To Optimize Business Outcomes, Shift your Focus from Technology to Tangible Business Gains using AI-ready data
As the data industry evolves, businesses are seeking practical methods to make their data AI-ready and cultivate a data-informed culture among executives. Storytelling remains a crucial tool for CDAOs and CIOs to engage with executive leadership regarding strategic business enhancement.
Once a business has achieved good data hygiene, it's time to make the data AI-ready. Robust governance is the key to unlocking innovation in artificial intelligence. Organizations must embrace the concept of data products derived from the data mesh to best utilize data in AI systems.
The emphasis on AI at the corporate level is clear. Gartner reports that 62% of companies have discussed AI and governance at the board level, underscoring the need for CIOs and CDOs to guide their organizations in defining their AI strategies. Companies that prioritize AI as a strategic tool outperform their peers 80% of the time, correlating to a notable 30% difference in net income according to Gartner.
The path to becoming "AI ready" requires organizations to embrace good governance to innovate securely, establish a clear AI vision, and extend existing governance practices. For CDOs and CIOs, the goal is to foster a digitally savvy culture, elevate internal AI training, and promote the value of collective intelligence as a cornerstone of value creation.
Leaders in data and analytics must enhance governance maturity and align their strategies more closely with business outcomes. Taking a strategic focus to AI is proving to be a significant advantage.
AI and Customer Analytics
The competitive edge lies in uncovering insights about customer expectations and marketers should pivot to harnessing the minimum viable customer data, focusing sharply on personalizing customer experiences and supporting decision-making.
It is important to understand direct, indirect, and inferred customer behaviors and expressions. Journey analytics are crucial in this process for capturing the voice of the silent customer, revealing the unspoken preferences and behaviors along their multiple;tichannel journey. Challenges abound when customers encounter fragmented experiences, such as unrecognized purchase histories or the need to rebuild shopping carts which are issues requiring integrating siloed data across touchpoints.
AI's potential in customer experience (CX) is especially great as it plays a key role in enhancing customer retention and driving revenue growth. AI can streamline sales by handling administrative tasks, thus saving time, and allowing for a more focused customer approach. In customer support, generative AI is also proving to be a powerful tool for increasing agent productivity.
Digital and Machine Based Customers
Creating a digital twin of the customer presents a new way to foster personalized customer experiences, anticipate needs, and strategize improvements and innovation. It creates a dynamic virtual model that is constantly updated with interaction data and emulates and predicts customer behavior.
In addition, as the technology matures, machines will not just be tools but participants and customers in their own right. Machines will participate in economic transactions, leading marketers to consider how their strategies might shift where machines autonomously execute and even initiate, transactions.
Data Driven Change Management
Do your company's data leaders truly know the company's current position, direction, and who is truly aligned with the vision?
Can they craft compelling data-driven narratives, confronting resistance and embodying the change they wish to see. Successful leaders are visionary and empathetic but also actively foster relationships, build community, and communicate the tangible benefits of change to both their organization and individual stakeholders. The data leader should be a risk-taker, be transparent, and always align their actions with a forward-looking vision that motivates collective and individual progress.
Value to the CEO
Data leaders without question need to capture their CEO's attention. For some, this means stepping up from being a service provider to becoming a trusted adviser especially as CEOs recognize AI as a force poised to reshape their industry. This transition requires a genuine dialog that uncovers CEO perspectives, biases and values.
Effective conversations are the bedrock for understanding a CEO's values and for formulating an appropriate data and analytics vision. Data leaders should be visionary but focus on CEO drivers such as performance, quality, cost efficiency, time management, loyalty, grit, experience, passion, and respect. To engage the CEO and other stakeholders, data leaders should offer real strategic choices and hone their art of listening actively.
In this process, they should be prepared for a test of your ideas and comprehending the upspoken meaning of the CEO. Knowing a CEO’s decision-making style is the first step. Here it is important to adapt your communication and to keep their attention about the future and the role of technologies like AI.
A Data Playbook for Generative AI
Generative models augment human creativity and decision-making and analytics are pivotal to weaving AI into the fabric of business operations. The pressing question today is not whether to adopt AI, but how to strategically implement it. Is it for enhancing productivity, transforming customer interactions, or innovating products and services.
The playbook for generative AI strategy should be built upon synchronicity with business goals. This means evaluating AI's potential to impact business model. Doing this involves asking about business priorities, potential areas needing improvement, competitive issues, and vulnerabilities to disruption. With this, prioritize investment and ensure strategic alignment.
Generative AI, distinct from machine learning, is not about automating tasks but about augmenting human capabilities. The roadmap for integrating Generative AI, therefore, requires a clear vision and alignment with the business's innovation goals and critical needs. Part of doing this well involves creating a more cohesive governance structure, AI ethics, and comprehensive AI education. The strategy should be built for speed including a composable ecosystem to effectively execute and scale. It should, also, include creating AI-ready data, establishing robust AI engineering, and a proactive change management.
At its core, the playbook requires talent, skills, and effective change management. Risk management should extend beyond technical risks to include intellectual property, reputation, fraud, malware, and ethical considerations, ensuring comprehensive governance of value, cost, and risk. Ultimately, a mature AI organization's strategy is characterized by a diversified portfolio of measures and sophisticated attribution models.
Generating Data and Analytics Value
The increasing complexity of use cases and the growing demand for data access and agility proves there is a healthy appetite for progress. With cloud costs surging, there is a shortage of data engineers to meet business needs. For CIOs and CDOs, the pressing questions revolves around the effectiveness of their effort, the suitability of their organizational models, their alignment with business outcomes, and whether their metrics adequately track the construction, operation, and expansion of their data architectures.
As businesses navigate generative AI and its implications for data roles, centralized models are being reevaluated in favor of those that directly link data management funding with the delivery of business value. start with existing assets, rigorously measuring outcomes, and addressing capability gaps. The investment in data management is a top priority. Smart data leaders combine a clear understanding of current capabilities, a commitment to aligning architecture to business value, and a funding model that fosters data literacy and management of skills across business domains.
2024 Data Predictions
CDAOs will drive 80% of major decisions, marking their evolution from data stewards to key competitive players. However, CDAOs who fail to achieve organization-wide influence are likely to be relegated back into traditional technology roles by 2026. You can avoid this fate by focusing on organizational priorities, deeply understanding the business, and communicating successes in business language. At the same time, the research highlights the risks associated with intellectual property and copyright infringement in the burgeoning field of generative AI. For this reason, data leaders should carefully select use cases, the risk monitoring, and the governance.
Looking forward to 2028, data leaders should invest in data literacy and AI programs, necessary to harness the full potential of AI. Additionally, governance should be rebranded as business enablement. It should encourage a shift from a command-and-control to one an approach that supports strategic business initiatives. Meanwhile, the challenge of building large language models from scratch is likely to lead many enterprises to abandon these efforts in favor of more manageable solutions. GenAI is expected to be at the forefront of transforming content.
2024 CDAO Agenda
Good governance is not just a nicety — it’s an imperative. Eighty-nine percent of those surveyed say robust governance is critical to innovation, delivering value, and ensuring data consistency and flexibility. With this said, many concede they're missing essential elements to support innovation and AI developments.
Governance is spotlighted as crucial for prepping AI-ready data. By 2027, 40% of CDAOs will pivot governance to act as a catalyst for strategic business initiatives.
Adapting swiftly is the clarion call for CDAOs in the face of generative AI's ascension. While many are gearing up for the generative AI wave, a startling two-thirds lag in AI-readiness. Gartner predictions state that by 2027, three-quarters of new analytics will be integrated with intelligent apps through generative AI.
Although 74% of CDAOs affirm that they meet expectations and enjoy executive confidence, there’s a stark contrast in the organizational culture and performance metrics, with less than half showing a conducive environment for outcome-driven analytics. For this reason, the future CDAO must become a multifaceted leader who fosters data and AI literacy, drives culture change, and curates a skilled workforce. The message is expanding influence by building relationships, proving business value, and magnifying successes to secure necessary resources.
Upskill and Reskill Your Data, Analytics and AI Employees
In the rapidly evolving data landscape, the catalyst for data and AI skills development lies at the intersection of technology advancement and business strategy. Organizations thrive when their strategy propels their business objectives forward. The modern enterprise demands a workforce that is not just technically proficient but also adept in business acumen and soft skills. This fosters a robust data-driven culture.
The path to crafting these hinges on clear vision and leadership, which drives business transformation. Societal trends, industry shifts, and internal technological advancements necessitate a dynamic response. The ability to measure success with precise goals and metrics, manage a portfolio that aligns with the business’s value proposition, and ensure stakeholder outcomes are paramount.
However, transformation is not without its hurdles. Skill and staff shortages are a significant barrier, often exacerbated by a lack of resources and the inertia of cultural resistance to change. To overcome these roadblocks, a systemic approach to skills development is essential. The process begins by identifying the data capabilities your organization needs. Ultimately, the strategy culminates in the formulation of a skills development portfolio. Human Resources collaboration is needed to develop enterprise-wide skills and a development program. Through a structured approach, the function can become a powerful driver of business success, equipped to navigate, and leverage the digital landscape.
Importance of Data Governance
The role of governance in business lies in evolving from traditional models to ones focused on business outcomes. Governance should not just be a set of rules but a framework for driving value, ethical transparency, and trust. Organizations should forge an environment where data valuation, creation, consumption, and control are balanced with accountability, risk management, and digital ethics.
Modernizing governance requires a strategic and collaborative approach. Organizations must benchmark their governance health against best practices, anticipate how emerging digital business scenarios will affect data, and pivot to an adaptive governance model. This transformation is not a solo endeavor; it calls for rallying champions and rewarding governance that propels digital advancement while aligning with business imperatives.
Parting Words
As businesses emphasize and place pressure on generative AI's role in delivering business value, emphasizing the need for alignment between business and technology, CIOs and CDAOs are tasked with creating the corporate future in partnership with business leaders, particularly CEOs and CMOs. It's not just about cutting-edge technology but about driving business change and delivering clear-cut business value.