Part 2: Integrating AI with Digital-First Growth Strategies
Eric Layland
Head of Digital Marketing | Marketing Modernization Lead | Digital Team Leader | Analytics & Insights | Operationalize AI | Strategic Growth | Digital Programs Optimization | Engagement Lead
Eighteen months ago, it seemed only the most forward-thinking organizations were examining how AI could impact their futures. Fast forward to today, nearly every organization is tripping over themselves trying to figure out what AI means to their future.??
Now, is AI really THAT disruptive? In a word: yes. But it's still very early days in what will be a long journey that’s still being defined. Developing an organizational point of view, as a prelude to a full-fledged AI strategy, should be on every leader’s to-do list now, if not yesterday. Assembling a cross-functional team to evaluate opportunities, launching meaningful pilots and rolling out initiatives should be on leadership's 2024-H2 OKRs. While it’s a long game, the pieces on the board are moving fast.?
This article does not attempt to review martech platforms and tools with AI capabilities. The AI-martech space is moving far too fast. Though an overview of AI Governance platforms is in the works. Rather I lay out 11 knowledge areas to prioritize and focus on to improve success for organizations beginning to leverage Generative and/or Predictive AI.?
1. Understand AI Foundations and Ethics
Before diving into AI initiatives, build a strong understanding of AI's core principles, limitations, and ethical implications. This helps support responsible development and use of AI. In need of structured learning about responsible AI? Right here on LinkedIn multiple short courses are available from under 5 minute concept overviews to multi-hour learning paths. A few resource links are included at the end of the article.?
2. Data Governance and Management
AI thrives on high-quality data. Robust data governance practices help ensure data accuracy, security, and accessibility, all vital for effective AI implementation. Developing your organization’s AI data strategy within the context of applicable regulations (i.e. HIPPA, GDPR, CCPA, etc.) is a non-negotiable step. Additionally, organizations need to take further steps to manage data ethically, ensuring privacy and security for stakeholders while leveraging AI for growth. Investments made in people, processes, and technology to support the governance, protection and management of data assets will pay off over the long term.?
3. Cross-functional Collaboration
Processes, especially those that cross functional areas, are key to capture and document. Use this opportunity to break down silos between technical and non-technical teams to ensure AI initiatives are aligned with user needs and business objectives. This involves communication skills and the ability to translate complex AI concepts into business cases that clearly articulate value. Continuous learning and developing “soft skills” are an underappreciated but emerging area of need for operationalizing AI.?A commitment to AI should be an organizational effort and not an interest area for select functional areas.
4. AI Strategy and Implementation
Clearly define your AI goals and how they align with your overall organizational and digital growth strategies. Develop a roadmap with a phased approach to implement AI effectively and avoid overwhelming teams. Encourage collaboration to identify opportunities where AI can enhance human effort, drive value, streamline operations, and/or enhance customer experiences. Organizations new to AI are encouraged to look at internal opportunities first before attempting to add AI into customer facing deliverables. Assess functional areas that are subject to manual processes, redundant work effort, or error prone with defined repeatable processes. The results will likely be a strong short list for initial projects. Start with proofs-of-concept, move to minimally viable products/processes?(MVP), and then launch into?internal production environments.?
5. Predictive Analytics for Decision Support
Begin developing expertise in AI-driven predictive analytics for forecasting. Finance and Sales are likely doing some form of this now for cost and revenue projections. Create benchmarks to compare future efforts against and log progress. Be realistic. This step may present more challenges for less data-driven organizations or teams as it is very much a "garbage in, garbage out" situation.
6. Customer Experience and Personalization
Using AI to add personalization to the customer journey is getting a lot of attention for the potential to increase satisfaction and loyalty. This is a great opportunity to ask your customers how your products and services could be improved. Provide methods of input so their voice is heard. Then leverage AI to enhance areas that improve the?experience through personalized interactions, recommendations, and services.?Bring teams from Product, Marketing and Customer Support together to collaborate and discuss opportunities and data needs.
7. Generative AI Applications
Develop an understanding of the capabilities and limitations of Gen AI models in creating multimodal outputs, content, designs, code, and new ideas. Build a base of knowledge?about?how to apply these outputs safely, creatively and effectively while maintaining brand integrity. A leadership supported document that defines acceptable use for Gen AI,?similar to?brand guidelines, can be an effective tool for managing focus during a time of experimentation.?
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8. Appropriate ROI Models for AI
Adoption and deployment of AI is very much an iterative process. Early AI efforts are unlikely to deliver a positive ROI, but key learnings will be uncovered. I’m an advocate of Emerj AI Research’s ROI Trinity model for early-stage projects. Consider adopting the model to assess your early efforts. Set milestones for strategic objectives that align with organizational priorities. Assign values for achieving milestones as a proxy for hard dollar ROI. Developing AI competencies and skill building increases the value of employees. Consider such increases in high-value skills as a component in an ROI calculation.?And where there's an element of financial or measurable ROI, set proper expectations. Results may be some time off or?achieved with different approaches, but establishing ROI success metrics early helps keep focus on what's important and evaluate longer term efforts.??
9. Continuous Learning and Adaptation
Encourage creation of a culture of continuous learning and innovation where AI learning approaches are regularly evaluated, updated, and knowledge shared. This includes staying abreast of advancements in AI and adapting strategies and tactics to new capabilities. Form Centers of Excellence or AI workgroups to facilitate knowledge sharing and allow for failing forward in the pursuit of developing AI capabilities.?Do you have key partners or strategic clients? Those organizations are likely in the same situation. Invite them to collaborate and share experiences, insights and learnings.
10. AI Transparency and Explainability
Strive for AI systems that are transparent and outputs that impact decisions can be explained in clear terms. This is important for building trust with users, regulators, customers and all stakeholders. Developing trust in AI outputs is an essential step towards not only customer loyalty, but also between internal groups and external partners.?Consider an AI?registry or more robust governance?platform to help your organization capture, document and manage efforts.?It's an order of magnitude easier to begin documenting AI efforts, models, algorithms, etc. earlier than attempting to go back and recall what the original intent was. (See the previous article about AI Model Cards.)
11. Sustainability and Social Impact
Consider the environmental and social impacts of AI technologies. Are connections to sustainability and social impact an integral component of your organization's brand values? While improvements are happening, AI consumes significant resources in its use of computing power. Avoid the temptation to “AI wash” messaging with thin claims of benefits delivered by AI. Be true to your commitment. This includes committing to sustainable and socially responsible AI practices to build a positive future for your organization, people, partners and all stakeholders.
Concluding Thoughts
?I've been fortunate to have been in digital marketing and business since the dawn of the commercial internet ('96). The emergence of technology in marketing has been formidable and with absolute certainty I can say, "AI isn't your father's martech." With AI the stakes are getting higher. A lot higher. For digital-first growth initiatives, thriving in the rapidly evolving landscape requires a balanced approach of curiosity, experimentation, cross-functional collaboration and risk management. Done well, the upside can be tremendous.
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