In previous Centelon papers on energy markets, we have discussed the challenges created by the revolution in energy markets accompanying the transition to Net Zero, AI and the proliferation of solar and more recently storage solutions for retail and large-scale customers.
Today’s paper expands on earlier thinking, and has been augmented by the benefit of recent round table discussions with Australia’s leading energy retail and distribution executives.
Three common themes emerged from these discussions:
- A more intense level of critical thinking needs to be applied to our business strategy and operations. If we want to thrive with AI we have to invest in both the skills within our teams and the technical/data capabilities to survive and thrive.
- The convergence of retail customers prosumer appetite, and acceleration of AI means fundamental business transformation is essential.
- We have a long way to go both technically and organisationally to manage the disruption AI has brought to our industry.
“It’s too slow”
Almost all our participants agreed that it simply takes too long to pivot a new proposition and product, as customer preferences change and new competitors enter the market.
An interesting call out in the round table was the investment bias towards billing system modernisation combined with a relatively simple client relationship model does not by itself provide the operating environment for rapid product development (NPD) and the new paradigms we face.
It’s clear, Speed-to-Action is a “must have” if we want to compete.
“Data first, AI second”
A further common insight from the round table was the acknowledgement effective data systems are a prerequisite for AI operationalisation.
Data (amount & form), data capture & storage, technology stack, consumer marketing skills and cross business unit collaboration were highlighted as being at the core of these issues and recent survey results in energy highlight a few elements that are areas that require attention if we want to play in the AI space.
Recent research papers validate this discussion.
- 76% of Australian energy companies struggle to access real-time data for decision-making.(Source: Appian)
- More than half of executives report their data and revenue teams operate in silos, according to industry surveys on utilities’ digital maturity. (Source: McKinsey & Company, Deloitte)
- Only 16% of Australian utilities have fully integrated AI, with the majority still in pilot or early maturity phases (Source: Computer Weekly)
- Utilities that scale AI use cases in CX and billing can reduce customer service costs up to 25 -30%, according to global benchmarks from Accenture and McKinsey(Source: NexGenCloud, Default, McKinsey & Company, Deloitte)
If we break this down into its simplest elements, there are 2 fundamental capability builds at the core of the transformation agenda.
- Technical / data capabilities (data models to capture, store, make sense of, and deploy data related insights/propositions); and
- Organisational capabilities (organisational willingness and action orientation to move beyond ideas to deployment.
“Whose problem is this?”
It is not ok to think of the challenges in speed to market and AI are problems that sit soley at the feet of your CIO, CTO and supporting IT teams.
Partnership and collaboration between IT and business teams are essential with leadership and the ability to progressively experiment in an uncertain environment at the core.
If we think about some practical challenges, such as
- Creating a single version of truth
- Turning new data into new revenue streams and/or cost savings
- Making AI stick into our business operations
.. then a critical capability (and maybe an organisational change) to resolve these challenges is the requirement for technical teams to spend more time “in the business” to understand the customer and, as important, business teams, accelerating their data and technical skills to understand the relationship between data, systems and machine learning as a foundation to build out new Use Cases.
I’m confident that the next generation C-suite, will, in part, develop their careers by leading transformations bridging technology and business operations, as boards and investors seek executives with proven AI delivery capabilities.
A few ideas to consider
Assessing your current entities maturity position in relation to data and AI readiness is a sensible starting point to consider priorities. Beyond that holistic view, a few other ideas might help inform priority projects.
1. Establish Unified Data-Commercial Operating Models:
This means bridging the gap between data teams generating insights and commercial teams driving revenue. By creating a “unified data-commercial operating model” organisations can ensure that every piece of intelligence contributes directly to top-line growth or cost reduction.
2. Embed real-time signals into key touchpoints and your CVP:
As BTM propositions develop, real-time responsiveness has become an increasingly important element in your Customer Value Proposition. This means integrating real-time signals into customer experience (CX) delivery, allowing for proactive interventions such as dynamic pricing adjustments or grid/ off grid load & storage shifting based on live market conditions.
3. Architect a Unified Digital Blueprint:
Moving beyond isolated initiatives requires a comprehensive digital platform that acts as a “single version of truth”, tying together systems, roles, and actions. This enables initiatives to scale organisation- wide and deliver measurable business impact.
There’s never been a better time to be in energy.
In conclusion, there’s never been a better time to be an ambitious energy executive and/or organisation in energy markets. It’s becoming clearer every day that embracing the adoption of BTM and AI is a winning play.
Strong leadership and well executed transformation plans will be central to success and will bring significant rewards to those who do this well.
