Innovation Principles, 6/7

football chalk game plan

“If you don’t have time to do it right, when will you have time to do it over?”

—John Wooden

Upon exiting the innovation spiral and kicking-off the formal product development process, the immediate first step should be creation of product requirements as a direct translation of the Target Product Profile into specific and measurable design characteristics and performance goals. Note that product requirements and the preliminary verification approach should be established before the development strategy and the main project master plan are finalized. Project requirements define the scope and complexity of the overall product development and inform how many different test methods are needed to verify the final design.

Some of the variable test methods could be unique and require time to develop test fixtures and associated Measurement System Analysis (MSA) for demonstrating a test’s adequate Repeatability and Reproducibility (R&R). Only when the fully defined product requirements and corresponding product verification plan are completed, we would be in a position to inform the confident planning necessary for a committable and predictable product delivery timeline. Product requirements are also associated with business requirements (e.g., target cost, branding, aesthetics, and more). These are inputs and design characteristics not intended to be verified in the same fashion as core performance requirements, especially in regulated industries.

Product requirements are considered one of the most defining success factors for a new product development. The underlying root cause for almost every failed product I have observed in the past 20 years could be traced back to inadequate requirements linked to missing or misinterpreted user needs.

“To be in hell is to drift; to be in heaven is to steer.”

—George Bernard Shaw

Once the product requirements are established, it is time to fully define the development strategy and outline exactly how the product will be developed—the Delivery Game Plan. Because both feasible concepts and product requirements are known at this point, we can be decidedly specific and structure the product development around exact systems, subsystems, and functions with all relevant options, trade-offs, and alternatives. That will also provide critical insights into required capabilities (professional expertise and specialties) and the capacity (number of people) required to get it done right and within the target time frame.

Position of product development strategy on the end-to-end product innovation framework.

If we are developing a complex product consisting of, for example, mechanical, electrical, and software subsystems, it is imperative to apply system engineering considerations. System Engineering relies on an integrated interdisciplinary approach that ensures that all system constituents, when working together, are achieving the intended product purpose and functionality. That is even more important if there is a bigger ecosystem within which this product might operate (e.g., Internet of Things).

Even for simpler products, the system engineering mindset could be valuable. In one example when developing a closed system transfer device for hazardous drugs, the device consisted of two interacting parts, one that attaches to the drug vial and the other to a syringe. It was convenient at the time to assign two available design engineers, one to each subsystem; however, this device’s key close transfer functionality was taking place at the elastomeric interface of the two subsystems when they were connected and fully engaged. Perhaps, a better development approach from the system engineering perspective would be to deploy a critical interface function owner with knowledge of the visco-elastic behavior of elastomers and another design engineer to own the overall device design.

A familiar illustrative example of development strategy applied to a residential house project.

The typical pitfall if you are underestimating the need to explicitly define development strategy is to jump to project planning and resources allocation with a focus on workload distribution among available team members. Even though that creates an illusion of an effective internal resource deployment, it does not necessarily reflect true project needs in terms of the actual required resource capacity and capabilities, which leads to potential slips or delays. The emphasis is on understanding critical design functions, variables, and corresponding governing science in order to define the development approach (experimental, modeling, iterative, internal, external, hybrid, etc.) and the required competencies and specialists.

A clearly defined development strategy that includes “what if” scenarios for contingencies and alternatives is foundational for confident, predictable, and committable project planning.

Editor’s Note: Selected topics from Milan Ivosevic’s book, Eureka to Wealth, will be featured as part of this Innovation Principles series in the following months:

  1. Introduction (Oct. ’23)
  2. Entrepreneurial Perspective: Human-Centered Design Entrepreneurship(Nov. ’23)
  3. Entrepreneurial Perspective: End to End Product Innovation Framework(Dec. ’23)
  4. Opportunity Incubation: The Innovation Spiral(Jan. ’24)
  5. Opportunity Incubation: Business Case (Sizing the Opportunity and Go / No Go check) (Feb. ’24)
  6. Product Delivery: Development Strategy (Mar. ’24)
  7. Product Delivery: Delivery Effectiveness (May ’24)

Capturing Value with Business Model Innovation

honey jars with dripping honey

Adventures in Business Model Innovation

Just what is business model innovation? With business model lifespans shrinking in this day and age, according to BCG’s exploration of business model innovation, it is the “art of enhancing advantage and value creation by making simultaneous—and mutually supportive—changes both to an organization’s value proposition to customers and to its underlying operating model.”

BCG further points out that at the value proposition level, these changes can address the choice of target segment, product or service offering, and revenue model. At the operating model level, BCG says the focus should be on how to drive profitability, competitive advantage, and value creation. This includes where to be competitive in the value chain; what is the best cost model of products and services to ensure profitability; and what is the best organizational structure and capabilities.

To be sure, not all business model innovation efforts are alike. BCG identifies four distinct approaches to business model innovation that can serve as a pathway to growth:

1. The reinventor approach is deployed in light of a fundamental industry challenge, such as commoditization or new regulation, in which a business model is deteriorating slowly and growth prospects are uncertain. In this situation, the company must reinvent its customer value proposition and realign its operations to profitably deliver on the new superior offering.

2. The adapter approach is used when the current core business, even if reinvented, is unlikely to combat fundamental disruption. Adapters explore adjacent businesses or markets, in some cases exiting their core business entirely. Adapters must build an innovation engine to persistently drive experimentation to find a successful “new core” space with the right business model.

3. The maverick approach deploys business model innovation to scale up a potentially more successful core business. Mavericks—which can be either startups or insurgent established companies—employ their core advantage to revolutionize their industry and set new standards. This requires an ability to continually evolve the competitive edge or advantage of the business to drive growth.

4. The adventurer approach aggressively expands the footprint of a business by exploring or venturing into new or adjacent territories. This approach requires an understanding of the company’s competitive advantage and placing careful bets on novel applications of that advantage in order to succeed in new markets.

The Business Transformation Equation

Business model innovation is also often critical to business transformation. In “Finding the Way to Business Transformation,” All Things Innovation explored how the concept of business transformation can be defined with a broad range of meanings depending upon the business and the different stakeholders that play a role in it. Even the forces of the marketplace, in terms of disruption, can define what type of transformation is necessary. One concept that many companies tout is that of digital transformation, but transformation can also be one of culture, talent, communication, strategy and more. In today’s environment, business transformation must also generate new value, whether that’s through new growth or other opportunities.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature the session, “Innovating Beyond Products: Strategies for Creating Value Through Business Model Innovation,” presented by Gus Valen, Chief Executive Officer and Entrepreneur, The Valen Group. While product innovation is important, true competitive advantage often lies in business model innovation. This session explores how organizations can unlock new sources of value by reimagining their business models. Register for FEI 2024 here.

Undertaking a Corporate Rethink

Creating value through business model innovation involves rethinking how a company delivers, captures, and communicates value to its customers. According to ChatGPT, here are some top strategies for achieving value through business model innovation:

  1. Customer-Centric Focus: Center the business model around understanding and meeting the needs of the customer. Engage in customer research, collect feedback, and tailor the business model to provide unique value propositions that resonate with customers.
  2. Agile Approach: Adopt an agile mindset to allow for rapid experimentation and adaptation. Encourage a culture that embraces change, and iterate on the business model based on continuous feedback and market insights.
  3. Ecosystem Collaboration: Explore partnerships and collaborations with other organizations to create value through complementary services or products. Building an ecosystem can extend the reach and capabilities of the business model.
  4. Digital Transformation: Embrace digital technologies to transform and optimize business processes. Leverage data analytics, cloud computing, and other digital tools to enhance efficiency, improve customer experiences, and create new revenue streams.
  5. Subscription and Membership Models: Transitioning from traditional product sales to subscription or membership models can create a steady and predictable revenue stream. This model often leads to stronger customer relationships and loyalty.
  6. Outcome-Based Pricing: Shift from traditional pricing models to outcome-based pricing where customers pay based on the value they receive. This ties the company’s success directly to the success of its customers.
  7. Servitization: Move from selling products to offering services. This can involve providing additional services, maintenance, or support that adds value to the customer’s experience and creates new revenue streams.
  8. Platform Strategies: Build platforms that connect different stakeholders in the value chain. Platforms can facilitate transactions, collaboration, and innovation, creating value for both the business and its users.
  9. Circular Economy Models: Embrace sustainable business practices by adopting circular economy principles. Design products for reuse, recycling, or remanufacturing, contributing to environmental sustainability and reducing waste.
  10. Personalization and Customization: Tailor products or services to individual customer needs. This strategy involves leveraging data and technology to create personalized experiences, enhancing customer satisfaction and loyalty.

Other key strategies include what is called frugal innovation, which focuses on delivering value with constrained resources. Brand innovation repositions or introduces new elements that resonate with evolving customer preferences. Rapid prototyping, data monetization, regulatory and compliance innovation also can be essential in business model innovation tactics.

Sustaining Business Model Success

Further ingredients for success include understanding the components of a robust business model canvas and how to leverage it for strategic decision-making; leveraging customer insights and market trends to identify opportunities for business model innovation; and infusing flexibility in strategy execution through agile methodologies to iterate quickly, test assumptions, and pivot as needed.

Successful business model innovation often involves a combination of these strategies, tailored to the specific industry, market conditions, and organizational context. Regularly reassessing and adapting the business model to evolving trends and customer needs is crucial for sustained value creation.

Whether you are an adapter, a reinventor, a maverick or an adventurer, as BCG puts it, business model innovation faces several critical questions and challenges. But reimagining the business model may just be what’s needed to position the business for sustainable future growth.

Video courtesy of Growth Manifesto Podcast

Applying AI to Anthropological Research

explorer in a cave

Augmenting Insights

The strong combination of AI and anthropological studies could indeed propel research methodologies forward. In fact, AI perhaps more than any other technological advancement could potentially navigate the complexity of human and societal behaviors.

In “Ten Predictions for AI and the Future of Anthropology,” from Anthropology News, Matt Artz makes the case that AI will be transformative to the field. Artz is an anthropologist specializing in user experience, product development and consumer insights. He is the founder of Azimuth Labs, and the creator and host of the Anthropology in Business and Anthro to UX podcasts.

“The future of anthropology is AI. We should embrace it and help shape its development, or risk being left behind,” says Artz. His predictions include:

1. All five fields will be disrupted: AI will profoundly impact all branches of anthropology, including applied anthropology. Its integration with archaeology will enable enhanced artifact analysis, reconstruction of ancient environments, and the identification of undiscovered sites. Biological anthropology will benefit from accelerated complex genetic data analysis and reconstructions of early humans. Meanwhile, linguistic anthropology will realize new opportunities for studying, reclaiming, and teaching endangered languages. It will also help cultural and applied anthropologists reveal hidden cultural patterns and spot emerging trends, leading to a better understanding of our past and more attuned interventions in the future.

2. AI as a collaborative partner: AI will soon be foundational to our work practice. We will engage AIs in discussions and creative brainstorming, leveraging their unique strengths to complement and scale our abilities. With the help of AI, anthropologists will be able to gain broader perspectives, leading to richer insights and increased problem-solving abilities.

3. Transforming ethnography: AI is poised to revolutionize ethnography by fundamentally altering how researchers conduct their work. AI-assisted ethnography will support researchers in collecting, analyzing, and interpreting data at scale. Techniques such as web scraping, natural language processing (NLP), and computer vision will make this possible and unveil new insights and patterns.

4. Enhancing public engagement: AI-generated visualizations, videos, interactive data representations, and immersive digital experiences will help anthropologists to convey complex research findings and narratives in appealing, relatable, and accessible ways to wider audiences.

5. Automated digital ethnography (ADE): ADE enhances traditional ethnographic methods by automating the research process within digital field sites. By deploying programmed ADE agents, researchers can tap into the vast amounts of unstructured data available on the Internet, such as social media posts, forum discussions, and blog entries. As these agents continuously collect and analyze data in real time, they act as ever-present partners in the field, providing researchers with valuable and up-to-date insights. This real-time engagement will assist anthropologists in quickly identifying emergent human behavior and cultural patterns, leading to more agile and timely research.

6. AI multimodal analysis: Integrating AI into ethnographic research will revolutionize the scale at which anthropologists conduct multimodal analysis. Handling multimodal data can be extremely time consuming, often requiring researchers to meticulously sort through and piece together various forms of information. But with AI we can automate the process of sifting through and analyzing diverse data types, such as text, images, audio, and video, significantly reducing the time and effort required. This efficiency will allow anthropologists to focus on more complex and nuanced aspects of the research process.

7. Anthropology-specific AI: With the increasing integration of AI into anthropology, we can expect to see specialized tools designed to address the unique challenges and complexities inherent in studying the human experience, going beyond the capabilities of general AI models. One example may be a fine-tuned large language model (LLM) that extends general knowledge LLMs such as OpenAI’s GPT-4. With these anthro-specific tools, anthropologists will benefit from more contextually relevant insights.

8. Advancing research with knowledge graphs: Anthropological knowledge graphs (AKGs) will revolutionize how anthropologists store and access information by creating specialized knowledge repositories that interlink entities—people, organizations, concepts, historical events, methods, disciplines, publications, and more—within a contextual framework. As these AKGs develop, they will empower researchers to better comprehend human social, cultural, biological, and linguistic diversity, paving the way for a web-scale model that accurately represents the complexity of human experience.

9. New models of anthropological entrepreneurship: Historically, anthropological entrepreneurs typically set up research practices. While that is not going to change, and it may even be accelerated by the incorporation of AI, the new model of anthropological entrepreneurship will take the form of founding tech companies that combine the wisdom, empathy, and ethics of anthropology with computer and data science to innovative businesses models and products. By bringing an anthropological perspective to the technology industry, these entrepreneurs can ensure that AI applications are developed with a deep understanding of the complexities of human societies and the potential consequences of technology. This will create products and services that prioritize ethical considerations, minimize adverse impacts, and contribute positively to global communities.

10. Productize anthropology: Artz’s vision for the future of anthropological entrepreneurship includes an AaaS platform. Imagine a subscription-based service that is accessible to anyone, regardless of their background. This platform would harness anthropology-specific AI tools for data collection, analysis, and insights generation, democratizing anthropological knowledge and promoting innovation. By making anthropological insights widely accessible, an anthropology as a service (AaaS) platform could help to propel the discipline forward, ensuring its ongoing relevance and impact in our increasingly digitized world.

Research Agility

In “Infusing the Behavioral Insights Mindset,” All Things Insights’ Seth Adler explored “Activating Behavioral Insights: Team & Consumer Themes” with Jennifer Avery, Senior Vice President, Strategy & Insights, Universal Parks & Resorts, and Cherie Leonard, Senior Director, Head of North America Insights, Colgate-Palmolive, NA. They discussed the implementation of a behavioral insights initiative from both a team and consumer perspective, team temperament, and leveraging consumer behavioral science in the research and outcomes.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature the presentation, “Agile Insights Unleashed: AI and Anthropology Transform Buzz into Reality,” presented by Ujwal Arkalgud, EVP and Group Director for Consumer Innovation, Lux Research. This session explores the pivotal role of the synergy between Artificial Intelligence (AI) and in-depth anthropological research in actualizing genuine agile research. This unique combination not only propels research methodologies forward but also deeply understands and navigates the complexities of human behavior and societal trends. The session will demonstrate the transformative impact of AI and anthropological insights working in tandem to redefine research agility. Register for FEI 2024 here.

The Convergence of Tools & Research

By delving into how AI-enabled tools and anthropological research converge, Lux Research says it aims to initiate a new wave of innovation and insights within the research community. This could well serve as a renewed vision for achieving agile research, focusing on the strategic use of AI and anthropology to foster an approach that is both more adaptive and deeply human-centric.

Of course, ethical considerations must also be top of mind when it comes to developing AI tools for various fields of study, including agile research.

Artz notes, “As we embrace the exciting possibilities that digital innovation brings to anthropology, we must remain vigilant and committed to addressing the ethical challenges that come along with it. By critically examining issues such as bias, fairness, transparency, privacy, and the potential impact on job markets, we can work towards a future where AI is a force for good within our discipline. We are uniquely positioned to contribute to these conversations and ensure that anthropological insights inform the evolution of AI technologies.”

Video courtesy of Matt Artz

8 Risks If You Don’t Have an AI Strategy

Shopper in a grocery store.

1. Competitive Disadvantage

For FMCG companies, the competition is relentless. Without AI, brands risk falling behind as competitors leverage AI for supply chain optimization, dynamic pricing, and consumer behavior prediction. Embracing AI is no longer about keeping up—it’s about setting the pace for innovation and efficiency in a sector where margins are thin and scale is everything.

2. Consumer Expectations

In an industry driven by consumer choices, AI provides the insights necessary to deliver the personalized experiences that customers now expect. Whether it’s through AI-driven recommendation engines or smart inventory management ensuring their favorite products are always in stock, consumers gravitate towards brands that seem to “know” them. Without AI, FMCG brands risk becoming faceless entities in a market that demands personalization.

3. Risk Management

AI’s predictive analytics can be the crystal ball that FMCG companies need to foresee and navigate risks—from fluctuating commodity prices to changing regulatory landscapes. In a sector where brand reputation is paramount, failing to predict and mitigate risks quickly can result in more than just lost sales—it can damage a brand’s credibility irreparably.

4. Regulatory Compliance

As regulations become more stringent and complex, especially around sustainability and ethical sourcing, AI offers FMCG companies the tools to stay compliant and ahead of legislative changes. Non-compliance can result in hefty fines and, even worse, a public relations nightmare that no FMCG brand can afford.

5. Efficiency & Cost Savings

With razor-thin margins, the FMCG sector’s success hinges on efficiency and the ability to reduce costs without compromising on quality. AI helps streamline production lines, optimize logistics, and reduce waste, ensuring that resources are utilized to their fullest potential. Without an AI strategy, FMCG companies may see their profits squeezed as operational inefficiencies go unchecked.

AI Strategy Graphic

6. Data Utilization

Data is the new oil, and in the FMCG sector, it’s flowing abundantly. AI is critical in converting this data into actionable insights—understanding market trends, predicting demand, and crafting marketing strategies that resonate. Ignoring AI means ignoring the data that could drive the next big FMCG trend.

7. Talent Attraction & Retention

AI expertise is becoming as essential as marketing savvy in the FMCG sector. Companies that showcase a commitment to AI innovation are more attractive to the best minds in data science and analytics. Without an AI strategy, FMCG companies may find themselves bereft of the talent that could drive their next breakthrough.

8. Strategic Alignment

For FMCG brands, aligning every aspect of the business with consumer demand is crucial. An AI strategy ensures that technological investments directly support the goal of meeting consumer needs quickly and effectively. Without this alignment, FMCG companies may find themselves investing in tech that doesn’t deliver ROI or, worse, detracts from the consumer experience.

The bottom line for FMCG companies in 2024 is clear: integrate AI into your strategy or risk falling behind. AI isn’t just about technology; it’s about staying relevant, resilient, and responsive in a sector where the consumer is king and the market waits for no one. An AI strategy is an indispensable tool in the FMCG toolkit, and those without it may soon find themselves unable to compete in the increasingly tech-driven marketplace.

Click here for more columns by Gail Martino; if you enjoy this content, please consider connecting with Gail Martino on LinkedIn.

Positioning the Role of Prompt Engineering

prompt engineering

Fine-Tuning the AI Model

According to Giskard, prompt engineering is not just about devising prompts to enhance the efficiency of AI. It’s also a method to “commandingly steer an AI model through a vast and sophisticated matrix of language options. An adeptly designed prompt offers the model a navigational chart, endowing it with context and direction, assisting in the production of relevant and actionable responses.”

The emergence of innovative prompts is ushering in a new era of sorts for this discipline, Giskard notes. This method is said to “derive creative strategies to exploit language models,” which thereby expand the AI’s capabilities and broaden its use in research and development activities. This takes place while balancing precision and innovation while keeping ethical safeguards and policies top of mind to shape AI progression.

Other emerging trends and techniques in prompt engineering include:

  • Exemplar Training: Usage of example prompts showcased improvements in output precision.
  • Situational Prompts: These prompts draw on the wider scenario to guide models, culminating in more relatable and sophisticated responses.
  • Link Prompts: This approach involves sequences of prompts, each one built upon the last, generating prolonged and coherent dialogues with the model.

Prompting Success Between AI & Innovation

All Things Innovation’s “The Impact of AI on Innovation” further looked at how artificial Intelligence is having a profound impact. From healthcare to publishing to industrial fields and manufacturing, the effects of AI on systems and the workforce are just starting. Just where does one balance the advantages versus the drawbacks? The technological developments of AI, and the rapid speed of adoption, are generating some potential advantages in the market yet there are some pitfalls to avoid as well.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature the keynote presentation, “Prompt Engineering & Innovation Evolution,” by Sanjana Paul, Executive Director at Earth Hacks. Earth Hacks works with college students and organizations to host environmental hackathons focused on creating innovative, equitable, and just solutions to the climate crisis. Register for FEI 2024 here.

An Evolving Field

On LinkedIn, Zia Babar, a technical adviser with PwC Canada, outlined some of the trends touching on the rapid evolution of prompt engineering. Babar notes, “The initial models were relatively simple, focusing on basic text prediction and pattern recognition. As computational power increased and more sophisticated algorithms were developed, these models began to evolve rapidly, leading up to the current state where LLMs can simulate human-like language comprehension and generation with remarkable accuracy.”

Babar further looks at the process of prompt engineering as a multi-layered step by step system that makes the most of the AI:

  1. Selecting a Suitable Pre-training Model: The first step in prompt engineering is to select an appropriate pre-training model. This choice is critical, as the chosen model’s design and prior training define its abilities and constraints. Factors such as the size of the model, the diversity and scope of its training data, and its architectural features need to be considered.
  2. Designing Effective Prompts: Designing effective prompts is a crucial part of prompt engineering. A well-designed prompt should not only align with the model’s training and capabilities but also be tailored to elicit the desired response for the specific task. This involves an in-depth understanding of language nuances and how various prompt structures might influence the model’s output.
  3. Creating Task-Specific Responses: Once an effective prompt is designed, the next step is to create task-specific responses. This involves defining the format and structure of the desired output. This step often requires a deep understanding of the task requirements and the target audience for whom the output is intended.
  4. Developing Efficient Training Strategies: The final step in prompt engineering is developing efficient training strategies. This involves finding ways to fine-tune the model with minimal resources while maximizing its performance. Training strategies might include techniques like few-shot learning, where the model is exposed to a few examples of a new task to adapt quickly, or transfer learning, where knowledge from one task is applied to another. The goal is to enhance the model’s learning efficiency, enabling it to quickly adapt to new tasks with minimal additional training.

Narrowing the Communication Gap

Looking ahead, the future of prompt engineering seems bright with potential. As natural language processing evolves and expands, so too will the capabilities of this complex discipline. As AI becomes more intuitive and intelligent, Babar notes, “this progression is likely to foster more personalized and interactive AI experiences, narrowing the communication gap between humans and machines.”

Babar further concludes that, “The integration of various data types (text, images, audio and video for instance) in prompt engineering opens up a realm of possibilities for creating more nuanced and sophisticated AI systems that better mimic human perception and cognitive abilities.”

Video courtesy of AssemblyAI

Powering AI-Driven Innovation

The Application of AI

At its basic level, AI-driven innovation means the application of artificial intelligence technologies to the innovation process, as noted by Orchidea’s Emma Lahti in her blog, “What is AI driven innovation? Role of AI in innovation.” This could potentially involve AI algorithms, predictive analytics, natural language processing, and other machine learning techniques to generate new ideas, develop them further and support them with data findings, and improve decision making.

Of course, the combination of AI with human talent is also key in this innovation discovery process. We are not suggesting that AI should be unsupervised, so to speak. “AI-driven innovation is not about replacing human creativity but rather empowering it,” writes Lahti. Can AI create innovation? Well, AI certainly can leverage its vast amounts of data and knowledge base. Lahti points out that AI excels at combining, refining, and altering pre-existing ideas, as well as restructuring data.

But the potential is there to drive true innovation, as well as enhance the process to developing and generating ideas. Further, as Orchidea, an AI powered innovation platform, notes, “AI drives innovation through its diverse capabilities, particularly in analyzing data to make predictions, generate creative ideas, and offer suggestions for improvement. AI capabilities can be harnessed at various stages of the innovation process, including idea generation and development, concept prioritization, and evaluation. It can potentially elevate creativity and generate innovative ideas while providing valuable perspectives to human ideas.”

The Impact of AI

All Things Innovation’s “The Impact of AI on Innovation” further looked at this growing process. Artificial Intelligence (AI) is having a profound impact and influence on a broad range of industries. From healthcare to publishing to industrial fields and manufacturing, the effects of AI on systems and the workforce are just starting. Already there has been cause for celebration of automating some tasks, to consternation over what could be perceived as the negative effects of AI on a given field. Just where does one draw the line on the advantages versus the drawbacks? The technological developments of AI, and the rapid speed of adoption, are generating plenty of questions in the market. Just what are some of the pitfalls and potential of AI when it comes to innovation?

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature “Implementing An AI-Driven Innovation Discovery Process,” presented by Miranda Helmer, VP, Innovation Discovery, The Clorox Co., and Oksana Sobol, Senior Director, Insights Lead, The Clorox Co. No matter how it’s informed, the traditional innovation discovery process is simultaneously messy and beautiful. The messiness is provided by humans knocking their heads together to generate ideas that provide the groundwork for the growth and resilience of an organization. What’s beautiful is when that “fuzzy front-end of innovation,” works. But it works slowly. At this moment, the innovation discovery process simply needs to be AI-driven. Dive into this session and experience what’s already working, at the modern pace of business. Register for FEI 2024 here.

The Advantages of AI-Innovation

Implementing an AI-driven innovation discovery process can yield several significant benefits for organizations. We asked ChatGPT, appropriately enough, to list the top advantages:

  1. Efficiency and Speed: AI can rapidly analyze vast amounts of data, trends, and market insights, accelerating the innovation discovery process. This efficiency allows organizations to quickly identify opportunities, stay ahead of competitors, and bring new ideas to fruition faster than traditional methods.
  2. Data-Driven Decision Making: AI leverages data analytics to make informed decisions based on real-time information. By incorporating AI into the innovation discovery process, organizations can rely on data-driven insights, reducing reliance on intuition alone. This approach enhances the accuracy and relevance of decisions, leading to more successful innovation outcomes.
  3. Pattern Recognition and Trend Analysis: AI excels at recognizing patterns and analyzing trends across diverse datasets. Implementing AI in innovation discovery enables organizations to identify emerging patterns, consumer behaviors, and market trends that may not be apparent through conventional methods. This foresight helps in proactively addressing market demands.
  4. Enhanced Creativity and Idea Generation: AI tools can assist in brainstorming and idea generation by offering diverse perspectives and insights. These systems can analyze existing data to propose novel ideas, providing a valuable resource for teams engaged in innovation. This collaborative approach between humans and AI fosters a more creative and dynamic ideation process.
  5. Cost Reduction and Resource Optimization: The efficiency of AI-driven innovation discovery can lead to cost savings by streamlining processes, minimizing the need for extensive human resources, and preventing the pursuit of ideas with limited potential. This optimization allows organizations to allocate resources more effectively and focus on initiatives with higher innovation success probabilities.
  6. Continuous Learning and Adaptation: AI systems are capable of learning from ongoing data inputs and adapting to changing market dynamics. This adaptability ensures that the innovation discovery process remains relevant and aligned with evolving consumer preferences and industry trends over time, contributing to sustained success.
  7. Risk Mitigation: AI can assess potential risks associated with innovation initiatives by analyzing historical data and predicting potential challenges. This risk assessment enables organizations to make informed decisions, reducing the likelihood of failed innovations and optimizing resource allocation.

Harnessing Innovation

By harnessing the power of AI in innovation discovery, organizations can gain a competitive edge, foster a culture of continuous improvement, and adapt more effectively to the dynamic business environment. AI is further unlocking growth opportunities, which should only enhance the innovation environment, especially as AI continues to be integrated into a diverse range of industries. This increase in efficiency should be tempered with the challenges of AI, such as its input and output limitations, privacy and ethical concerns.

Back to the human equation for a moment. As Orchidea asks, “Can AI completely replace the human aspect of innovation management? While AI can generate cost-effective and creative ideas, human expert judgment remains challenging to replicate. Therefore, organizations must embrace AI as a valuable tool that empowers human creativity and decision-making. By adopting best AI practices and evaluating AI outputs with human effort, businesses can harness the full potential of AI in driving innovation.”

Video courtesy of Dell EMC

Empowering Innovation Through the Democratization of AI

The Benefits of Open Source

While large tech companies are rapidly investing in AI research and development, which has resulted in advances in the tool, issues around bias, accountability, security and transparency remain. This has led some to call for more open AI ecosystems.

As Ryo Sakai asserts in his blog on Medium, “The Democratization of AI: How Open Source is Fueling the AI Revolution,” it is the “open-source movement that aims to make AI more accessible by creating free, public resources and technologies. Proponents argue that open-source spurs innovation through collaboration while also building trust by showing how AI systems work under the hood. As AI becomes integrated into more aspects of our lives, ensuring it aligns with shared values becomes increasingly important.”

The benefits of open AI to innovation include more open partnerships, increased collaboration and communication as well more accelerated innovation and faster development cycles. Sakai observes, “The collaborative nature of open-source enables faster iteration and innovation. By working transparently, developers can build on top of existing tools and frameworks instead of reinventing the wheel. Open communities also attract diverse talent and produce more robust tools through large-scale peer review.”

Promoting fairness and accountability, building trust, and expanding accessibility can also enable startups and small businesses to tackle more innovation tasks through open AI sources. Certainly, there are still challenges that remain, such as security issues, copyrighted materials, and the protection of intellectual property. Ease of use can also still be a challenge, as some training might still be required for a non-technical person to use these tools. But Sakai argues that nonprofits, government agencies and universities may benefit the most from democratizing AI. An intriguing question could be whether private enterprises could also have potential success in leveraging open-source AI.

The Open Road

All Things Innovation’s “Embracing Open Innovation” looked at how open innovation is a growing tactic for today’s globalized enterprises. Rather than focusing just on closed loop systems and internal sources, such as their own research and development department, open innovation is a collaborative approach with those outside the company and a way to find key external resources to foster innovative thinking and practices. This calls for the innovation team to broaden their reach through open tactics and collaborative approaches, rather than closed strategies, to bring open and resilient innovation to the table as an important management tool.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature Gen AI roundtable sessions. The Gen AI roundtables are plenary sessions where the audience will be providing AI use cases that are real, either within an organization, team based or simply based on a personal executive’s remit. FEI will populate hundreds of real time Gen AI use cases within this session in an anonymized fashion, so who shares what at the roundtable remains anonymous. The key takeaways will be populated into a post-show report, where you’ll see hundreds of Gen AI use cases. Register for FEI 2024 here.

Encouraging Innovation

The agile democratization of AI can significantly benefit innovation across various industries. We asked ChatGPT for several ways in which this approach can contribute to fostering innovation:

  1. Faster Prototyping and Iteration: Agile methodologies, combined with the democratization of AI, enable faster prototyping and iteration of AI-powered solutions. This speed facilitates a more dynamic and responsive development process, allowing teams to experiment with different AI models and algorithms to find the most effective and innovative solutions.
  2. Cross-Functional Collaboration: By democratizing AI, organizations can involve individuals from various departments and skill sets in the AI development process. This cross-functional collaboration enhances creativity and brings diverse perspectives to problem-solving, leading to more innovative AI applications that address a broader range of business challenges.
  3. Empowering Non-Technical Teams: Democratizing AI makes AI tools more accessible to individuals with non-technical backgrounds. This empowerment of non-technical teams, such as marketing, sales, and customer support, allows them to explore innovative use cases and implement AI solutions in their respective domains without heavy reliance on data scientists or developers.
  4. Rapid Experimentation and Learning: Agile practices coupled with AI democratization encourage a culture of experimentation. Teams can rapidly test hypotheses, gather insights, and learn from failures, fostering a continuous improvement mindset. This iterative approach accelerates the innovation cycle, leading to more effective AI implementations.
  5. Enhanced Problem-Solving: The democratization of AI enables a broader range of professionals to engage in problem-solving using AI tools. This access allows individuals to apply AI to unique challenges in their domains, leading to innovative solutions that might not have been apparent within a more constrained development environment.
  6. Increased Innovation Diversity: With a more diverse group of contributors participating in AI projects, there’s a greater likelihood of diverse perspectives and ideas. This diversity contributes to a richer pool of innovative solutions and applications, fostering a more inclusive and comprehensive approach to leveraging AI for various business purposes.
  7. Flexibility in AI Implementation: Agile democratization allows for flexibility in how AI is implemented within an organization. This adaptability enables businesses to experiment with different AI models, algorithms, and deployment strategies, leading to the discovery of novel approaches and innovative applications.
  8. Improved Time-to-Market: The agile democratization of AI streamlines the development process, reducing bottlenecks and improving time-to-market for AI solutions. This accelerated timeline enhances the organization’s ability to stay ahead in a rapidly changing business landscape.
  9. Data-Driven Decision-Making: The combination of agile practices and democratized AI empowers organizations to make more informed, data-driven decisions. This data-centric approach enables better identification of market opportunities, customer needs, and areas for improvement, contributing to more innovative and targeted business strategies.
  10. Scalability and Accessibility: Democratizing AI makes AI capabilities more scalable and accessible across different teams and departments within an organization. This scalability allows for broader innovation initiatives, making it possible for diverse teams to leverage AI for various purposes without overwhelming resource constraints.

Empowering Organizations

The agile democratization of AI promotes a collaborative and adaptable environment that accelerates innovation by empowering diverse teams, fostering experimentation, and enhancing problem-solving capabilities across different domains within an organization. Just how open-source approaches to AI, or open innovation methods, would play a role is still a work in progress, and might depend on the needs of the company.

But as Sakai writes, “The open-source approach provides a compelling model for AI development that promotes transparency while accelerating innovation. By making knowledge and technologies freely accessible, we can bring the benefits of AI to society more quickly and equitably.”

Video courtesy of Future of AI & Data

Finding Best Practices for Data Management

Creating the Data Management Pipeline

According to Thoughtspot’s blog, “14 proven data management best practices,” implementing best data management practices can help streamline and implement better decision making for the enterprise. Some of the best practices they recommend include:

  1. Establish a single source of truth: All data should be stored in one centralized system that is accessible to everyone in the organization. This ensures consistency and accuracy across all operations. 
  2. Properly tag and store data: Data should be clearly labeled for easy retrieval, and ideally stored in an organized database or cloud data warehouse.
  3. Utilize data lineage: Track each piece of data’s origin and its transformations as it makes its way through the organization, helping to ensure accuracy. This is particularly important for analytics engineers, who leverage software best practices to create more agility in their organizations.
  4. Make security a priority: Protect important information with strong authentication measures such as two-factor authentication and encryption technologies. 
  5. Define data access policies: Establish clear guidelines for who can access what types of data and when they can access it to ensure compliance with privacy laws. This includes being able to govern access for every single user, down to the row and cell level.
  6. Leverage automation technologies: Automate processes such as backups, archiving, and workflow execution to help increase efficiency and accuracy across the organization.
  7. Monitor user activity: Track how users interact with your systems in order to identify any potential issues or suspicious behavior. 
  8. Keep data clean: Regularly audit data for accuracy, completeness, and consistency with data observability tools. 
  9. Launch self-service analytics: With the right data governance, organizations can launch self-service analytics tools to their entire organization.
  10. Continually review processes: Regularly assess your data management policies and procedures to make sure they are meeting current needs.

More Data Details

In “Solving the Data Bottleneck,” All Things Innovation explored some of the data pipeline issues currently happening in today’s organizations. The flow of data has increased as technology such as AI has been developed. Big data is here. Some companies may feel pressured by just what to do with all that data. There’s a lot of untapped value. Some have likened the flow of data to that of a firehose on full power. There’s just one issue, however, of effectively directing that flow of information. This has led to more of an emphasis on data governance and data democratization, which is a process of shifting the mindset and responsibility of data analytics from the data scientists or IT to all users across the organization.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature a session called “High Performance: Marrying Profession & Passion Through Data,” presented by Maximiliano Just, Vice President, Data Governance and Shared Platforms, Fortune 100. Building good data structure, leadership, data practices and methods of working with data can improve business performance. There are ways to optimize data, mindset, and ways of working to enhance performance and outcome. If you want to win, you’ve got to be data-driven. Register for FEI 2024 here.

Implementing Best Practices

Implementing best practices in data management is crucial for organizations to ensure the accuracy, security, and effective utilization of their data assets. We asked ChatGPT for some key benefits associated with adhering to best practices in data management:

  1. Data Accuracy and Quality:
    • Improved Decision-Making: Accurate and high-quality data provides a reliable foundation for decision-making, enabling organizations to make informed and strategic choices.
    • Reduced Errors and Inconsistencies: Following best practices helps in minimizing errors, duplications, and inconsistencies in data, contributing to a more reliable and trustworthy dataset.
  2. Data Security and Compliance:
    • Mitigation of Risks: Adhering to data management best practices helps mitigate the risk of data breaches and unauthorized access, safeguarding sensitive information.
    • Compliance with Regulations: Following best practices ensures that data management processes align with industry regulations and data protection laws, avoiding legal complications and fines.
  3. Efficient Data Governance:
    • Clear Accountability: Establishing data management best practices promotes clear roles and responsibilities within the organization, ensuring accountability for data quality and security.
    • Effective Policies and Procedures: Implementing best practices enables the development of effective data governance policies and procedures, facilitating consistent and standardized practices across the organization.
  4. Improved Data Integration:
    • Seamless Data Exchange: Best practices in data management support efficient data integration, allowing seamless exchange of information across different systems and platforms.
    • Enhanced Interoperability: Organizations can achieve better interoperability between disparate systems, enabling a more cohesive and connected IT environment.
  5. Optimized Data Storage and Retrieval:
    • Cost Savings: Effective data management practices contribute to optimized data storage, reducing unnecessary costs associated with maintaining and storing redundant or outdated information.
    • Faster Retrieval: Well-managed data ensures faster and more accurate retrieval of information, improving operational efficiency.
  6. Enhanced Data Collaboration:
    • Improved Communication: Data management best practices promote better communication and collaboration among different departments within an organization, fostering a data-driven culture.
    • Cross-Functional Insights: Teams can leverage a unified and accurate dataset for collaborative efforts, leading to more comprehensive insights and solutions.
  7. Scalability and Future-Proofing:
    • Adaptability to Growth: Best practices allow organizations to design scalable data management frameworks that can accommodate growing datasets and evolving business needs.
    • Future-Proofing: Implementing best practices helps organizations stay adaptable to emerging technologies and industry trends, ensuring relevance and longevity of data management strategies.
  8. Increased Customer Trust:
    • Enhanced Customer Experience: By maintaining accurate and secure customer data, organizations can provide a better and more personalized experience for their customers.
    • Building Trust: Demonstrating a commitment to data integrity and security builds trust with customers and partners, enhancing the organization’s reputation.
  9. Effective Analytics and Reporting:
    • Reliable Insights: Best practices enable the generation of reliable and consistent data, supporting accurate analytics and reporting for better business intelligence.
    • Data-driven Decision-Making: A well-managed data environment facilitates a data-driven decision-making culture, leading to more effective and strategic choices.
  10. Reduced Data Silos:
    • Improved Collaboration: Breaking down data silos through best practices promotes collaboration between departments, ensuring that information is shared and utilized across the organization.
    • Holistic View: Organizations can achieve a more holistic view of their operations by integrating data from various sources, enabling a comprehensive understanding of business processes.

Gaining An Advantage

Adopting best practices in data management is essential for organizations seeking to maximize the value of their data assets, minimize risks, and create a foundation for innovation and growth. It also enables organizations to create a data-driven culture, a mindset that relies on analytics and insights to drive expansion and growth. This in turn will give the company a data-driven edge, putting the company at an advantage over its competitors.

Video courtesy of Lights On Data

Increase Consumer Engagement Through Content Interactivity

Content is King

Whatever types of media and interactivity one might leverage in their business, a well thought out content marketing strategy can be a significant driver of growth. This should align with industry trends. This also connects to having a user-centric design and technological perspective while developing content and driving engagement.

ESS Global Training Solutions references a new eBook from Grazitti Interactive, a global digital services provider, which outlines “2024’s Top Interactive Content Trends Unveiled.” As brands turn to visual content to capture short-lived consumer attention spans, short-form videos, live streaming, and interactive content is staying on-trend to engage audiences and drive brand loyalty. This includes interactive content such as quizzes, polls, surveys, and infographics. “The ability to convey information quickly, entertain, and actively involve users has made visual content a preferred choice for content marketers,” the report notes.

Key trends in the report that aim to enhance the user experience include implementing an effective SEO strategy; creating high-quality content, which includes not only short-form but long-form content as well to build trust as an information resource; developing structured data and featured snippets; and mobile optimization. Have we reached a tipping point in terms of immersive experiences such as VR and AR? Perhaps not yet, but the report notes that these technologies can engage audiences and create memorable experiences.  

Other key takeaways of the report include:

  • Interactive content marketing is on the rise in 2024.
  • Creating engaging content that aligns with industry trends is crucial.
  • Personalization at scale using AI and machine learning is a growing trend.
  • Ethical and sustainable content resonates with consumers.
  • Video marketing and influencer collaborations are effective strategies.

Connecting Interactivity to AI

In All Things Innovation’s “Advancing Universal Interaction,” we explored some of AI’s nascent technologies and how it might benefit consumer interactivity. When viewed through AI-human interactivity, it is about a scale of mass adoption and invention of a technology that is only at the beginning stages of development and interaction. One expert points to augmented reality and virtual reality as the next wave that will impact consumer devices. Yet, it is with the power of AI that may make this a more possible reality.

Looking forward to FEI 2024? The conference, which will be held June 10 to 12, will feature a session called “When Your Customer Is Your Product: Uncovering Future Trends Through Direct Interactivity,” presented by Tamar Rimmon, VP, Research & Analytics Strategy, Fandom. No matter what we sell, we all know that directly engaging with the communities we serve is a key theme. The business model is that the customer is the centrifugal force of the product. User Generated Content drives the business, which provides a fair amount of information around where engagement is coming from and going to…and what that means for how one can innovate the offering. Register for FEI 2024 here.

Sharing Content Interactivity Benefits

Content interactivity, which involves engaging users and encouraging them to actively participate with digital content, offers several benefits across different contexts. Here are some key advantages as noted by ChatGPT:

  1. Increased Engagement: Interactive content captures and maintains users’ attention more effectively than static content. Users are more likely to stay engaged with interactive elements such as quizzes, polls, and games, leading to longer time spent on a website or platform.
  2. Enhanced User Experience: Interactivity creates a more enjoyable and memorable user experience. Users appreciate content that allows them to actively participate, providing a sense of control and personalization, which contributes to a positive overall experience.
  3. Improved Learning and Information Retention: Interactive content facilitates better understanding and knowledge retention. Features like interactive simulations, tutorials, and quizzes enable users to learn by doing, making the information more memorable and easily absorbed.
  4. Personalization: Interactivity allows for personalized experiences based on user preferences and interactions. This customization can range from personalized recommendations to dynamically adjusting content based on user inputs, creating a more tailored experience.
  5. Increased Conversion Rates: Interactive elements can be strategically designed to guide users through a conversion funnel. Whether it’s a quiz leading to product recommendations or an interactive demo showcasing features, well-designed interactivity can boost conversion rates.
  6. Data Collection and Insights: Interactive content provides opportunities to collect valuable user data. Through quizzes, polls, and interactive forms, businesses can gather insights into user preferences, behaviors, and opinions, which can inform future content strategies and marketing efforts.
  7. Social Sharing and Virality: Interactive content is often more shareable on social media platforms. Users are more likely to share quizzes, polls, and other interactive experiences with their networks, increasing the content’s reach and potential virality.
  8. Brand Differentiation: Using interactive content can set a brand apart from competitors. Innovative and engaging interactive features showcase a brand’s commitment to providing a modern and user-centric experience, contributing to brand differentiation.
  9. Foster Community and Interaction: Interactive elements can be used to build a sense of community among users. Features like comment sections, forums, and collaborative projects encourage users to interact with each other, fostering a community around the content or platform.
  10. Adaptability to Multiple Platforms: Many interactive content formats are adaptable to various digital platforms, including websites, social media, and mobile apps. This versatility ensures that the interactive experience reaches a broader audience.

Enterprise, Engage

Incorporating interactive elements into digital content requires thoughtful planning and design, but the benefits in terms of engagement, user satisfaction, and business outcomes make it a worthwhile investment for many organizations.

The digital landscape is always evolving but enhancing content interactivity can pave the way for increased brand development and growing online presence. As noted by ESS Global Training Solutions in its trend report, “By embracing these interactive content trends and digital marketing strategies, you can elevate your brand’s visibility, engage your audience, and drive meaningful results in 2024 and beyond.”

Video courtesy of Rock Content