Business & Technology
Why enterprise AI strategies need an operating model
Every boardroom has an AI strategy. Executives are talking about productivity gains, customer experience improvements, and competitive advantage.
Investment is flowing while expectations rise. Yet a familiar story is emerging – AI initiatives are generating excitement, but not always outcomes.
The real issue here is the widening gap between AI strategy and AI execution. Many organisations are still approaching AI as a technology project rather than a business transformation initiative.
Such a distinction may determine whether an organisation creates lasting value or simply accumulates a growing collection of disconnected pilots.
How can organisations implement AI properly?
For most enterprise leaders, the first phase of AI adoption has been relatively straightforward. Teams identify a promising use case, deploy a pilot, generate encouraging results and build confidence internally.
However, scaling that success across the wider organisation proves considerably harder. The pilot works, but the business doesn’t change.
This is where many strategies stall. The technology works, but does it fundamentally improve the way work gets done?
That distinction matters because AI doesn’t operate independently. It inherits existing workflows, governance models, approval processes and organisational structures. Fragmented foundations mean AI multiplies existing issues.
If you’re automating a bad process, you still have a bad process. It’s just accelerating something that’s bad in the first place.
This should serve as a warning. AI can’t be treated as a shortcut around operational complexity. Value is created when organisations are prepared to change.
Is there a hidden cost of AI ambition?
Growing fragmentation is a common symptom of a widening execution gap. There’s a central AI strategy, but execution often becomes decentralised.
Departments select their own tools, and teams experiment with different platforms. Individual users develop their own workflows.
On the surface, it’s innovative. In the long-term, it creates confusion.
Governance becomes inconsistent as data flows become harder to control. Soon, it’s harder to maintain visibility, while business units pursue conflicting priorities. ‘Shadow AI’ emerges, introducing new risks alongside new opportunities.
All that’s being achieved is heavy AI investment in an increasingly complex environment.
Access to technology doesn’t translate to AI maturity. Enterprise models and generative AI tools are widely available. Now, competitive advantage is determined by how effectively organisations embed those capabilities into their operating model.
The winners are the organisations creating the conditions for AI to scale safely, consistently and predictably.
Why has governance become a competitive advantage?
Historically, governance was often viewed as a barrier to innovation. In the AI era, it’s an execution enabler. It’s something highly regulated industries have understood for some time.
Healthcare providers must balance innovation with patient safety. Financial institutions operate within strict compliance requirements. Public sector organisations face intense scrutiny around risk, transparency and citizen data protection.
The same principle is increasingly being applied across every sector.
Without clear ownership, AI programmes struggle to move beyond experimentation. Organisations become trapped in what many leaders now describe as ‘pilot purgatory.’ A constant cycle of testing, learning and proving value without ever reaching meaningful scale.
The strongest AI strategies are the ones that start with operating models.
Enterprise leaders should be asking:
- Who owns AI outcomes?
- How are use cases prioritised?
- What governance framework supports deployment?
- How will success be measured?
- How will AI integrate with existing workflows and decision-making structures?
Technology remains crucial, but execution is ultimately an organisational capability.
Does less mean more?
Many enterprises respond to AI pressure by increasing activity. More pilots, more proof-of-concepts, and more experimentation.
Ironically, the organisations making the greatest progress are often doing the opposite.
Successful enterprises typically focus on one high-value use case and execute it thoroughly. It starts with governance and security and ends with workflow redesign and adoption.
Rather than proving dozens of concepts, the focus is on operationalising one. The result is a repeatable blueprint.
The experience gained through one successful AI initiative becomes the foundation for broader transformation. Teams learn how decisions are made, how risks are managed and how adoption is achieved.
Future deployments become faster, more predictable and more impactful.
Preparing for agentic AI
The urgency surrounding the execution gap becomes even more apparent when considering where AI is heading.
Enterprise AI is evolving beyond copilots and task automation. Organisations are increasingly exploring agentic systems. These initiatives make decisions, coordinate activities and interact with other systems – all with limited human intervention.
These capabilities promise substantial gains in productivity and operational efficiency. They’re also increasing organisational complexity.
How can enterprises manage a network of autonomous agents if enterprises struggle to govern a single AI deployment today?
For enterprise leaders, this is perhaps the most important consideration of all. That’s why AI readiness is about execution capability.
Are you closing the gap?
The organisations that succeed with AI are unlikely to be those pursuing ambitious roadmaps. They’ll be the ones mastering execution.
AI would be treated as an operational transformation programme rather than a technology investment. Processes are redesigned before being automated, with governance established before scaling.
It’s all about outcomes before activity.
AI’s greatest challenge is embedding intelligence into how an organisation works. The AI execution gap is real, but it’s a leadership challenge rather than a technology problem.
The enterprises that close it first will be the ones best positioned to realise AI’s full potential.
Speak with Gamma Communications today to learn more about closing the AI execution gap.
Business & Technology
Network Rail will not reopen Botley Road early despite completion
Gas network company SGN confirmed it had repaired three minor gas leaks and left the site on Monday, August 3, six days earlier than expected.
The leaks were discovered during excavation works last month and contributed to the pushing back of the road’s reopening date, yet again, to September 20.
The completion of the gas mains replacement marked a significant step forward in the wider Oxford Station improvement project, which was originally budgeted at £161 million but is now expected to cost at least £237 million.
The development prompted hopes that Botley Road, closed beneath the rail bridge since April 2023, could reopen earlier than planned.
However, Network Rail has moved to manage expectations, saying the project remains on course to meet its existing target date rather than finish ahead of schedule.
A Network Rail spokesperson said: “We’re pleased that SGN has completed its gas mains replacement work.
“While this is an important milestone, it doesn’t necessarily mean the overall project will finish early as some remaining work is dependent on access to the railway, which we have had to rearrange to enable the replacement of the gas main.
“Our focus remains on meeting our planned deadline of 20 September for reopening Botley Road to traffic.”
While the completion of the gas works removes one of the most recent obstacles facing the scheme, Network Rail says further work under the bridge and around the station is still needed before the route can reopen to traffic.
Business & Technology
40-year-old Oxfordshire gymnastics club at risk of closure due to heat
The club is currently struggling in the summer heat, and has launched a new fundraiser to keep its gymnasts safe.
The club, which is based at Grove House Barn near Warkworth in Banbury, launched the fundraiser so it could buy and install four air conditioning units to keep its space cool.
Currently, the club hopes to raise £7,000 through the appeal so it can buy four 10kW air conditioning units and cover all the installation costs.
So far, the club has raised £380.
Karl Wade, director of Wade Gymnastics, said the club has become “increasingly warm” during the summer months due to the rising temperatures.
READ MORE: Thames Water leakage targets are ‘not realistic’ says boss after pay rise
Wade Gymnastics at Grove House Barn in Banbury (Image: Google Maps)
“Despite our best efforts to keep doorways and shutters open, it becomes very uncomfortable for gymnasts to play and train,” Mr Wade said.
He added: “The safety of our gymnasts and coaches is always our utmost priority.
“Unfortunately, the risk of having to close the business during these hot spells is increasing and we need to have more effective ways of keeping everyone cool.
“An air conditioning system would allow the business to stay open during those extreme hot conditions and continue to provide classes for everyone who attends.”
The gym currently delivers classes seven days a week for around 900 people, which range from toddlers to athletes competing at national level.
The gym club was founded more than four decades ago by Ruth Wade and, for the past 20 years it has been based at its current facility.
Business & Technology
Solihull Council appoints ICS.AI for AI discovery phase
SOFIAH NICHOLE SALIVIO
News Editor
Solihull Council has appointed ICS.AI to deliver the first phase of an AI Transformation Discovery programme to examine how artificial intelligence could be used across several resident-facing services.
The 24-week programme will review opportunities in Adult Social Care, Children’s Services, Economy & Infrastructure, and Public Health. It is intended to help the council decide where AI could be used and where future spending should be directed.
In this first phase, ICS.AI will assess the council’s readiness for AI and identify use cases across the four service areas. The programme is expected to produce a prioritised shortlist of about 200 use cases, including 50 validated from a finance perspective, alongside a longer-term AI Transformation Roadmap.
The work is intended to create an evidence base before any wider implementation decisions are taken. Ethics, privacy, and safeguarding will be considered throughout the assessment process.
Discovery phase
ICS.AI will use its AI Target Operating Model framework to review Solihull’s current position across five dimensions before ranking opportunities. The outputs will be based on council-owned baseline data and reviewed by public sector specialists.
The approach reflects a broader pattern among local authorities exploring AI in service delivery while facing pressure to justify spending and manage risks around data use and public accountability. Councils have also been seeking clearer business cases before committing to larger technology programmes.
Solihull said the discovery exercise would support a measured approach to service modernisation. The authority wants to identify where AI could improve services for residents while also demonstrating value for money.
“We are committed to taking a well-considered and planned approach to modernising the services we provide. By building a strong evidence base for future decisions, this programme will help us understand where the greatest AI opportunities exist. We will then be able to prioritise those improvements that will deliver the greatest benefit for residents, while ensuring full value for the council,” said Councillor Dave Pinwell, Cabinet Portfolio Holder for Resources, Solihull Council.
Public sector focus
ICS.AI said the Solihull engagement builds on work it has carried out with more than 20 public sector organisations using its AI transformation and discovery assessments. Those organisations include Derby City Council.
The company focuses on AI projects for the public sector, where interest has increased as authorities look for ways to manage demand pressures in social care, public health, and other frontline services. At the same time, councils are under scrutiny to show that new technology investments are proportionate and supported by practical evidence.
Dwayne Johnson, Chief Local Government Officer at ICS.AI, said local authorities need stronger justification before committing funds. “Local authorities need confidence that every investment is backed by robust evidence and long-term value for residents. Solihull Council is taking the right approach by starting with a structured discovery programme that builds a clear understanding of priorities before decisions are made. By developing finance-validated business cases and a practical roadmap, the council can be more proactive in the decisions it makes,” he said.
The programme’s initial outputs are expected to give Solihull a ranked view of where AI could be applied across services, the level of organisational readiness, and which projects may warrant further consideration. This first phase is focused on identifying options rather than moving directly into deployment.
For local government leaders, that distinction is becoming increasingly important as councils test AI in areas that affect vulnerable residents and essential public services. In Solihull’s case, the work spans some of the authority’s most visible functions, including care services, children’s provision, public health activity, and parts of local infrastructure planning.
The council aims to use the findings to inform later investment decisions through finance-validated business cases and a practical roadmap for future priorities.
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