Lessons of the Recent Past – the impact of strategy in Scaling Digital Solutions, must be Remembered in the AI Age
For our public services to truly benefit from AI there’ll need to be a significant maturing in the current conceptual narrative about the technology and in the practical approach to its deployment. Put another way, there’s a massive difference between developing the right data foundations to scale AI to solve society’s biggest problems versus just doing stuff with AI. Fortunately, lessons can be drawn from recent transformation history; doing stuff with websites didn’t create the same outcomes as building modern foundations for meaningful digital transformation.
No increase in data maturity = No safe, scaled AI.
No strategic data foundations = No safe, scaled AI.
No strategic, modern data platform = No safe, scaled AI.
learning the lessons of ‘tactical digital’
20 years ago, public service organisations were ramping up their digital ambition and activity, websites were viewed as the hinterland for this work. Some organisations led the way with strategically focused investment in the right ‘digital front door’. An approach that was designed around clearly defined user needs and enabled by modern technical principles and standards. However, many other organisations – most? – approached web modernisation tactically – ‘Our website looks tired.” “People say its rubbish.” “I’ve seen better, we need to replace it.”
In every such organisation, there were undoubtedly voices seeking more strategic direction before rushing to upgrade – ‘What is our website for? Who is our audience. What are users trying to do? What happens when we involve other systems?” However, for many reasons these voices were ignored and so, many rushed to select a new CMS or polish their existing site with little agreed strategic direction.
10 years later the ask moved on to “we need more services online, we need customer accounts, we need push updates to customers, we need…to be more like Amazon, why doesn’t our website do that?” The result for those who again chose the tactical path was inevitable. CMS limitations, obsession with tactical IT solutions, broken customer journeys, excessive keeping the lights on spend – the list goes on. The lack of non-technical foundational activity and an absence of strategic service design led to a tech oriented doom loop. Where little improved for users, nothing was switched off, sprawl and complexity grew and – still – stakeholders couldn’t easily get their hands on useful, timely information, while beyond initial contact very little was digitally transformed.
repeating patterns in data & AI
Today we see similar patterns with data and AI as organisations begin to understand the limitations of their current data fabric. Many have successfully deployed data visualisation through the magic of dashboards. This is often a digital veneer, just like many tactical web refreshes of 10-20 years ago. Maybe this is even more dangerous than a tactical web approach because senior decision makers appear unaware that just because something looks nicer it isn’t necessarily more accurate, trustable or safe than when they were served up information in spreadsheets.
Even when public bodies have attempted to be more strategic their approach is often tech and vendor led. Data platforms, Data Warehouses or Data Lakehouses have been deployed but struggle to deliver impactful data products at scale due to the lack of wider foundational change.
A successful scalable approach should be underpinned by business strategy, clear user needs and – of course it does need tech – it will be enabled by a modern data platform. To stress, enabled not led.
SIMPLYFYING STRATEGIC GOALS – INTRODUCING PIVOTL MISSIONS
Pivotl support organisations to get ahead of the demand for AI by getting the right strategic foundations in place. We underpin this through our, proven best practice for planning. Then iteratively scaling your data maturity and AI activity, while delivering impactful data products at pace, where you need them the most – whether that’s for greater insight or for scaling AI.
This is called Pivotl Missions. Just like good on-line service strategies, Pivotl Missions promotes deliberate identification of as many use cases as possible, as early as possible. By identifying as many use cases as possible we can see commonality across your business, helping you to see opportunities to scale efficiently, we then help you prioritise and move immediately into delivery of those priorities, confident in the choices you have made. This isn’t just asking people what reports they currently get. Investment should be based on known business requirements and user needs.
Organisations might not know what technology to use, or what skills are required yet, but they do know where their pain points and areas of greatest opportunity are. Pivotl Missions helps organisations identify these into 4 key improvement areas:
✅ Improve Transactions using Data & AI
Where can we improve our transactions and user experience with screen pops, chat bots, amazon style prompts, streaming data etc?
✅ Improve Insight using Data & AI
Where do we need to improve decision making using automated visual reports & dashboards, entity profiling, options appraisal, predictive analytics, automated decisions etc?
✅ Improve Connectivity using Data & AI
Where are the pain points in our processes, where does information get stuck requiring manual intervention? Where do we need automated workflows, data sharing, collaborative environments, IoT data flow etc?
✅ Increase Empowerment using Data & AI
Where are we blocking those best placed to act, how can we un block this using automated prompts, intelligent search, semantic translations, open standards, cognitive direction etc?
Teams, services, departments all know what they need to improve in these areas. Missions gets these needs out in the open and collectively understood and then provides a proven structure to deliver these needs, with the right scalable foundations.
building foundations to support your missions
Pivotl’s methodology then underpins the delivery of these missions with a blend of the following strategic foundations in all aspects of design and delivery
Data Management – what data do we have / need to meet the improvements we have identified
Security and Compliance – what are the risks associated with processing that data for those purposes, how do we optimise security and compliance in our activity?
Centre of Excellence – what skills, capability, roles, responsibilities, accountabilities, policies, processes and protocols do we need to support and maintain our delivery?
Tools and platforms – what tech are we going to use to do this?
delivering ai gains at pace with strategic confidence
The good news is that developing this type of strategic approach does not mean years of strategic naval gazing. At Pivotl we help our customers move quickly through this activity, often by focusing on 5 – 6 priority improvement missions and using our expertise to develop a foundational roadmap in weeks. We can help our clients to think big, start small and scale appropriately.
The difference such a small period of strategic focus up front can make in the long term is huge, just as those of us who worked through the early decades of digital learned.
To support Pivotl Missions we’ve also developed a pre-built convention based data-platform – Pivotl Arc, but that’s the subject of a future blog.
If you want to know more about Pivotl Missions please contact me directly paul.kearsey@bepivotl.com
