AI Hype vs Reality: A Framework-Based Guide for Actionable Predictions

Using frameworks to structure thinking is especially valuable when addressing novel situations. With so much disruption being caused by Artificial Intelligence (AI), it is a perfect time to embrace this approach. Using 10 different frameworks, each individually combined with the 2023 Gartner AI Hype Cycle, this analysis explores predictions for the coming 12-18 months specifically to give you an actionable guide. While predicting the future is never foolproof, using frameworks piques creativity and strategically minimizes the risks associated with our decisions.

To use this guide for your environment, select 1-3 frameworks that align with your situation from the table below. Then, scroll to find those frameworks. Finally, see the identified predictions and consider the accompanying actions.

Gartner Hype Cycle

This was conceptualized by Gartner analysts in the 1990s to help tech companies navigate emerging trends. They noticed technologies often follow predictable adoption patterns. The Hype Cycle visualization helps decision-makers understand what stage disruptive technologies are at to make more informed investment choices.

Predictions

• Innovation Trigger, AI in Edge Computing: Expect a surge in pilot projects aimed at integrating AI capabilities into edge devices.
• Peak of Inflated Expectations, Conversational AI Platforms: High investment in voice and chat solutions for customer service but with mixed results.
• Trough of Disillusionment, Autonomous Vehicles: Slower-than-expected progress due to regulatory and safety concerns.
• Slope of Enlightenment, Natural Language Processing (NLP): Widespread adoption in various sectors like healthcare, finance, and customer service.
• Plateau of Productivity, Predictive Analysis: Mature and stable, expect to see this technology as a standard feature in business analytics tools.

Accompanying Actions

AI in Edge Computing:

• Invest in pilot projects and evaluate their ROI and scalability.
• Evaluate the security implications of deploying AI at the edge.

Conversational AI Platforms:

• Audit current voice and chat solutions for ROI.
• Conduct A/B tests to measure customer satisfaction and ROI.

Autonomous Vehicles:

• Keep an eye on regulatory changes and adjust your strategy accordingly.
• Explore partnerships with companies focused on safety technologies.

Natural Language Processing (NLP):

• Integrate NLP into customer service channels.
• Evaluate NLP applications in internal communications and business question answering.

Predictive Analysis:

• Make predictive analytics a standard feature in business tools.
• Train teams on interpreting predictive analytics for decision-making.

Lean Startup

In the late 2000s, Eric Ries worked with startups like IMVU and observed they wasted valuable resources on products customers did not want. This led to the Lean Startup methodology centered around rapid customer feedback loops. The Build-Measure-Learn model encourages fast, cost-effective experimentation to reduce risk and guide product-market fit.

Predictions

• AI Ethics and Fairness: Companies that prioritize ethical AI will see a brand boost.
• Conversational AI: With the rise of ChatGPT and similar technologies, expect a surge in customer service automation that actually feels human.
• AI in Healthcare: Expect more AI-driven diagnostic and treatment options to become mainstream.
• AI in Supply Chain: AI in supply chain management will see significant investment and growth.
• AI in Marketing: Personalization engines will become more sophisticated, offering unprecedented levels of customization.

Accompanying Actions

AI Ethics and Fairness:

• Conduct an ethics audit on your AI models and use of AI tools.
• Develop a public-facing ethics policy to boost brand trust.

Conversational AI:

• Implement ChatGPT or similar technologies for customer service.
• Monitor and analyze customer interactions for continuous improvement.

AI in Healthcare:

• Invest in AI-driven diagnostic tools.
• Partner with healthcare providers for pilot testing.

AI in Supply Chain:

• Identify key supply chain processes that can be optimized with AI.
• Evaluate the ROI of AI implementations in supply chain management.

AI in Marketing:

• Integrate AI-driven personalization engines into your marketing stack.
• Use AI to segment customers for targeted marketing campaigns.

Design Thinking

In the 1980s, designers at IDEO pioneered a human-centered approach that relied on understanding user needs to create innovative solutions. As companies increasingly focused on customer experience, design thinking became popular for its empathic, collaborative process bringing together diverse perspectives to solve complex problems.

Predictions

• AI for Mental Health: Expect apps that offer mental health support through AI to gain traction.
• Voice Assistants: Voice-activated technologies will become ubiquitous in smart homes and offices.
• AI in Education: Personalized learning platforms will become more mainstream.
• Natural Language Processing (NLP): Expect a surge in applications that can understand and generate human-like text.
• AI in Cybersecurity: With the increasing number of cyber threats, AI-driven security solutions are a necessity.

Accompanying Actions

AI for Mental Health:

• Explore partnerships with mental health organizations.
• Conduct user experience research to refine AI-driven mental health apps.

Voice Assistants:

• Implement voice-activated technologies in smart homes and offices.
• Monitor user interactions to refine voice command recognition.

AI in Education:

• Develop or invest in personalized learning platforms.
• Partner with educational institutions for pilot programs.

Natural Language Processing (NLP):

• Integrate NLP into customer service channels.
• Use NLP for sentiment analysis in customer feedback.

AI in Cybersecurity:

• Implement AI-driven security solutions.
• Regularly update AI models to adapt to new cyber threats.

Jobs to be Done

Tony Ulwick's consulting firm in the 1990s found most firms focused on features, not jobs customers hire products to do. His framework shifts attention to the outcomes people seek when making purchase decisions, creating a deeper understanding that drives better products addressing real customer needs.

Predictions

• AI in Customer Service: Chatbots and virtual assistants will become the go-to solution for customer support "jobs."
• Predictive Analytics: Expect businesses to apply predictive analytics for various "jobs," from marketing to inventory management.
• AI in Content Creation: The "job" of content creation will see a significant shift towards AI-generated content.
• AI in Financial Services: Fraud detection and algorithmic trading will become more sophisticated and reliable.
• AI in Healthcare: Telehealth and remote monitoring will become more prevalent "jobs" that AI can fulfill effectively.

Accompanying Actions

AI in Customer Service:

• Integrate chatbots with your CRM system to provide personalized customer support.
• Conduct A/B tests to compare effectiveness of AI-driven vs. traditional support.

Predictive Analytics:

• Use predictive analytics to forecast customer behavior and personalize marketing.
• Implement predictive analytics in inventory management to optimize stock levels.

AI in Content Creation:

• Use AI-generated content for initial drafts and have human editors refine it.
• Analyze performance of AI-generated content versus human-created content.

AI in Financial Services:

• Integrate AI into fraud detection systems to identify unusual patterns.
• Use AI to back-test algorithmic trading strategies on historical data.

AI in Healthcare:

• Implement AI-driven telehealth solutions that can triage patients.
• Use AI for remote monitoring of patient vitals and send alerts for anomalies.

Systems Thinking

Biologist Ludwig von Bertalanffy introduced general systems theory in the 1960s noting parallels across scientific fields. Donella Meadows at MIT popularized systems thinking in the 1970s observing cross-cutting dynamics within complex systems. This non-linear approach sees the interrelations rather than things in isolation for managing adaptive challenges.

Predictions

• AI in Sustainability: Expect a systemic shift towards greener business practices.
• AI in Data Privacy: The "job" of data protection will become more automated and effective.
• AI in Supply Chain: Expect a systemic improvement in supply chain efficiency.
• AI in Talent Management: Technologies for HR and talent management will create new feedback loops for employee engagement and productivity.
• AI in Customer Experience: Personalization engines will become more integrated into the customer journey, affecting multiple touchpoints systemically.

Accompanying Actions

AI in Sustainability:

• Conduct an energy audit and identify areas where AI can optimize energy consumption.
• Partner with AI startups focused on sustainability to pilot new green technologies.

AI in Data Privacy:

• Implement AI-driven compliance tools that automatically update data protection policies.
• Use AI to monitor real-time data transactions and flag unauthorized access.

AI in Supply Chain:

• Integrate AI algorithms into your inventory management system.
• Use AI to analyze supplier performance and risks.

AI in Talent Management:

• Utilize AI-driven analytics tools to measure employee engagement and productivity.
• Implement AI-powered chatbots for internal HR queries.

AI in Customer Experience:

• Use AI to analyze customer behavior and preferences for real-time personalization.
• Implement AI-driven recommendation engines on your website and app.

Scenario Planning

In the 1970s, Royal Dutch Shell faced huge uncertainties around oil prices. They turned to scenario planning, developing multiple plausible futures to strategize different scenarios. This helped them anticipate surprises, remain flexible, and even spot opportunities that competitors missed.

Predictions

• AI in Regulatory Compliance: Expect a rise in AI-powered compliance tools, especially as regulations tighten.
• AI in Mental Health: AI-driven mental health platforms will gain mainstream acceptance.
• Quantum Computing: Breakthroughs in quantum computing will disrupt multiple industries.
• AI in Agriculture: AI-driven sustainable farming techniques could either become essential or face regulatory hurdles.
• AI in Autonomous Vehicles: Depending on legislation and public acceptance, self-driving cars could either become common or remain experimental.

Accompanying Actions

AI in Regulatory Compliance:

• Invest in AI-powered compliance tools that adapt to changing regulations.
• Train your compliance team to work alongside AI tools.

AI in Mental Health:

• Partner with AI-driven mental health platforms to offer employee well-being programs.
• Conduct pilot studies to measure effectiveness of AI-driven interventions.

Quantum Computing:

• Keep an eye on emerging quantum computing technologies and consider early investment.
• Assess the potential impact of quantum computing on your industry.

AI in Agriculture:

• Experiment with AI-driven sustainable farming techniques on a smaller scale.
• Stay informed about regulatory changes that could affect AI in agriculture.

AI in Autonomous Vehicles:

• If in the automotive or related industries, consider investing in R&D for autonomous vehicles.
• Keep a pulse on public sentiment and legislative changes.

Objectives and Key Results (OKR)

Intel CEO Andy Grove developed Objectives and Key Results in the 1970s to align teams, drive accountability, and measure progress. John Doerr brought OKRs to Google in 1999, helping it scale rapidly. Today it is widely adopted by growth-focused companies balancing goals with flexibility.

Predictions

• AI in Cybersecurity: Expect OKRs focused on enhancing security measures to be a top priority.
• AI in Customer Service: OKRs around customer satisfaction metrics could be impactful.
• AI in Content Creation: OKRs related to content marketing and engagement are likely to gain traction.
• AI in Supply Chain Optimization: OKRs around reducing waste and improving efficiency could be key.
• AI in Healthcare: OKRs focusing on patient outcomes will be more prevalent.

Accompanying Actions

AI in Cybersecurity:

• Set OKRs to measure effectiveness of AI-driven security solutions.
• Include cybersecurity training for employees as part of your OKRs.

AI in Customer Service:

• Establish OKRs that aim to improve customer satisfaction scores through AI.
• Set OKRs to track efficiency gains from AI in customer service.

AI in Content Creation:

• Create OKRs that focus on increasing user engagement with AI-generated content.
• Set OKRs to measure the ROI of AI-driven content marketing campaigns.

AI in Supply Chain Optimization:

• Implement OKRs that target reducing waste through AI optimization.
• Set OKRs to improve supplier reliability and reduce stockouts.

AI in Healthcare:

• Establish OKRs that focus on improving patient outcomes through AI-driven diagnostics.
• Set OKRs to track efficiency of AI tools in healthcare delivery.

Theory of Inventive Problem Solving (TRIZ)

Soviet inventor Genrich Altshuller studied thousands of patents from 1945 to compile patterns of innovation. He developed the TRIZ methodology codifying the principles that repeatedly solved technological contradictions, inspiring systematic creativity. This approach is popular in R&D.

Predictions

• AI in Data Privacy: TRIZ could help in inventing solutions that both secure data and maintain user accessibility.
• AI in Renewable Energy: TRIZ principles could guide the development of AI algorithms that optimize energy consumption.
• AI in Education: TRIZ could help in resolving the contradictions between personalized learning and scalable solutions.
• AI in Manufacturing: TRIZ could offer inventive solutions for automating complex tasks without sacrificing quality.
• AI in Mental Health: TRIZ could help in creating AI tools that are both effective and ethically sound.

Accompanying Actions

AI in Data Privacy:

• Use TRIZ to identify contradictions in current data privacy solutions.
• Apply TRIZ principles to brainstorm new encryption methods.

AI in Renewable Energy:

• Utilize TRIZ to identify inefficiencies in current renewable energy systems.
• Apply TRIZ to explore inventive solutions for energy storage.

AI in Education:

• Use TRIZ to resolve the contradiction between personalized learning and scalability.
• Apply TRIZ principles to innovate ways to keep remote learners engaged.

AI in Manufacturing:

• Utilize TRIZ to identify bottlenecks in manufacturing processes.
• Use TRIZ to brainstorm how AI can improve supply chain transparency.

AI in Mental Health:

• Apply TRIZ to identify ethical concerns in AI-driven mental health solutions.
• Use TRIZ to brainstorm AI tools that provide effective, culturally sensitive support.

Agile

17 software experts met in Utah in 2001, concerned over inflexible, documentation-heavy processes. They crafted the Agile Manifesto valuing individual interactions, working software, and responding to change, evolving iterative delivery best exemplified by Scrum. Agile transformed programming with its people-centric, adaptable practices.

Predictions

• AI in Natural Language Processing: Agile teams could focus on sprints to improve customer service chatbots.
• AI in Predictive Analytics: Agile sprints could focus on integrating predictive analytics into business processes.
• AI in Automation: Agile methodologies could help in quick iterations and deployments.
• AI in Personalization: Agile sprints could be geared towards enhancing customer experiences.
• AI in Healthcare Monitoring: Agile will aid in the rapid development and iteration of monitoring tools.

Accompanying Actions

AI in Natural Language Processing:

• Use Agile sprints to iteratively improve chatbot conversational abilities.
• Dedicate a sprint to integrate real-time feedback mechanisms for chatbots.

AI in Predictive Analytics:

• Focus an Agile sprint on integrating predictive analytics into one key business process.
• Use a sprint to create dashboards that visualize predictive analytics data.

AI in Automation:

• Implement Agile sprints to quickly develop and test robotic process automation.
• Use Agile to iterate on automation tool user interfaces.

AI in Personalization:

• Dedicate Agile sprints to enhance personalized marketing algorithms.
• Use a sprint to develop A/B tests for different personalization strategies.

AI in Healthcare Monitoring:

• Use Agile to rapidly develop and deploy remote healthcare monitoring tools.
• Dedicate a sprint to integrate patient feedback into monitoring tools.

STEEP (Social, Technological, Economic, Environmental, and Political)

As businesses extended operations globally, understanding political, environmental, and social settings grew critical. The 1960s acronym PEST evolved into STEEP including Ecological and Legal lenses for situational assessments and strategic decision-making in complex operating environments.

Predictions

• Social Awakening: Increased public discourse on AI ethics and responsible AI.
• Technological Leaps: Breakthroughs in AI efficiency, reducing computational costs.
• Economic Shifts: AI will become a key differentiator in market competitiveness.
• Environmental Focus: More AI algorithms will be designed with energy efficiency in mind.
• Political Moves: Initial steps towards international AI governance and standards.
• Cross-Sector Adoption: AI will penetrate deeper into healthcare, finance, and manufacturing.
• Talent War: A surge in demand for AI specialists.

Accompanying Actions

Social Awakening:

• Conduct internal audits to ensure your AI practices align with ethical standards.
• Engage in public forums to share your company's stance on responsible AI.

Technological Leaps:

• Invest in R&D to explore ways to make your AI algorithms more efficient.
• Partner with academic institutions to stay at the forefront of AI efficiency breakthroughs.

Economic Shifts:

• Conduct a competitive analysis to identify how AI can give you an edge.
• Reallocate budget to accelerate AI initiatives that will make you more competitive.

Environmental Focus:

• Audit current AI algorithms to assess their energy consumption.
• Collaborate with environmental experts to integrate sustainable practices into AI development.

Political Moves:

• Stay updated on international AI governance discussions.
• Advocate for fair AI governance by participating in industry consortiums.

Cross-Sector Adoption:

• Identify new sectors where your AI technology could be applied.
• Partner with industry leaders to co-develop AI solutions.

Talent War:

• Develop an attractive employee value proposition focused on career development in AI.
• Partner with educational institutions to create a pipeline of AI talent.

Technology Readiness Levels (TRL)

In the 1970s, NASA's Space Division developed the TRL framework to assess the maturity of space technologies from basic research to full operational use. It gained international popularity as businesses also sought objective yardsticks for innovation commercial readiness.

Predictions

• Early-Stage Innovations (TRL 1-3): Increased funding and academic interest but not yet market-ready.
• Mid-Stage Technologies (TRL 4-5): Expect to see more pilot projects and initial commercial applications.
• Advanced Prototypes (TRL 6-7): These technologies will undergo rigorous testing and may face regulatory hurdles.
• Market-Ready Technologies (TRL 8-9): Widespread adoption and refinement, becoming industry standards.
• Cross-Industry Impact: Technologies at different TRLs will start impacting multiple sectors.
• Regulatory Evolution: As technologies move up the TRLs, expect more comprehensive regulations.
• Public-Private Partnerships: Collaborations between governments and private entities to accelerate technology readiness.

Accompanying Actions

Early-Stage Innovations (TRL 1-3):

• Allocate a portion of your R&D budget specifically for early-stage innovations.
• Build relationships with academic institutions to tap into emerging research.

Mid-Stage Technologies (TRL 4-5):

• Identify potential use-cases within your organization for pilot projects.
• Seek partnerships with startups specializing in mid-stage technologies.

Advanced Prototypes (TRL 6-7):

• Conduct comprehensive risk assessments for potential regulatory challenges.
• Develop a testing roadmap that includes internal evaluations and third-party validations.

Market-Ready Technologies (TRL 8-9):

• Integrate these technologies into core business processes and train your team.
• Continuously monitor performance metrics to refine and optimize impact.

Cross-Industry Impact:

• Conduct a cross-industry analysis to identify how technologies at different TRLs could impact your business.
• Develop a flexible technology adoption strategy that allows you to pivot.

Regulatory Evolution:

• Keep a legal team or consultant updated on technology advancements to anticipate regulatory changes.
• Engage with industry bodies and regulators to contribute to the formation of new regulations.

Public-Private Partnerships:

• Identify areas that could benefit from public funding or expertise.
• Actively seek out opportunities to collaborate with government agencies or industry consortiums.

How Generative AI Can Benefit Your Strategy

With the massive market focus on Generative AI, it is worth addressing separately as it will be a major force in your AI strategy, especially for innovation and creating new products or services:

• Content Creation: Automate your marketing efforts with Generative AI that can create high-quality content. This not only saves time but also ensures consistent messaging and tone.
• Data Augmentation: Generative AI can produce synthetic data to bolster your machine learning models, particularly when dealing with data scarcity or gaps.
• Data Ingestion: Streamline data ingestion by predicting the structure of incoming data for easy sorting and storage. AI-generated rules can validate data quality, spot anomalies, and help with legal compliance.
• Product Design: Accelerate your R&D process by using Generative AI to produce multiple design options based on predefined parameters.
• Customer Interactions: Enhance customer service with more natural and dynamic conversational agents.
• Ethical Considerations: Generative AI has made significant strides in being designed to adhere to ethical guidelines, ensuring responsible use.

Action Steps

1. Training: Empower your team with the knowledge and skills to harness the full potential of Generative AI. The goal is immediate productivity gains.
2. Feasibility Study: Conduct a thorough assessment of your existing data architecture and technology stack to identify how Generative AI can be seamlessly integrated.
3. Pilot Project: Kick off your Generative AI journey with a small, focused project that promises real business value. Avoid the allure of "shiny" or "toy" AI.
4. Monitoring and Feedback: Establish a robust monitoring and feedback loop. Continuously track key performance indicators and gather user feedback.
5. Scale: After validating the success of your pilot project, expand the use of Generative AI across various departments or product lines.