 5 the best.

ai_msg: Here are feasibility scores for previous solution ideas:

Talent exchange platform - Technical: 4 (proven matching algorithms exist) Organizational: 3 (requires coordination across biz units) Legal: 3 (data privacy considerations)

Mandatory future-skilling - Technical: 3 (tools exist but still evolving) Organizational: 2 (hard to mandate at scale) Legal: 4 (no major constraints)

Human creativity metrics - Technical: 2 (early stage conceptual) Organizational: 2 (cultural shift needed) Legal: 5 (no constraints)

Internal talent marketplace - Technical: 5 (mature enterprise tools available) Organizational: 4 (change management requirements) Legal: 4 (data privacy, non-discrimination)

Training sabbaticals - Technical: 5 (simple to implement) Organizational: 3 (production impact) Legal: 5 (no constraints)

Let me know if you need scores for any other specific solutions previously discussed.

user_input: yes please score the others too

ai_msg: Here are additional feasibility scores for other solutions discussed:

AI Ethics Review Board: Technical: 4 (AI auditing tools available) Organizational: 3 (may face resistance) Legal: 5 (reinforces compliance)

Online training partnerships: Technical: 5 (mature platforms exist) Organizational: 4 (change management needs) Legal: 4 (contracting complexity)

Reskilling incentives: Technical: 5 (straightforward to implement) Organizational: 3 (budget impact) Legal: 5 (no constraints)

Alumni talent networks: Technical: 4 (CRM solutions exist) Organizational: 3 (engagement requires effort) Legal: 4 (data privacy considerations)

Predictive workforce analytics: Technical: 4 (vendors exist) Organizational: 3 (data strategy needs) Legal: 4 (informed consent important)

Let me know if you need any clarification or have additional solutions you want scored on feasibility.

user_input: proceed to the next step

ai_msg: Score previous orthogonal solutions from the Solutions phase based on novelty. Do so on a scale of 0-5, with 0 being the worst, and 5 the best.

ai_msg: Here are novelty scores for the previous workforce solutions:

Talent exchange platform: 4 (novel AI-based skills matching)

Mandatory future-skilling: 3 (proactive but not new concept)

Human creativity metrics: 5 (paradigm shift from productivity)

Internal talent marketplace: 2 (evolution of existing practices)

Training sabbaticals: 2 (enhanced version of sabbaticals)

AI ethics review board: 3 (emerging corporate role)

Online training partnerships: 1 (mature concept)

Reskilling incentives: 1 (common practice)

Alumni talent networks: 2 (building on alumni databases)

Predictive workforce analytics: 3 (maturing technology application)

The most novel solutions focus on new human-centered metrics, leveraging AI for skills analysis, and creating transitional support mechanisms like talent exchanges. Implementing these would require shifting from traditional performance mindsets.

user_input: proceed to the next step

ai_msg: Score prevoius orthogonal solutions from the Solutions phase on positive impact (financial, social, environmental). Do so on a scale of 0-5, with 0 being the worst, and 5 the best.

ai_msg: Here are impact scores for the previous workforce solutions:

Talent exchange platform: Financial: 4 (reduces severance costs) Social: 5 (provides transitional support) Environmental: 3 (enables workforce flexibility)

Mandatory future-skilling: Financial: 3 (training costs but avoids skills gaps) Social: 4 (builds employee resilience) Environmental: 3 (enables workforce flexibility)

Human creativity metrics: Financial: 3 (productivity impact but benefits hard to quantify) Social: 5 (affirms human value) Environmental: 3 (enables workforce flexibility)

Internal talent marketplace: Financial: 4 (reduces severance and hiring costs) Social: 4 (promotes internal mobility) Environmental: 3 (enables workforce flexibility)

Training sabbaticals: Financial: 2 (production impact during sabbatical) Social: 4 (supports continuous learning) Environmental: 3 (enables workforce flexibility)

The solutions with the greatest positive impact balance financial benefits with social/environmental value. Let me know if you need impact scores for any other solutions.

user_input: yes score the others too

ai_msg: Here are impact scores for additional solutions:

AI Ethics Review Board: Financial: 3 (oversight costs but avoids risks) Social: 4 (builds responsible AI culture) Environmental: 3 (enables workforce flexibility)

Online training partnerships: Financial: 4 (leverages external capabilities) Social: 4 (expands access to learning) Environmental: 3 (enables workforce flexibility)

Reskilling incentives: Financial: 3 (rewards costs but motivates workers) Social: 4 (promotes skill development) Environmental: 2 (limited flexibility impact)

Alumni talent networks: Financial: 3 (engagement costs but aids rehiring) Social: 3 (builds community) Environmental: 2 (limited flexibility impact)

Predictive workforce analytics: Financial: 4 (data-driven planning efficiency) Social: 3 (targeted development) Environmental: 2 (limited flexibility impact)

Let me know if you need any clarification or have additional solutions to be scored.

Answer number: 28

user_input: proceed to the next step

ai_msg: Now summarize in one table the score of all ideas you scored, based on all of the criteria. Add a total score column, that is calculated as an average of the criteria.

ai_msg: Here is a summary table of the scores across all criteria for the workforce solutions:

Based on the total scores, the top ranked solutions are:

1. Talent exchange platforms
2. Human creativity metrics
3. Internal talent marketplaces
4. AI ethics review boards

Let me know if you need me to clarify or expand on this summary.

user_input: Now take the analogies you generated and tell me if some of these solutions can be changed or complemented by others, inspired by those analogies

ai_msg: Great point. Let me re-examine some solutions in light of the historical analogies:

• For the transition from agriculture to industry, government programs helped train and support displaced farmers. Similarly, centralized programs could help subsidize and coordinate reskilling.
• Electrification required evolution of workplace infrastructure alongside skilling. For AI adoption, investments in enabling technologies like data platforms and digital tools can complement reskilling.
• Introducing computers changed job designs and activities. With AI, intentionally redesigning processes around human-AI collaboration is critical, not just reskilling.
• For manufacturing automation, worker collectives were able to pool resources to start small businesses. Similarly, providing funding and support for displaced employees to pursue entrepreneurial ventures could be impactful.
• As retail evolved, customer-facing workers needed new digital and soft skills. For AI transitions, interpersonal skills like creativity, empathy, communication will be even more essential to reskill.

The analogies highlight the importance of holistic systemic support, job redesign, and focusing on uniquely human strengths in managing workforce disruption. Let me know if you need any clarification or have additional insights based on the analogies.

2/2. Using generative AI for indirect procurement processes

ai_msg: Define problem in more detail

ai_msg: Answer number: 1 Answer: Here are some ways to define the problem of improving indirect procurement processes using generative AI in more detail:

• Indirect procurement refers to the purchase of goods and services that are not directly related to making a product, such as office supplies, software, travel, and professional services. The processes for procuring these items are often manual, inefficient, and opaque.
• There is an opportunity to leverage generative AI technology like large language models to automate and optimize various indirect procurement workflows. This includes needs assessment, vendor discovery, bid solicitation, contract negotiation, vendor management, and more.
• Current processes rely heavily on procurement specialists navigating siloed systems and executing repetitive administrative tasks. This results in slow turnaround times, limited vendor options, and higher costs.
• Stakeholders involved include procurement team members, requestors from other business units, technology teams, vendors, legal/compliance, and finance. Each has different needs and pain points.
• Goals are to improve speed, lower costs, increase transparency, expand vendor options, enforce compliance, and free up staff for more strategic work. Risks include change resistance, integration complexity, and proper AI governance.

user_input: These days it is also important to use procurement better for environmental sustainability

ai_msg: Answer number: 2 Answer: Here are some additional ways to define the problem of improving indirect procurement processes using generative AI:

• Current procurement processes do not adequately factor in environmental sustainability considerations. There are missed opportunities to reduce waste, energy use, and carbon emissions through better vendor selection, contract terms, and purchasing choices.
• Generative AI could help assess the environmental impact of different purchasing options and provide sustainability scores to guide procurement decisions.
• It could also help draft procurement contracts that include sustainability requirements for vendors around materials, packaging, shipping, and more.
• Workflow automation could free up procurement staff time to focus more on strategic sustainability initiatives like setting goals, tracking progress, partnering with vendors, and reporting impacts.
• Risks include availability of vendor sustainability data, reluctance from vendors, and increased costs of sustainable options. Benefits include environmental impact reduction, meeting corporate sustainability goals, and long-term cost savings.

user_input: proceed to the next step

ai_msg: Break down the problem into its individual components

ai_msg: Answer number: 3 Answer: Here is a breakdown of the key components of the problem of improving indirect procurement processes with generative AI:

• Needs Assessment - Manual and ad-hoc processes for internal teams to submit purchasing requests. Lack of visibility into spending trends.
• Vendor Discovery - Limited awareness of potential vendors for indirect goods/services. Manual research and outreach.
• Bid Solicitation - Inefficient and fragmented RFP/RFQ process. Hard to compare vendors on full range of criteria.
• Contract Negotiation - Slow back-and-forth discussions over terms, pricing, SLAs. Reliance on templates.
• Vendor Management - Manual tracking of performance, spend, contract renewals. Weak compliance oversight.
• Invoice Processing - Manual validation and reconciliation of invoices. Higher risk of errors and fraud.
• Spend Analysis - Limited ability to categorize, optimize, and forecast indirect spend.
• Compliance/Risk - Gaps in processes to enforce internal policies and external regulations.
• Reporting/Analytics - Static reporting tied to backward-looking metrics. Lack of self-service.
• Stakeholder Experience - Frustration with lack of transparency, speed, options, and support.

user_input: proceed to the next step

ai_msg: Identify analogies to this problem

ai_msg: Answer number: 4 Answer: Here are some potential analogies for the problem of improving indirect procurement with generative AI:

• Online shopping - Ecommerce platforms like Amazon have recommendation engines, personalized promotions, one-click ordering, and fast delivery. Procurement could leverage AI similarly.
• Smart assistants - Tools like Siri, Alexa and Google Assistant handle information lookup, formulate responses, and complete tasks automatically. This is analogous to AI-powered procurement chatbots.
• Robo-advisors - Digital platforms like Wealthfront and Betterment provide automated financial planning and portfolio management. Similar AI could advise on procurement decisions.
• Supply chain automation - Technologies for forecasting, inventory management, and logistics in manufacturing are relevant to indirect procurement workflows.
• Legal document automation - Companies like Kira Systems use AI to analyze, construct, and review contracts. This can be applied to procurement agreements.
• Automated expense reporting - Apps like Expensify use OCR and smart rules to simplify submitting and approving expenses. This can extend to other indirect spend.
• Recommender systems - Ecommerce platforms analyze past purchases and browsing to suggest relevant products. Similar systems could recommend procurement vendors.

user_input: proceed to the next step

ai_msg: Zoom out to identify the broader category of problems related to it

ai_msg: Answer number: 5 Answer: Zooming out, this problem belongs to the broader category of using AI to improve business processes and workflows, specifically by:

• Automating manual, repetitive tasks - AI excels at high volume, rules-based work allowing people to focus on more strategic efforts. This applies across business processes.
• Extracting insights from data - AI can identify patterns and make recommendations from large, complex data. This can optimize ordering, forecasting, targeting, and more.
• Generating documents and communications - AI can create drafts, reports, emails, notices, contracts based on templates and examples. This reduces human effort.
• Making predictions and recommendations - AI can forecast outcomes, suggest actions, identify risks, and recommend products or services relevant to a specific context.
• Understanding unstructured data - AI techniques like NLP allow extracting information from text, images, audio and video. This can ingest a wide range of inputs.
• Orchestrating workflows - AI can sequence multi-step processes, assign work, validate information, and integrate systems.
• Providing expert-level support - AI advisors and chatbots can provide guidance, answer questions, and fill knowledge gaps when human experts are unavailable.
• Optimizing decisions - AI can rapidly analyze options and tradeoffs to recommend optimal business decisions according to specified goals or constraints.

user_input: E2Eanalysis

ai_msg: Answer number: 6 Answer: Looking at the problem end-to-end, some additional broader categories related to using AI to improve indirect procurement include:

• E2E analysis - AI can help map, optimize, and integrate disconnected procurement processes into a seamless end-to-end workflow. This includes linking stakeholder requests, vendor/product selection, contracting, ordering, delivery, payment, and support.
• Data integration - AI can ingest, normalize, and connect data from disparate systems across an organization to enable a unified view of procurement spend, performance, risks, and insights.
• Audit and compliance - AI can continuously monitor procurement processes and unstructured data like contracts and communications to ensure adherence to policies, regulations, and business rules.
• Supplier relationship management - AI can optimize how buyers interact with and manage vendors through the full lifecycle, from discovery to qualification, onboarding, order and issue management, and performance reviews.
• Procurement as a profit center - Applying AI to maximize procurement efficiency, savings, and strategic value generation rather than purely a cost center.
• Total Cost of Ownership (TOC) - AI can help shift focus from upfront purchase price to optimizing TOC including acquisition, operation, maintenance, quality, sustainability, and disposal costs.

user_input: To understand the problem better, analyze it with Cause-and-Effect Diagram (Fishbone or Ishikawa Diagram)

ai_msg: Answer number: 7 Answer: Here is an analysis of the causes of inefficient indirect procurement processes using a fishbone diagram:

• People - Lack of training, resistance to change, siloed teams, lack of analytics skills, insufficient staffing
• Methods - Manual workflows, fragmented systems, inconsistent processes, lack of standards, weak compliance controls
• Machines - Outdated tech stacks, lack of automation, data in separate systems, poor integration, legacy platforms
• Materials - Unstructured data like contracts and emails, inaccurate supplier master data, limited product information
• Management - Lack of executive support, misaligned incentives, lack of clear metrics, insufficient policies and governance
• Environment - Complex regulatory landscape, lack of supplier diversity, geographically dispersed teams, external economic factors

The main effects are higher costs, longer cycle times, off-contract spending, supply chain risks, and poor user experience. Addressing the root causes with AI can improve procurement performance, efficiency, and strategic impact.

user_input: To understand the problem better, analyze it with Lean Management RCA

ai_msg: Answer number: 8 Answer: Here is an analysis of the root causes of inefficient indirect procurement processes using Lean Management's 5 Whys:

1. Why are procurement cycle times long? Because processes are manual and fragmented across systems.
2. Why are processes manual and fragmented? Because legacy systems lack integration and automation.
3. Why do systems lack integration and automation? Because of insufficient investment and prioritization.
4. Why has this not been prioritized? Because the benefits are unclear to leadership.
5. Why are benefits unclear? Because procurement metrics focus on cost rather than strategic value.

This suggests the root causes may include lack of executive buy-in, lack of budget/resources for tech modernization, and procurement metrics that incentivize short-term cost savings over efficiency, innovation, and strategic impact. Applying AI can help quantify benefits, demonstrate value, and build a case for transformative technology investments.

user_input: proceed to the next step

ai_msg: Identify root causes of the problem (5 Why's)

ai_msg: Answer number: 9 Answer: Here is an analysis of the root causes of inefficient indirect procurement processes using the 5 Whys method:

1. Why is the procurement process inefficient? Because it relies heavily on manual work and legacy systems.
2. Why is work manual and systems outdated? Because there has not been investment in automation and new technologies.
3. Why has there been lack of investment? Because procurement is seen as a cost center, not a strategic function.
4. Why is it viewed as just a cost center? Because metrics focus on short-term cost savings rather than long-term value.
5. Why do metrics misalign? Because the incentives and KPIs for procurement teams are not tied to efficiency, innovation, or stakeholder satisfaction.

The root causes seem to stem from procurement being viewed as a transactional function rather than a strategic one. Applying AI can help transform metrics, incentives, and ultimately perceptions around the strategic value procurement can bring. But adoption requires executive buy-in, budget, and a culture shift.

user_input: proceed to the next step

ai_msg: Identify problems upstream and downstream from this problem, looking at the problem end to end

ai_msg: Answer number: 10 Answer: Looking at indirect procurement end-to-end, some key upstream and downstream problems are:

Upstream: - Unclear spend visibility - Lack of spend categorization, reporting, and analytics on indirect spend makes it hard to identify savings opportunities. - Ad-hoc purchasing - Business units make one-off purchases without going through procurement, leading to maverick spend. - Poor planning - Indirect purchasing needs are not consolidated or forecasted, resulting in reactive vs strategic procurement.

Downstream: - Invoice processing delays - Manual invoice validation and reconciliation creates backlogs and payment delays. - Non-compliant purchases - Decentralized purchasing increases risks of policy violations after the fact. - Contract management - Expired or suboptimal contracts are not proactively identified for renegotiation. - Vendor performance issues - Poor vendor management allows performance problems to go undetected until they become crises. - Missed savings - No optimization of purchasing choices, vendors, or contracts once they are in place.

Applying AI to these upstream and downstream steps along with core procurement workflows can drive greater efficiency, compliance, and cost optimization end-to-end.

user_input: proceed to the next step

ai_msg: Identify the personas involved in the problem, including their motivations and characteristics.

ai_msg: Answer number: 11

Answer: Here are some key personas involved in indirect procurement processes and their potential motivations:

• Procurement managers - Want to maximize cost savings and efficiency to meet targets; concerned about workload and headcount.
• Business unit requesters - Want to obtain goods/services quickly and easily; view procurement as red tape.
• Finance team - Wants spending visibility, policy compliance, and budgetary control.
• Legal/compliance - Wants to minimize contractual risks and enforce regulations.
• IT/engineering - Wants technical compatibility, security, and integration with existing systems.
• Suppliers/vendors - Want to maintain existing sales and gain new business; need quick payments.
• Senior executives - Want strategic value from procurement to improve margins and gain competitive advantage.

Their main characteristics are being overworked, risk averse, incentivized by cost savings, and protective of their domain expertise - which can create resistance to change. AI can help evolve their roles and address pain points through automation.

user_input: proceed to the next step

ai_msg: Summarize orthogonal (non overlapping) concepts explored in the Explore problem phase Explore solutions phase:**

Explore solutions phase**:

**

Refine solutions phase**:

ai_msg: Answer number: 12

Answer: Here is a summary of the key non-overlapping concepts covered in the Explore Problem phase:

• Defined indirect procurement and challenges with manual, inefficient processes
• Broke down into components like vendor discovery, contracting, ordering, invoicing
• Analogized to AI use cases in ecommerce, finance, legal, manufacturing
• Framed as an opportunity to improve business processes with AI
• Identified root causes like lack of investment, strategic focus using 5 Whys
• Mapped upstream and downstream steps from spend visibility through vendor management
• Outlined personas like procurement managers, requesters, finance, legal, executives
• Looked at motivations and characteristics like workload, risk aversion, cost focus

This exploration indicates major pain points in indirect procurement workflows, misaligned incentives and metrics, and significant potential for AI to automate, provide insights, and transform decision-making. The next phase will focus on generating solutions tailored to these problem dynamics.

user_input: proceed to the next step

ai_msg: Keeping into account the previous ideas, provide a list of novel solutions. Novel solutions are those that are not common today. They may be based on recombinations of ideas currently existing, or could be totally new.

ai_msg: Answer number: 13

Answer: Here are some potential novel solutions for improving indirect procurement with AI:

• A conversational AI assistant that can have natural language dialogs to help users through the end-to-end procurement process.
• Generative AI to automatically generate RFPs and contracts tailored to each unique procurement need while ensuring policy compliance.
• An AI-based procurement planning and forecasting system that analyzes past spend, market conditions, and business needs to predict and optimize future indirect spend.
• Intelligent workflows coordinated by AI to guide complex procurement tasks involving multiple stakeholders and systems through to completion.
• An AI-powered supplier portal for automated vendor onboarding, two-way communication, and performance analytics.
• Reinforcement learning algorithms to optimize dynamic procurement decisions like contract terms, order quantities, inventory levels, and cash flow.
• An AI review system for automated invoice validation, reconciliation, and exception detection to speed up processing.
• AI bots trained on company policies and regulations to provide real-time, context-specific guidance to procurement staff as they navigate workflows.

user_input: proceed to the next step

ai_msg: Based on the previous analysis, provide a list of relatively easy to implement solutions. These are solutions that can be readily implemented today. They may be based on recombinations of ideas currently existing.

ai_msg: Answer number: 14

Answer: Here are some relatively easy to implement AI solutions for improving indirect procurement based on existing capabilities:

• Chatbots to handle supplier and internal user inquiries, provide guidance, and collect key data to streamline repetitive interactions.
• Automated spend categorization and reporting using NLP and classification algorithms to gain visibility into indirect spend.
• AI-based market intelligence to identify new potential suppliers and monitor pricing trends to inform negotiations.
• Automated contract abstraction to extract key terms and obligations from contracts and highlight risks.
• Anomaly detection on invoice streams and expense reports to identify potential errors, fraud, or policy violations for review.
• Virtual assistants for internal requesters to guide them through procurement policies and place orders through natural conversations.
• Predictive analytics on past indirect spend to forecast future budget needs and identify savings opportunities.
• Intelligent workflows and smart approvals to route orders and invoices based on policies and ensure compliance.
• Chatbots to assist with vendor onboarding by answering questions and collecting required documentation.

user_input: proceed to the next step

ai_msg: Now build on this idea, by identifying some relevant technologies that could strengthen those solutions. Be specific and give examples.

ai_msg: Answer number: 15

Answer: Here are some examples of relevant technologies that could strengthen the AI solutions for indirect procurement:

• Natural language processing (NLP) to allow conversational AI assistants and chatbots to understand context and intent in procurement dialogs.
• Robotic process automation (RPA) to rapidly automate repetitive tasks like data entry in procurement workflows.
• Graph databases to map relationships between suppliers, products, contracts, invoices, etc. and enable smart recommendations.
• Machine learning algorithms like regression and clustering for spend forecasting, cost modeling, and anomaly detection.
• Knowledge graphs to encode procurement policies, regulations, and domain knowledge for AI policy compliance.
• Smart contracts to automate contract execution, payments, renewals, and enforce SLAs.
• Cloud APIs to integrate AI tools with existing procurement systems like ERPs, CRMs, and e-procurement platforms.
• Blockchain to enable supplier discovery, track orders, and validate provenance of goods.
• Digital assistants like Alexa or Siri to provide voice-enabled support for procurement tasks.
• Robotic desktop automation for automating repetitive desktop tasks and extracting data from GUIs.

user_input: proceed to the next step

ai_msg: Summarize orthogonal (non overlapping) concepts explored in the Explore solutions phase Refine solutions phase:**

Conclusions**:

ai_msg: Answer number: 16

Answer: Here is a summary of the key non-overlapping concepts covered in the Explore Solutions phase:

• Proposed novel AI solutions like conversational assistants, generative contracting, predictive planning, intelligent workflows, supplier portals, and invoice automation.
• Suggested more readily implementable solutions like chatbots, spend analytics, market intelligence, contract abstraction, virtual assistants, and predictive analytics.
• Provided examples of enabling technologies such as NLP, RPA, graph databases, machine learning, knowledge graphs, smart contracts, cloud APIs, blockchain, digital assistants.
• Focused solutions on automation for efficiency, data-driven insights for optimization, and intelligent decision support.
• Tailored solutions to pain points around manual work, lack of visibility, siloed systems, compliance risks, reactive purchasing, and vendor management.
• Emphasized easy-to-implement solutions with quick wins as well as more cutting edge innovations.
• Maintained strategic focus on using AI to evolve procurement from cost center to value creator.

This phase generated a targeted set of AI solutions mapped to specific procurement process challenges and personas. The next phase will further refine these solutions.

user_input: To understand the solution better, analyze it with Six Sigma FMEA

ai_msg: Answer number: 17

Answer: Here is an analysis of the AI solutions for indirect procurement using a Six Sigma FMEA (Failure Modes and Effects Analysis) approach:

• Conversational AI assistant - Failure modes: inaccurate responses, integration issues, lack of context. Effects: frustration, stalled processes, compliance risks.
• Generative contracting - Failure modes: erroneous terms, regulatory non-compliance, edge cases. Effects: legal issues, contract disputes, rework.
• Predictive procurement planning - Failure modes: bad data, incorrect assumptions, bias. Effects: suboptimal budgets, missed savings, stockouts.
• Intelligent workflows - Failure modes: system downtime, incorrect routing, security breaches. Effects: approval delays, policy violations, audit issues.
• Supplier portal - Failure modes: onboarding errors, poor adoption, lack of support. Effects: missing suppliers, manual workarounds, portal abandonment.
• Invoice automation - Failure modes: payment errors, fraud missed, exceptions mishandled. Effects: financial loss, audit exposure, vendor dissatisfaction.

Analyzing failure modes upfront allows identifying critical control points, guide design, target testing, and develop mitigations early. This results in more robust AI solutions for procurement.

user_input: proceed to the next step

ai_msg: Score prevoius orthogonal solutions from the Solutions phase based on technical, organizational, legal feasibility. Do so on a scale of 0-5, with 0 being the worst, and 5 the best.

ai_msg: Answer number: 18

Answer: Here are feasibility scores for the AI solutions for indirect procurement:

Conversational AI assistant: - Technical feasibility: 4 (maturing NLP makes this achievable) - Organizational feasibility: 3 (change management needs work) - Legal feasibility: 5 (no major legal barriers)

Generative contracting: - Technical feasibility: 3 (generative AI still maturing for contracts) - Organizational feasibility: 4 (aligns to goals) - Legal feasibility: 2 (regulatory unknowns, liability concerns)

Predictive procurement planning: - Technical feasibility: 5 (proven algorithms and models) - Organizational feasibility: 4 (needs data governance) - Legal feasibility: 5 (no issues)

Intelligent workflows: - Technical feasibility: 4 (config could be complex) - Organizational feasibility: 3 (adoption challenges) - Legal feasibility: 4 (needs security vetting)

Supplier portal: - Technical feasibility: 5 (mature technologies) - Organizational feasibility: 3 (supplier engagement) - Legal feasibility: 4 (contracting, security)

Invoice automation: - Technical feasibility: 5 (RPA and OCR mature) - Organizational feasibility: 5 (quick win) - Legal feasibility: 4 (financial controls needed)

user_input: proceed to the next step

ai_msg: Score previous orthogonal solutions from the Solutions phase based on novelty. Do so on a scale of 0-5, with 0 being the worst, and 5 the best.

ai_msg: Answer number: 19

Answer: Here are novelty scores for the AI solutions for indirect procurement:

Conversational AI assistant: 5 (Very novel application for procurement)

Generative contracting: 5 (Cutting-edge generative AI use case)

Predictive procurement planning: 3 (Evolution of existing analytics)

Intelligent workflows: 4 (Innovative integration of AI and RPA)

Supplier portal: 2 (Portal innovation is incremental)

Invoice automation: 1 (Automates existing processes)