The digital transformation playbook: a 3-part roadmap to navigate AI implementation

Opportunities and challenges

While operational excellence in resource use and assets is an important value of adopting digital water technologies, the value proposition is more robust. Technologies such as AI provide greater support to the workforce, decrease business disruption, and contribute to more sustainable and resilient operations. Digital water technologies can tangibly contribute to achieving corporate sustainability and water stewardship commitments. Quantifiable improvements in water stewardship can provide necessary proof points for external ESG reporting which is important to investors and the rating and ranking agencies (e.g., CDP Water).

The challenges in adopting digital water technologies center on the availability of data, capabilities of the workforce, the culture of innovation, and company commitment and investment. These challenges can be overcome by acknowledging what it takes for a company to have a transformational digital water strategy.

Water technology innovation

Corporate water stewardship programs are often framed as an investment to mitigate water-related risks and/or as a Corporate Social Responsibility. When viewed in this narrow manner, it leaves business value “on the table,” making stewardship an incomplete value proposition.

Existing efforts tend to focus on risk management as opposed to creating business and societal value (as outlined in Water Strategy: Moving from Risk to Value Creation by Will Sarni and Hugh Share). Water stewardship is stuck because actions are mostly transactional in nature rather than transformative. A corporate water strategy is an opportunity to contribute to business growth while also mitigating risks and improving the well-being of communities and the environment.

A transformative corporate water strategy would identify and invest in activities that create brand value, drive innovation and digital transformation, strengthen water governance, advance funding and financing mechanisms, and establish higher impact partnerships and business models. This approach would generate greater societal value and would position a company as part of the solution to our 21st-century water challenges.

Quantifiable improvements in water stewardship can provide necessary proof points for external ESG reporting, important to investors

We are now seeing corporations building upon their water stewardship strategy and actively integrating innovation in water technologies. PepsiCo, for example, has created PepsiCo Labs where selected startups work with PepsiCo experts to test and scale their ideas within the business units. Microsoft and AB InBev have leading water stewardship strategies and have launched investment funds (e.g., Microsoft Climate Fund and Emerald Venture Fund) and sustainability accelerators which include water technologies (e.g., 100 + Accelerator). The 2021 100 + Accelerator program now includes Unilever, The Coca-Cola Company and Colgate Palmolive in addition to AB InBev, the founder of the accelerator program. It is clear that corporations are embracing water technology innovation with an interest in how they can identify and scale digital water technologies to deliver business value in addition to having a positive environmental and social impact.

As other companies begin to adopt digital technologies for water stewardship and other ESG initiatives, the question often remains: How can we achieve success in this arena? One part of the solution involves the use of AI as a strategic enabler to achieving real digital transformation results.

Part 2: AI value creation

Industrial companies have long grappled with AI. Business leaders are often faced with figuring out how to implement AI in a practical way that creates value. For any new technology to work within an organization, it must start with a well-defined scope. This enables us to prove that it works. These success templates can then be used to drive enterprise-wide adoption.

The playbook to create value with AI has several components:

Use cases

Start with a use case. Before launching an AI initiative, ask yourself whether or not there’s a burning need today. A need is “burning” if it has a large impact on your business. If this need is addressed, it can directly increase revenue and/or margins for the company. We need to describe this burning need in the form of a use case. These use cases are actually very simple to describe: “We’re using too much electricity to make our beverage product”, “We’re taking too long to fix our pumps” or “We’re spending too much money on chemicals to clean our water”.

Data

Next, identify what specific data is needed to support the use case. If you try to process all the available data, it leads to chaos and confusion. It will also give you a false sense of progress because people seem to be working all the time on data wrangling. Be disciplined on what data you need. It will drive focus on the outcomes and ensure that the deployment is manageable.

Then figure out what amount of historical data is needed to build a use case. A good rule of thumb is to make sure there are at least six months of historical data.

Automating workflows

Once you figure out what data you need, figure out the data workflow. This is a series of steps needed to transform raw data into useful information.

Building a data workflow allows you to understand what it takes to get something working. This is the essence of Operation-Specific Digital Transformation for which we conducted a survey of 500 professionals and found that 78% felt supported by their team leaders when they embarked on this approach. The full report is titled Instruments of Change: Professionals Achieving Success Through Operation-Specific Digital Transformation.

Benefits scorecard

The primary reason to deploy AI is to drive a specific outcome that is measurable and has a direct impact on the business.

Include all stakeholders in creating a benefits scorecard. The people implementing the AI solution should hold themselves accountable for this scorecard. The time to realize those benefits should be short (think: 90 days).

People

To create value with AI, people must use the tools and take actions on time. Behavior change is an important aspect of AI value creation. Successful teams are proactive and data savvy when it comes to embracing change. Modern operators rely on tools to automate the monitoring work. They understand that brute force is not going to achieve meaningful progress and prioritize their work using tools and automation.

Unless your primary product is cloud-based software, it’s best to invest in how to choose the right building blocks of software

These tools arm them with information that’s needed to get the work done and limit the number of manual hours needed to put the data together. This is a powerful way for modern teams to keep an eye on their infrastructure.

Nuts and bolts to scale

Once you successfully execute an AI initiative, you should be able to replicate it with more use cases across the company. There’s no point in doing a Proof of Concept (POC) if the approach is not scalable. Make sure you have a data platform that supports deploying a wide range of use cases. The nuts and bolts of the platform should enable you to compose many workflows with ease. Nuts and bolts include automating all the work related to data -- checking data quality, processing it, transforming, storing, retrieving and visualizing the data, keeping it API-ready, and validating its integrity.

Architecture

Creating enterprise-grade AI software is a huge undertaking. The software must work across thousands of employees and a variety of use cases. Building every component from scratch in-house would be a multi-million dollar project and would face ongoing maintenance costs.

Once AI is integrated into your digital transformation roadmap, it’s imperative to focus on its most critical enabler: people

Successful companies are integrators of software tools. They don’t create every piece of software from scratch. They bring together the right software into their architecture to drive their business forward. For a company whose primary product is not cloud-based software, you’ll position yourself for success if you invest in understanding how to choose the right building blocks of software versus focusing on building from scratch.

Competitive advantage

Successful companies focus on their core product to the exclusion of everything else. For everything else, they get other tools and firms to do the work. AI software shouldn’t be looked upon as an asset that is external to the business and something that can generate returns that are independent of your core business. AI software delivers a competitive advantage that will have a direct impact on your core business.

Once AI is integrated into your digital transformation roadmap, it’s imperative to focus on the most critical enabler of this roadmap: people.

Part 3: implementation & risk management

A key hurdle in championing a new technology or service inside an organization is the ability to explain the business case. One could go in many directions when laying out the costs, risks, advantages and disadvantages. Start with the end in mind.

Here, we’ll guide you through some of the inevitable realities that may be encountered by those considering the use of digital technologies such as AI and machine learning to enhance manufacturing processes, enhance sustainable operations, and achieve ESG goals.

While it’s important to understand your exact audience and their preconceived notions surrounding your company-specific initiatives, it’s valuable to learn from others’ experiences in similar areas. We’ll provide advice and lessons learned from the practical application of leading the digital revolution of current manufacturing operations.

1. Don’t make it all about water. Do include other KPIs, such as production and quality

Water is important for the food and beverage industry. However, for decision-makers to realize the full potential of applying AI and other digital solutions, the initial business case cannot simply focus on the economics of water. It will be seen as too minimal for such a major resource investment. In reality, water is only a fraction of the overall digital transformation needed to optimize operations.

The KPI to include at the forefront alongside water is production - specifically, how much water is used in relation to the amount of production. You can’t digitize your process related to water without fully adopting other digitization efforts, such as factory automation. Without digitalization for the production side as well, your ROI will fall flat.

A major driver for the business case should be the quality of the product, which can be positively or negatively influenced by the overall operational strategy. Companies cannot afford to sacrifice product or performance quality at the expense of digital transformation efforts. Ensure all the parts affected by digital transformation cohesively work together in a manner that allows for the biggest return on investment and the most sustainable operations.

2. Don’t oversell the short-term value of real time data. Do focus on the need for a long-term execution strategy

Getting information on a visual dashboard initially drives engagement for the end-user. But this “curbside appeal” does not last long. Once the excitement wears off, so do the actions needed to follow through on building the dream home for your data. It is ultimately the action of the people that drives results.

The KPI to include at the forefront alongside water is production: how much water is used in relation to the amount of production

Proactively avoid this misstep by highlighting the actual use case and the user experience. Clearly outlining the journey from end-to-end involves a great deal of planning and understanding, especially around the design thinking process which is not readily known in industrial-type settings.

When you begin to ask yourself, “How do we get insightful information at the right time to the right people?” you will start to shift your focus from the short- to the long term.

3. Don’t underestimate the amount of work required. Do account for factors such as culture and skills

Turnkey solutions are common in certain industries, and for good reason – you can’t be everything and do everything well. If the company can bolt on an existing solution instead of building a new toolbox, then so be it. Manufacturers, for example, can be inclined to look for external sources for digital support since this is outside the core business.

The amount of the culture change needed to really embrace digital or sustainability initiatives should not be underestimated

However, if the company is approaching digital transformation or sustainability initiatives from a turnkey mindset, there will likely be a disappointment.

The amount of culture change needed to embrace these technologies is significant. The new ways of working with data and maintaining data/sensors require different skills. It is much more than just learning how another machine runs.

Conclusion

The realities of operational transformation can be challenging, even with the increasing interest in practical solutions such as digital water technologies. In order for AI to work within an organization, it must start with a specific desired outcome that is measurable and quickly shows a direct impact on the business. This, along with a well-defined scope that also considers issues such as workforce readiness, is imperative to successfully achieve long-term sustainability and economic goals.