A decade of consulting experience at Accenture, followed by a variety of leadership roles culminating in his current role at BT, has given Lorenzo a deep understanding of how to use technology to drive business results. This was most recently seen in the way BT devised and implemented a new set of AI tools in its internal systems to move away from legacy technologies and adopt a more value centric approach to data in the business.
Challenges
A challenging landscape
BT Business, the B2B unit of the wider BT group, had an extensive string of legacy technologies and a complex landscape when Lorenzo joined the fold in 2019. BT Business caters to a spectrum of customers that range from microbusinesses to large multinational corporations, which meant there were particular complexities for the system and data landscape that needed to be addressed, by driving a large-scale transformation to simplify and innovate the technology and processes being utilised.
“You can’t trust AI if you don’t trust the data feeding it,” said Lorenzo. “Even before getting ambitious with AI, we realised that it was extremely challenging to access the right data without the need of extensive manual curation and intervention.”
BT Business was split into Global and Enterprise teams with Lorenzo hired to drive the data and AI strategy for the Global portion of the operation. He noted that, due to the nature of the big customers in the Global team, that data siloes were a huge issue, as well as an approach that treated customers as an island with their own database, meaning a multi-pronged solution to a data overhaul was required.
Furthermore, there was no immediately identifiable data team within the organisation for Lorenzo to coordinate with. This would need to be identified, created, and integrated into the business.
Solutions
First steps to address the challenges
The first moves that needed to be made by Lorenzo and the data team involved assessing where the organisation was in terms of data maturity, and where the first quick wins could be made. The Global team was the most suitable starting point and could be used as a strong example as to how a data-centric approach could be adopted across the wider organisation.
Within the Global wing of BT’s organisation, there was no centralised data function and there were pockets of people used heavily fragmented data. This fragmentation had been exacerbated by the introduction of new customer groups and systems, brought in through acquisitions over the years, which created a huge amount of shadow IT and shadow data.
Lorenzo successfully secured sponsorship for his ambitions from BT’s leadership by presenting a clear vision that highlighted value opportunities. He facilitated candid discussions about current limitations, ensuring leadership understanding of improvement areas needed across multiple domains. This approach gained crucial buy-in from decision-makers.
“To get the senior leadership excited is the easy part, but then you have to constantly keep the balance right between ambition and realistic delivery,” said Lorenzo. “AI is not a silver bullet; it is about resilient execution and culture of innovation.”
The Global team that Lorenzo joined had to be built from the ground up. He started with zero people within the data and AI function, but there were existing pockets that had the skills in some functions running shadow forms of data processes. By making use of existing skills and seeking out new talent, Lorenzo was able to build out the data capabilities of his new team, finding around 50% these different pockets and started hired the other 50% as new talent.
The strategy defined a detailed roadmap for four areas:
- Tech
- People
- Operating Model
- Value
The roadmaps for each area would move away from legacy technologies and operations, while instilling a new data culture across teams in the business. Eventually, each marker on the roadmaps would become a company decision.
“We realised quickly that to really succeed we had to get better in each area of the strategy, iteratively but coherently, to keep it balanced without trying to race ahead in one aspect with no foundations built for the others,” said Lorenzo. “To evolve in a balanced manner is the only way to scale properly. For the organisation it is like learning a new language, so it is a combination of having the right grammatical foundations, and to try and have different conversations. That’s the only way to learn it properly.”
One major portion of the roadmaps highlighted the need to sufficiently invest resources and time into skills for data management and engineering. This required a specific map to identify areas where niche skills were needed, as well as details of investment to ensure support from the wider organisation. The success of this skills investment required a restructuring of the team following an assessment of where the existing skills were.
When it came to examining the skills, Lorenzo explained to DataIQ that his team must be an organisation that can change to meet the requirements of its stakeholders and ultimately BT Business’ customers. This meant that each team needed the right skill sets for their needs, but they then must be able to deploy those skills appropriately and effectively embed people into the right departments to deliver the outcomes they were aiming for. It was decided that there must be a level of flexibility with a partially decentralised approach to keep skills close to the Group’s business domains.
Cloud development
To be able to harness the power of the new tools and the colossal amount of data, a cloud solution was needed. A value framework was created to expose the value from use cases and AI in a structured manner, and there was emphasis on the ownership of data, including pushing data as a product.
Lorenzo noted that the teams at BT were proficient in legacy tech but needed to be trained in more modern cloud and data driven ecosystems. The data team used existing skills wherever possible, but there was a distinct need for fresh talent to achieve the cloud ambitions. The team highlighted to decision makers that there needed to be a progressive approach to cloud native thinking. He and the team convinced decision makers on the approach through early wins, use cases, and improved education about the future possibilities for AI-driven wins.
“We really didn’t want to lift and shift on-prem technology and ways of working to cloud,” said Lorenzo. “It took us time and focus to re-think the way of designing and developing software in a Cloud ecosystem, to get the most out of it.”
Lorenzo emphasised that, after deciding to move to the cloud, many in the organisation might think it is entirely turnkey. However, governance and management need to be customised for the team’s needs. This would be done by improving storytelling, data literacy, and focusing on AI tools education.
BT Business worked with Accenture to accelerate the cloud journey and Lorenzo explained that Accenture was active in assisting with:
- Strategy
- Securing funding
- Devising pilot use cases
The new approach used the mix of internal skills and partners, such as Accenture, and this was crucial for success. The first phase of the AI journey, moving to the cloud, was catalysed through collaboration with Accenture and identifying the existing skills and passions of team members.
At the time of writing, the AI journey for BT Business is in the last leg of the cloud transition and moving away from the existing legacy infrastructure and is delivering value through a range of use cases supporting sales, service and operational transformation.
Results
Benefits of AI solutions
Since implementing AI solutions, BT Business has contributed significant value to BT Group’s overall AI value realisation, estimated at approximately £125 million at the end of March 2024. Additionally, there is likely further value across various departments, including sales, which cannot be easily quantified, but owes thanks to the data and AI transformation.
Several features have been delivered, and initial challenges in gaining full acceptance for AI from decision-makers have been overcome. This is evidenced by a reduction in legacy issues and active upskilling across departments to use the new tools effectively.
Lorenzo notes that one unexpected benefit is the increased ambition within the business to engage in discussions about AI, facilitated by open channels of communication. Initially, the data office had to push AI initiatives onto the organisation. Now, the organisation is proactively seeking insights from the data office, indicating successful data culture evolution, no doubt supported and enhanced by the rapid rise of generative AI in the market more widely.
One advantage of the cloud-based data solution is enhanced visibility of potential capabilities across the wider business, making access to data-powered solutions clearer than ever. This is particularly important given the extensive scope of the team, including governance, business intelligence, engineering, and more.
Lorenzo described the beneficial input of partners such as Accenture in accelerating the positive impact of AI and migration to Cloud Native technology. There must be strong groundwork put in place to be able to attempt a transition away from legacy systems and embrace an AI-centric future, and collaboration with partners can help expedite this journey.
Furthermore, there is a growing ambition within the wider business to integrate AI more broadly. Leaders, decision-makers, and even non-data professionals now have a better understanding of what needs to be done to improve. Through AI, BT has demonstrated enhanced business support and secured stronger sponsorship.
Final steps
BT’s data and AI transformation is not over, rather it is entering a new phase. Some remaining checkpoints to be finalised include moving the remnants of shadow IT into the new system and decommissioning of the remnants of legacy systems. The focus will shift from transformation to implementation and scaling.
Due to a necessarily cautious approach to how the cloud move has been handled, a long timeline for full completion was put in place. This would also allow for relevant talent to be hired, trained, and embedded across the organisation to ensure smooth operation of the cloud services.
Furthermore, after inheriting a huge amount of shadow and fractured data, the task to clean and sort the data, while ensuring the quality of new data, has been a monumental task that is now close to completion. Lorenzo and BT took a measured approach to accessibility and guaranteeing high levels of security which meant that a rapid transfer from one system to another would not be possible, but it was appropriate given the criticality of data for meeting customer and regulatory needs in the context.
“Data and AI transformation is not about reaching a final stage; the technology evolves too quickly for that. It is about staying current, be adaptable and future proofed, in order to scale adoption and value realisation. We’re looking to become a high performing business with AI and generative AI, buying for pace, building and integrating to differentiate and win in the market. We are in continuous evolution, evaluating new approaches, new tech, and working with the business in partnership,” said Lorenzo.
Finally, the hope is that Lorenzo and the team can move from scaling in certain areas to the whole organisation and move from giving the business insight and AI intelligence into automation and stronger capabilities. The aim for BT is to have a journey of AI and generative AI solutions at scale for all aspects of the business.
Considerations for achieving success
Lorenzo added some thoughts about his experience and things for other organisations to consider when attempting their own AI transformation.
Identify and champion use cases:
In BT’s case, the Business and Global functions were originally not seen as relevant for data when the journey began but are now seen as the template for the rest of the organisation. Data leaders should seek out areas within the organisation that have the potential to generate valuable AI use cases. Even if initial scepticism exists among non-data leaders, pursue these opportunities to demonstrate the potential benefits and set a precedent for other departments.
Foster a data-driven culture:
Recognise that adopting AI requires cultural transformation. Data leaders must continuously nurture a data-centric mindset across the organisation and understand that this culture will evolve and require ongoing effort and attention. In BT’s case, the data culture is now central to operations having come from a sporadic and incremental existence when the journey began.
Manage expectations and encourage experimentation:
New tools and technology can be seen as exciting and receive immediate enthusiasm, but they can often lose support quickly if they do not meet expectations for stakeholders rapidly. Lorenzo highlighted that when introducing predictive tools, ensure that decision-makers understand the iterative nature of AI development, including the potential for initial false positives. Promote a culture of rapid experimentation and learning from failures to ultimately achieve success.
It must be explained that it is a process and a journey, not an immediate solution. There is an inherent need to fail to succeed, so it requires encouragement and support to fail faster to reach the end goal.
Promote AI’s value through storytelling:
Maintain enthusiasm for AI by regularly sharing success stories and tangible benefits with stakeholders. This will help secure ongoing investment in AI technologies and training, reinforcing its perceived value within the organisation.
Position AI as an empowering tool, not a threat:
Finally, Lorenzo highlighted the importance to communicate the role of AI as a complement to human work rather than a replacement. Emphasise how AI can empower individuals to excel in their roles, enhance productivity, and simplify tasks, fostering a collaborative environment for AI adoption.
