Maximising the impact of strategic change via agility, data, and AI

The need for financial services organisations to use the latest market, customer, competitor, and execution insight to shape their strategic change agenda, and deliver the most impactful change quickly, safely, and cost effectively, has never been greater.

Yet many business leaders are struggling to unlock the opportunity. Some organisations systematically link their strategy to work via integrated portfolio and work management tooling. This becomes embedded in the way of working for distributed agile teams and results in vast volumes of data being generated daily as work is completed. But traditional portfolio governance is often incompatible with the agile ways of working adopted to deliver change. Data lacks quality and is stored across disparate sources, and leaders lack the knowledge to navigate rapidly advancing technology and the behavioural skills to act on new insights.

However, by establishing an intelligent change capability by tapping into the opportunities that organisational agility, data, analytics, and generative AI provides, leaders can shape their strategic change agenda quickly, safely, and cost-effectively.

Having collaborated with leaders of some of the world’s biggest businesses on their journey to deliver change most effectively, we’ve found four key elements that form an intelligent change capability. Here’s how to establish one:

Think standardised, lean governance meetings and reporting

For those organisations that incorporate multiple businesses, decision-makers at group level require insight into the performance and challenges of the change portfolios aligned to each business division and function. An accurate enterprise view is essential when assessing progress against group strategy, allocating resources as part of annual planning and quarterly business reviews, and managing cross-cutting dependencies. It’s also necessary when demonstrating compliance to the regulator.

The information needed to form group-wide insights should be captured through the portfolio governance arrangements of each division and function and be readily accessible when needed. However, often, the portfolio governance approach taken by each division and function varies widely. An overly localised governance approach makes it difficult to quickly and efficiently draw group-wide portfolio intelligence. But there’s a solution.

When designing a new group and divisional governance and reporting approach for the UK’s largest banking group, we found the following tactics enabled group-wide portfolio intelligence:

• A single, synchronised group-wide meeting cadence: Ensuring availability of inputs and outputs from lower-level meetings for timely group-level reporting and decision-making.
• Standardised agile metrics and terms of reference for meetings in the standard cadence: Enabling more consistent decision-making across each division and function
• Meeting templates stored in the group-wide collaboration tool: Improving accessibility and structure of key data points, such as escalations, risks, opportunities, and decisions.

Establish an agile metrics framework

Having a standard set of agile metrics appropriate to each level of concern keeps attention focused on value creation and delivery performance.

For organisations that have adopted agile ways of delivering change, the metrics used to draw insight at executive level must be compatible with ways of working across all ranks. Metrics should also encourage leadership to new ways of working. This often requires leaders to give up the metrics they’re comfortable with and move beyond Red Amber Green (RAG) statuses and milestone reporting.

The change portfolios of large financial services organisations are significant, often with budgets that exceed £1 billion each year and headcount running into the tens of thousands. In such large, dynamic parts of a business, it’s essential to take a disciplined approach when defining what’s most important to who and how to measure this.

We developed a user-led approach with a Tier 1 bank to define what metrics were needed based on employee role type and function. Creating personas that ranged from CxO Leaders to Product Owners provided a way of aligning the right sub-set of metrics with the specific focus of newly defined roles. By taking this approach, the divisional executive committee were able to track maturity of the new agile operating model and its overall performance using a scorecard of just seven metrics. These could be viewed in real time via online reporting dashboards integrated with portfolio, work management, and service management tooling. We found that the following principles helped measure what matters:

• Balance current perspectives and future needs: Define metrics with input from real people but ensure they support new ways of working and don’t reinforce legacy leadership behaviours
• Limit cognitive load and promote focus: Leaders of group and divisional change portfolios have much wider remits relative to more junior colleagues; stick to just one or two actionable metrics aligned to key focus areas
• Be realistic on what’s measurable: Shiny new metrics are useless if they can’t be measured cost-effectively; test the measurability of potential new metrics before committing them to the new framework.

Seek data-driven leaders who are skilled in new technology

Visionary financial services leaders embrace AI to tap into unseen growth opportunities, unlock transformational efficiency gains, and map out disruptive strategies through machine-enabled intelligent decision-making. The pioneers of AI adoption are also likely to lead organisations that have adopted agile structures, methods, and tooling to deliver their strategic change agendas.

Leaders who maximise the potential of new, rapidly evolving AI capabilities and agile ways of working do so through an understanding of:

• Key AI concepts, potential use cases, pre-requisites, limitations, and risks
• Measuring value, progress, and delivery performance in an agile change portfolio
• Translating agile and flow-based metrics into appropriate behaviours and courses of action
• Using portfolio management and intelligence tooling to ‘go-see’ for themselves in real-time.

Helping leaders to establish these capabilities requires a flexible, learner-led approach that takes individual needs and preferences into account.

Make the most of data, analytics, and AI-driven insights

Quality, accessible data is foundational to understanding a change portfolio’s strategic impact, delivery performance, and potential headwinds. But generating quality data as work gets delivered requires mature, consistent adoption of the chosen change framework and tooling – both of which can take years to achieve when transitioning from a traditional to agile model.

As part of a data quality strategy, generative AI offers new opportunities to increase trust and usability of data. By enhancing data quality business rules and identifying anomalies in data, for either human attention or fixing autonomously, generative AI can accelerate the creation of data required for developing portfolio intelligence and drive new ways of working and tooling.

Beyond portfolio delivery performance, the insights used to respond to ad-hoc challenges from the regulator, gauge the impact of initiatives in the market, and shape the strategic planning process are typically derived from numerous disparate data sources. The latest generative AI solutions can interrogate vast volumes of data regardless of structure and source, reducing the need for manual research and analysis.

When helping a global business services group to improve sales portfolio intelligence and performance, we designed and built a Commercial Ops GenAI assistant, featuring GenAI data agents that integrate multiple data sources to form comprehensive insights and recommendations. This enabled much deeper analysis than previously possible, uncovering trends and actionable insights in seconds instead of days.

When delivering AI solutions to automate and increase the quality of insight to benefit business performance, strong indicators of success are usually:

• Sustained focus on data quality and improvement: Automated intelligence solutions are only as good as the data they consume
• Client SME ownership: Training Large Language Model (LLM)-based AI solutions must involve people with deep content knowledge in the relevant area, from collecting training data to fine-tuning results and continuous improvement
• Leadership awareness: Leveraging AI to inform high-stakes decision-making takes trust, which can only come from a sufficient understanding of the technology, its potential benefits, limitations, and risks.

The approach to informing high-stakes decisions across strategic change portfolios has remained unchanged for decades. Despite embracing organisational agility, data, and analytics, many leaders base decisions on questionable insight.

To continue to thrive and improve the impact of their strategic change agendas, organisations must combine the four elements of intelligent change to increase the diversity, quality, and production of insight available to key decision makers.