Artificial intelligence (AI) has fundamentally changed how organisations think about data, technology and business capability.
Organisations are exploring new ways to improve productivity, enhance decision-making and create business value.
But as investment in AI grows, an important question to ask is:
Is your existing data strategy equipped to support what your business wants to achieve next?
For many organisations, the answer may be yes. Others may find that evolving requirements have introduced new considerations around architecture, governance, investment and internal capability.
The original strategy, if you had one, may not have been wrong. It’s that the environment may have changed.
ADAPT’s research highlights the challenge, finding that only 41% of data leaders are confident in their ability to meet their organisation’s data needs.
This reinforces the need for organisations to evaluate their existing capabilities and priorities, to ensure AI investment and data strategy align with broader business objectives.
AI is Changing What Organisations Need From Their Data
Traditionally, many enterprise data strategies have focused on consolidating information, improving reporting, supporting analytics and establishing reliable data platforms.
These remain important objectives. However, AI introduces additional expectations.
Data may need to be accessible to new applications, support more automated decisions, integrate across different environments and meet increasingly complex requirements for quality, security and governance.
For example, an organisation introducing AI-powered customer service may need to consider how its applications access operational data, whether that information is sufficiently current and reliable, and what controls govern its use.
Similarly, a business expanding self-service analytics or AI-assisted decision-making may need to reconsider how data access, semantics, ownership, and accountability operate across departments.
These changes don’t automatically require a new data platform or strategy. But they do warrant examining whether existing capabilities remain fit for purpose.
Technology Investment and Data Strategy Are Not the Same Thing
When new business requirements emerge, technology investment is often part of the response.
A new AI platform, data integration tool or analytics application may provide valuable functionality.
However, introducing additional technology does not necessarily resolve broader questions about data capability.
Research from ADAPT reinforces the importance of this distinction, reporting that CFOs estimate approximately 40% of technology spending is wasted. This raises important questions about whether technology investments are sufficiently aligned with business needs and delivering their intended value.
Consider the difference between having the technology to make data accessible and having the organisational capability to use that data effectively.
The latter depends on more than platforms. It also involves:
- Clearly defined business objectives and priorities
- Appropriate data architecture and integration
- Reliable data quality and governance
- Ownership and accountability
- People, skills and operating models
- Sustainable investment and operational practices
An organisation may already have much of the technology it needs, but require changes to processes, responsibilities or how existing capabilities are used.
Equally, new technology investment may be entirely justified where current platforms cannot support emerging requirements.
The important distinction is that technology decisions should follow business requirements and strategic priorities, rather than define them.
5 Areas To Reassess
1.
Business Alignment
What outcomes are AI and data investments expected to deliver?
Whether the objective is operational efficiency, improved customer experiences, faster decision-making or new revenue opportunities, there should be a clear connection between investment decisions and business priorities.
This also means considering how success will be measured and whether the organisation has the data capability required to achieve those outcomes.
2.
Data Architecture
Can the existing architecture support changing requirements?
AI initiatives may introduce new demands for data integration, accessibility, performance, scalability and interoperability.
Some organisations will find that their current architecture can accommodate these requirements with relatively minor adjustments. Others may need to address limitations or reconsider particular components.
The objective should be to understand what is required before committing to significant architectural change.
3.
Governance & Accountability
As data becomes accessible to more users, applications and AI systems, questions of ownership and appropriate use become increasingly important.
Who is responsible for data quality? Who determines which information can be accessed and for what purpose? How are privacy, security and compliance requirements managed?
Effective governance should enable responsible use of data while providing appropriate oversight and control.
4.
People & Operating Models
New technologies can change how data teams work and how responsibilities are distributed across an organisation.
AI and self-service capabilities may reduce some manual activities while introducing new requirements for engineering, governance, platform management and specialist expertise.
Leadership should consider whether existing skills, resources and operating structures remain appropriate for the organisation’s future direction.
5.
Investment & Value
Before committing to additional technology, it is worth examining how effectively existing investments are being utilised.
Are current platforms delivering the expected benefits? Are capabilities duplicated across departments? Could changes to processes, integration or skills unlock greater value?
In some cases, the most effective next step may be optimisation rather than further investment.
In others, targeted investment may be necessary to support new business requirements.
Reassessing Your Strategy Doesn’t Mean Starting Again
A data strategy should provide direction, but it should also remain responsive to changing business needs.
Reassessment does not necessarily mean replacing existing platforms, restructuring teams or embarking on a major transformation programme.
It may confirm that the current direction remains appropriate.
It may identify a small number of changes that would improve efficiency, governance or readiness for new initiatives.
Or it may highlight more significant decisions that require consideration over the next several years.
What matters is understanding the current position, evaluating it against future requirements and making informed decisions about where to focus effort and investment.
Making the Next Data Decision with Confidence
AI is creating opportunities for organisations to use data in new ways.
At the same time, investment scrutiny, governance expectations and evolving business priorities make it increasingly important to understand whether existing data capabilities can support those ambitions.
Before making the next major data or AI investment, leadership teams should be confident that their decisions are aligned with business objectives, informed by their current capabilities and based on a clear understanding of future requirements.
An independent perspective can help organisations validate their existing direction, identify areas that warrant attention and establish practical priorities for what comes next.
Independent Advice for Your Next Data Decision
Data Army’s Data Strategy & Capability Review provides an independent assessment of your current data direction and capability, with practical recommendations and a prioritised roadmap.
Whether you’re validating existing decisions or considering new investments, our experienced advisors can help you determine what deserves attention.