Just a few years ago, the phrase “database query” was enough to make most people’s eyes glaze over. It belonged to the world of IT departments, data analysts, and computer‐science students hunched over glowing screens. But the arrival of systems like ChatGPT has quietly pulled the writing of database queries into the cultural mainstream. What was once a task reserved for specialists is now something almost anyone can attempt simply by typing a sentence.
This shift may sound small, even trivial. Yet beneath the surface it signals a profound transformation, one that reaches deep into workplaces, education, public services, and the digital competence of the UK population. In this commentary, written from my perspective as a UK academic council member, I explore not merely what ChatGPT can do, but how its newfound ability to write database queries reshapes everything from employment and productivity to trust, governance, and the meaning of digital literacy.
The UK is currently grappling with questions about AI adoption, technological competitiveness, and the upskilling of its workforce. The capability of AI systems to write database queries—accurate, complex, and tailored to real business logic—may be one of the most underestimated developments with implications for every citizen. It is a change that deserves our full attention.
Before discussing ChatGPT, it is worth reflecting on what a database query represents. Most data stored in organisations—whether in the NHS, Tesco, HMRC, or a local council—sits inside structured systems. These systems can only be “talked to” through formal languages, most commonly SQL (Structured Query Language).
Even if an organisation has sophisticated dashboards, the essential work of extracting new insights, running bespoke reports, or validating unexpected patterns often requires someone who knows how to write a query.
The importance of such queries cannot be overstated:
They determine which patients appear on a waiting-list review.
They define which transactions are flagged as suspicious.
They generate the performance indicators used to judge a department’s success.
They guide both policy decisions and business strategies.
In other words, database queries are the hidden plumbing of modern decision-making. They are not glamorous, but they govern much of the information flow in the UK economy. Understanding this gives us a sense of the significance of an AI tool that can now write these queries fluently.
When a user types, “Show me all customers in Glasgow who purchased more than £200 of electronics last month,” ChatGPT translates this natural-language instruction into the formal structure of a SQL query.
This translation is more than pattern-matching. A correct query requires understanding:
tables
relationships
date functions
grouping logic
joins
filters
aggregation
business context
For many technical staff, writing such queries is a core competence developed over years of experience.
ChatGPT’s ability to generate them instantly changes the dynamics dramatically.
Where a junior analyst might take half an hour to puzzle through a join, ChatGPT can produce a ready-to-run query in seconds. It can also explain the query line-by-line, check logic, rewrite it in a different style, or convert it between SQL dialects used by systems such as MySQL, PostgreSQL, SQL Server, or Oracle.
Even more importantly, ChatGPT can act as a troubleshooting assistant. A user who encounters an error—perhaps a missing comma or an ambiguous column name—can paste the message in and receive a clear explanation and correction.
A domain that once required technical fluency can now be navigated conversationally.
One of the most profound consequences is the empowerment of non-technical staff.
Across the UK—inside councils, charities, retail operations, SMEs, and public services—staff who previously relied on overworked IT teams are discovering that they can access and analyse their own data. Instead of logging a support ticket and waiting days or weeks for someone to extract a report, they can ask ChatGPT to generate the query themselves.
This shift does not mean people will suddenly become database administrators. Databases still require permission systems, safeguards, and oversight. But it does allow individuals to experiment, learn, and iterate without fear.
The implications include:
Teams can test ideas, explore patterns, and validate assumptions in real time.
Pressure on IT support teams decreases, allowing them to focus on strategic rather than reactive work.
Employees who once felt intimidated by data tools can now explore them with conversational support.
A business does not need a large team of highly paid analysts to begin using data creatively.
For many UK organisations—especially those in the voluntary sector or in rural areas with limited tech staff—this is potentially transformative.
While AI tools reduce the barrier to entry, they also create a new form of skill differentiation. The question is no longer “Can you code?” It has become “Can you prompt?”
The expertise now lies in:
describing the logic clearly
specifying data constraints
anticipating edge cases
validating that results make sense
aligning queries with organisational definitions
Those who can articulate their needs precisely will get better results. Those who cannot may generate misleading queries that appear correct but encode subtle misunderstandings.
In this sense, ChatGPT changes the digital divide rather than eliminating it. Instead of relying on syntactic knowledge of SQL, individuals must rely on conceptual clarity and critical thinking.
As educators and policymakers, we must respond by teaching not only traditional coding skills but also:
data reasoning
logic
analytical literacy
prompt engineering
verification techniques
The UK’s future competitiveness depends on these forms of literacy.
Proponents of AI often highlight increased productivity. ChatGPT undoubtedly accelerates query writing. But speed is only one part of the picture.
For experienced analysts, ChatGPT functions like an intelligent pair-programmer. It helps with boilerplate, catches errors, suggests optimisations, and rewrites code in cleaner forms.
Without domain knowledge, the user may not know whether the output is logically correct—even if it is syntactically valid.
For example, a query may:
double-count records
use an incorrect join
rely on ambiguous business definitions
exclude nulls unintentionally
use a date format inconsistent with UK standards
ChatGPT does not yet possess full understanding of organisational semantics. A human must still validate the results.
This creates a paradox: ChatGPT accelerates work, but only users who understand their data deeply can use it safely.
In UK public life, trust is an increasingly scarce resource. Whether discussing the NHS, policing, data privacy, or elections, the public is rightly cautious about the role of AI.
When an AI system writes queries that inform real decisions, the stakes are high.
A mis-written query could:
exclude vulnerable patients from a waiting-list review
incorrectly calculate school performance statistics
misidentify benefits fraud
generate misleading environmental indicators
AI does not remove responsibility. It enhances the need for:
transparent governance
audit trails
reproducibility
human verification
ethical oversight
As a nation, we must resist the temptation to think of ChatGPT as a magical oracle. It is a powerful tool, but one that amplifies both competence and error.
The fear that AI will eliminate jobs is common, and for some roles the risk is genuine. Query writing is a core activity for many junior analysts and data engineers. If ChatGPT performs this task instantly, what remains for them to do?
Several dynamics are emerging:
Positions focused on repetitive reporting or boilerplate queries will likely be reduced. This is similar to how spreadsheets reduced the need for manual ledger work.
The UK will need more:
data stewards
model auditors
AI supervisors
prompt specialists
ethicists
systems integrators
domain-savvy analysts
Organisations need people who understand:
context
compliance
risk
operational nuance
organisational history
the messy reality of data
AI accelerates technical execution but cannot replace institutional knowledge.
As a member of a UK academic committee, I see daily the challenge of preparing students for a world where AI now writes code. Universities must rethink curricula. Colleges and training programmes must adapt. School digital literacy must evolve.
We need a national conversation about how AI changes what we teach.
Yes—because understanding structure enables validation. But the emphasis should shift from syntax to reasoning.
Absolutely. It is the new user interface for complex systems.
More than ever. Every query is a decision encoded.
We must, because it is already part of professional practice.
The UK can either lead or fall behind. At present, our adoption is uneven. We have pockets of brilliance, but also widespread uncertainty. A coordinated approach—across universities, FE colleges, and industry—is essential.
The ability of ChatGPT to generate operational queries touches on legal and ethical responsibilities. The UK must ensure:
clear responsibility for decisions
audit trails of generated queries
logging of AI-assisted outputs
transparent documentation
appropriate human oversight
Regulators will increasingly need to assess not just outcomes but processes. If an AI-written query influenced a decision affecting citizens, can we trace why?
We must design systems where AI enhances accountability, not obscures it.
ChatGPT helps clinical coders, analysts, and managers explore datasets without heavy technical dependency. But it also introduces risk if misused. The NHS requires rigorous oversight.
Universities are already using AI for enrolment data, student retention analysis, and scheduling. ChatGPT slashes the time required to produce custom extracts.
Large chains use AI-generated queries for stock optimisation, sales forecasting, and customer segmentation. SMEs appear especially empowered.
Councils, often stretched thin, can perform data analyses previously requiring specialist hires.
Across sectors, the theme is similar: empowerment paired with responsibility.
Something subtle—but significant—is happening. As queries become conversational, data becomes more accessible, and the British public begins to see information not as an intimidating technical domain but as a navigable landscape.
The democratisation of database access may:
increase public engagement
reduce fear of technology
foster better decision-making
encourage creative experimentation
support a more data-savvy society
In a world increasingly defined by information, this cultural shift may be one of the most valuable outcomes.
For everyday Britons, whether working in an office, running a business, or studying, three pieces of advice matter:
Prompting is a communicative skill.
Validation is essential.
The combination of human insight and AI speed is the winning formula.
The UK government frequently speaks of its ambition to be an AI superpower. True leadership does not come from building the largest models—it comes from integrating AI responsibly and creatively across society.
ChatGPT’s ability to write database queries is a microcosm of a much larger challenge. It forces us to re-examine:
skills
governance
ethics
productivity
trust
education
If we build the right frameworks, we can create a society where AI amplifies human capability and strengthens public services. If we ignore the risks, we invite uncertainty and distrust.
The future is not predetermined. It depends on choices we make now.
Database queries may not sound like the battleground of the future, but they sit at the heart of how decisions are made. ChatGPT’s fluency in generating them marks a turning point. It brings power to the many, not just the technically trained few.
If we embrace this change, while preserving human oversight and institutional wisdom, the UK can move forward with confidence—smarter, faster, and more inclusive. If we treat AI as a shortcut rather than a tool, we risk deepening inequalities and eroding trust.
Ultimately, the question is simple:
Will we let AI think for us, or will we use it to think better?
The answer will define the next decade of our technological society.
