The Three Pillars of a Principled Approach to Innovation


These pillars offer a blueprint for companies as they develop and deploy AI-powered solutions that deliver real value to end-users while maintaining a sustainable competitive advantage in a constantly evolving market.

There’s no denying that artificial intelligence (AI) is not only transforming the broader business and social landscape but also changing the way industries operate. In construction, AI tools can flag when project costs are approaching approved budgets and promote safety by predicting when equipment needs maintenance. Contrast this with the bustling retail industry, where AI tailors the shopping experience for each customer, from personalized product recommendations to targeted sales messaging.

This AI surge across industries pushes businesses to strategize how to deploy the latest technology to maintain a competitive edge. In fact, 46% of board members already have AI as their top priority.

Yet, this begs the question: How do organizations begin their AI transformation?

The Three Pillars to AI Innovation

Navigating this transformative journey starts with having a very pragmatic approach and embracing four pillars of principled innovation:

  1. Be Practical
  2. Be Customer-Centric
  3. Be Responsible

These pillars offer a blueprint for companies as they develop and deploy AI-powered solutions that deliver real value to end-users while maintaining a sustainable competitive advantage in a constantly evolving market.

Pillar #1: Be Practical

The potential benefits of this AI revolution invite swift, strategic adoption. AI global market value is estimated to grow at a compound annual growth rate of 37.3% between 2023 and 2030, signaling what appears to be a universal call to action. But companies must avoid falling prey to hype and innovating without a clear purpose. Make sure to anchor AI deployments in practicality in the quest for innovation. Solutions that directly tackle real-world challenges provide immediate benefits and ensure long-term sustainability and value.

Take, for instance, that one-third of organizations use generative AI (GenAI) regularly in at least one business function. One practical application for GenAI is to personalize and automate customer outreach campaigns that move the needle for businesses. GenAI can create compelling subject lines, body text, and calls to action by analyzing user behavior, preferences, and historical data. The personalized approach that GenAI can offer improves open, click-through, and conversion rates, helping organizations achieve their goals.

By focusing on practical applications and outcomes, businesses can transcend the allure of innovation for its own sake and instead deliver technologies that make a tangible difference in their operations and for their customers.

See also: The Ethical AI Imperative: How OpenAI is Leading the Way in Responsible Development

Pillar #2: Be Customer-Centric

A deep understanding of customer needs and challenges is at the core of successful AI innovation. By placing customer needs at the forefront of development efforts, companies can create AI solutions that truly resonate and deliver substantial value.

Being customer-centric means actively integrating insights into every stage of AI solution development. For instance, AI can sift through vast amounts of data to pinpoint where customers experience friction. If a retail customer experiences a complicated checkout process, AI can analyze the steps causing confusion or delay and suggest simplifications or automation. The AI feature or capability might also recommend reducing the required fields, integrating more payment options, or providing more precise instructions.

Additionally, companies can continuously refine AI systems, making them more effective and responsive to evolving customer expectations. In this way, being customer-centric with AI goes beyond merely meeting expectations—it’s about exceeding them, fostering loyalty, and driving long-term success.

Pillar #3: Be Responsible

Lastly, companies must align AI development and implementation with industry guidelines for responsible AI. One such guideline focuses on reducing bias in AI systems. After all, AI should benefit everyone. First, ensure that the data sets for training AI models are diverse and representative, capturing a broad spectrum of demographics and perspectives to prevent skewed outcomes. Regularly audit AI tools to check for unintended bias that may emerge over time. Assembling a team of diverse stakeholders who bring varied viewpoints can aid in the process of identifying bias.

Always be focused on the impact AI may have on customer data and privacy. Adhere to data and privacy regulations such as the California Privacy Rights Act (CPRA) and the General Data Protection Regulation (GDPR). Decision-makers should also consider how they will answer questions about handling customer data related to AI systems.

Businesses that are transparent about the AI development and implementation processes can build trust and foster positive relationships with their customers, paving the way for successful and ethical AI solutions.

Consider a Phased Approach

A thoughtful, phased approach to innovation involves incremental development and iterative improvement of AI technologies. This method reduces risks associated with premature launches and ensures that the final products are well-refined and ready to provide real benefits to users. A thoughtful, phased approach includes:

Setting Clear Priorities: Clearly articulate the desired outcome of implementing AI features to all stakeholders. Your desired outcomes could be to increase efficiency, automate processes, boost collaboration, or drive better decision-making. Whatever the objective, being clear before making changes can easily discern what tools and features to prioritize and identify those processes best suited for AI innovation, ensuring effective resource allocation.

Identifying Customer Needs: As previously mentioned, AI-powered products and services should always focus on specific customer needs. Design mechanisms to continuously gather and integrate customer feedback into the development process. There are several ways to collect valuable feedback, including digital surveys, user testing sessions, customer advisory boards, A/B testing, and more.

Communicating the Plan: Like any new tool or process, it’s crucial to communicate the AI plan to all stakeholders, including investors, employees, customers, partners, etc. Approaching AI deployment with transparency builds trust and sets unified expectations, aiding in seamless implementation and, when needed, plan adjustments.

Limiting Initial Release: When introducing a new AI system, consider releasing it to a limited audience first. A selective rollout, through focused testing, can result in valuable insights from early users. It’s an intelligent move that curbs potential risks and paves the way for a smoother release.

A phased strategy centered on iterative improvement and user feedback can lead to the successful introduction of AI technologies. By refining user experience and system performance, organizations can bridge the gap between technological potential and practical usability, fostering stakeholder trust and reliance.

The Future of AI Innovation

AI’s pervasive influence across industries underscores the urgency for businesses to navigate this transformative landscape strategically. Organizations that consider embracing these four pillars of principled innovation—practicality, customer centricity, thoughtfulness, and responsibility—can chart a course to AI success that delivers tangible value to end-users and drives competitive advantage in the marketplace.

Doug Johnson

About Doug Johnson

Doug Johnson has over 30 years of product management and marketing experience and is currently the Vice President of Product Management at Acumatica, where he defines business requirements for flexibly deployed ERP business management software using SaaS and Cloud technologies. Prior to this position, Doug served as the VP of Marketing for Parallels, a leading provider of automation and virtualization software, where he focused on the delivery of technology through service providers in a service model. Doug has extensive business experience in marketing and general management, with a concentration on developing new products and services in global markets. Previous employers include start-ups and established companies such as AT&T. Doug has an undergraduate degree in Electrical Engineering from Duke University and an MBA from the Stern School of Business at New York University.

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