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Six pillars for AI success: how the C-suite can drive results

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11 minute read 22 Aug 2024

A number of functional areas and capabilities are needed for competitive advantage. But overlooking just one creates an unstable foundation.

In brief
  • Your organization likely has vastly more GenAI ideas than resources. One vital component is a control tower for oversight, focus and governance.
  • Data maturity, alliances and talent are also vital components of a forward-thinking AI strategy, beyond the actual technology itself.
  • Leaders must also resist the impulse to fit GenAI into today’s processes and business models instead of reimagining them entirely.

You likely have hundreds of generative AI (GenAI) use cases and may have seen some early success with prototypes. Maybe you also have the sandboxes for controlled experiments, buy-in from key executives, and even a healthy budget for testing and development. And yet, after a year, you may find you are still struggling with moving fast enough to mitigate competitive risks and confidently turn possibilities into opportunities — to truly transform your business.

After another strangely resilient yet uncertain economic year, C-suites and boards in the US are expecting to see cost savings, greater efficiencies and added revenue from GenAI implementation. To some extent, GenAI infrastructure and processes are much like those for regular artificial intelligence (AI), which some companies have been leveraging for decades. But these are often more basic automation projects, and important distinctions with GenAI must be addressed, including:

  • The sourcing, management and preconditioning of data vital for training GenAI algorithms and creating outputs for different stakeholder groups
  • Ethical frameworks that can put guardrails around a technology whose upsides and downsides are not fully defined yet
  • Partner ecosystems to help organizations cost-effectively keep pace with the future, as well as gain more data sets to enrich their large language models and knowledge repositories
  • Change management for employees who are concerned about what GenAI means for their careers and responsibilities

Success can be found in the narrow, overlapping center of this Venn diagram of functional areas — but the payoff for those who get there first can be enormous. We see that truly transformative GenAI capabilities are built on six pillars.

Six pillars to guide AI success for C-suites

1

Pillar 1

Establish an AI control tower

A control tower effectively oversees use cases and investments for the whole organization to help set priorities and avoid duplicated efforts.

Voluminous GenAI use cases all compete for time, attention and resources. Many companies are wasting their money on duplicated efforts, some of which exist in shadowy corners without proper governance, creating potential reputational risk. Companies may not even know where all their AI models are being deployed. This is a blind spot that will become more threatening as regulation takes shape in the US and across the world, and one that certain industries (such as financial services, energy and health care) could feel more acutely.

As a catalyst for the new age of GenAI, a control tower — and not individual functions — relentlessly focuses on value from AI and GenAI so that time, money and effort are spent wisely, efficiently channeling people’s enthusiasm in positive directions while setting up oversight and guardrails. Many AI and GenAI solutions have common patterns and benefit from reusable assets that can accelerate time to value and reduce costs. Without a control tower, different groups across an enterprise are at risk of building very similar things from scratch for various use cases. The control tower effectively has authority over where an organization will make its investments and create value by identifying patterns across the various use cases that align with business needs and prioritizing the development of GenAI solutions, for example.

2

Pillar 2

Reimagine future business models and functions

C-suites are thinking more about how the technology fits into today’s operations and business models instead of how it redefines tomorrow’s.

It’s natural to try to shoehorn a new advancement into what you already understand. Amid the Industrial Revolution, it took time for master planners to redesign cities for automobiles instead of horses, and to rethink how manufacturing plants were designed after steam engines were rendered obsolete. GenAI is poised to be every bit as transformative.

Consider procurement. You could automate, say, the first five steps of reviewing requests for proposal: a GPT creates a content comparison showing which met the requirements and how they differed, with references pointing to page numbers within each document. Instead of four weeks spent redlining, you’ve cut this down to a week — a tremendous gain in efficiency within a process that is important but not core to why your organization exists.

The truly transformative impact would be to entirely reimagine what you do in the front office, not just streamline the back office. GenAI unlocks new products, services and business models that are easy to overlook if you approach the technology with a robotic process automation mindset. That can include creating new products and features enabled through GenAI, equipping them with connectivity under pay-as-you-go service subscription models, selling them directly to consumers instead of through intermediaries, and leveraging the consumer data for insights and perhaps selling it as a separate revenue stream. And while so much is changing, one timeless fact of business remains: if you don’t innovate first, your competitors will.

3

Pillar 3

Ensure confidence in AI

The buzz of GenAI may dwindle in the first couple of sprints after you first deploy it, as the transformative potential meets the messy reality. Continuous testing and governance are essential.

How EY can help

Does your GPT give perplexing answers, tread down the wrong paths, maybe even “hallucinate”? Does it have access to the right data — or even too much data?

 

Within control towers, processes and rules must govern how algorithms and models are created and then maintained — such as mitigating bias, enforcing fairness, and enabling explainability and transparency, from the point at which the AI model is created through how it’s continually managed. From the beginning, data security and integrity are crucial. Oftentimes the type of data required for GenAI differs from what’s used in typical AI: it doesn’t exist within neat rows and tables but instead lives in chat logs, emails, surveys and more, and therefore requires far greater preconditioning.

 

Leaders in a control tower also must define their ethical compass with regard to GenAI and how it aligns with company values. As a consumer, would you feel better or worse if you knew that a GenAI model was guiding your investment decisions or health care diagnoses? Organizations must answer these questions before GenAI is deployed, not after. EY leaders have oriented our AI efforts around fairness, accountability and reliability. These aren’t just words — each correlates to metrics that measure the confidence of any particular solution we deploy. The EY organization, ServiceNow and others are helping companies define, enforce and test their AI principles.

It may feel like a paradox, but humans must be kept in the loop for GenAI confidence. Are the people who will ultimately be affected by GenAI being included in its design, build, testing and deployment? One way is through reinforcement learning: people are core to training the models and rating the output of a GenAI chatbot or algorithm, helping it to determine what is and is not useful. Without that stopgap in place, the efficacy of the models becomes poor. Additionally, it’s important to have mechanisms in place for those people to be able to flag problems and provide feedback on what isn’t working well, what responses are incorrect and what outcomes aren’t aligned with business needs?

GenAI algorithms and outputs must be rigorously challenged. Think of the chatbots that were set up for a narrow purpose that are able to be goaded into spewing hatred, surrendering intellectual property or informing customers about nonexistent company policies. Algorithms set up for certain tasks can also evolve in ways that may seem mysterious to outsiders, without “explainability.” Red teams — which are resources that assume the role of adversaries to try to subvert AI — are crucial for challenging and validating efforts, with independent monitoring.

 

The EY.ai Confidence Index offers a framework for helping organizations transcend these pitfalls, driving the reliability and explainability of responsible AI to supports enhanced decision-making and more efficient operations.

 

Principles of responsible AI

4

Pillar 4

Address talent and tech gaps

The buzz of GenAI may dwindle in the first couple of sprints after you first deploy it, as the transformative potential meets the messy reality. Continuous testing and governance are essential.

A key takeaway from launching our own internal GenAI model to 400,000 EY employees was: give people training when you give them tools. The importance of that finding was further cemented when the results of the EY AI Anxiety in Business survey found that 80% of employees felt they would be more comfortable with AI if they were trained, yet 73% said they weren’t getting the coaching they needed. The survey shows that people are overwhelmingly using these tools at work already, and they want a deeper understanding of responsible AI practices.

What will make people more comfortable about using AI at work?

On the technology side, many organizations are struggling with a familiar question: do I build what I need, buy it or some combination of the two, such as through augmenting existing software or building on existing platforms with your intellectual property? There’s no substitute for debate and planning, even in an unsettled technology landscape. Maybe you decide to hold off on investing in financial GenAI apps because you believe your current enterprise resource planning systems will eventually incorporate that functionality. Yet it may be worthwhile to pursue GenAI tools customized to your specific needs across the product development lifecycle. This discussion is not just about what gaps exist but the method for filling them.

5

Pillar 5

Develop an ecosystem of alliances

AI is a complicated technology that affects every level of an organization. Getting ahead of the curve — or just reaching the median level of maturity — requires partners.

How EY can help

Organizations are also often looking at yesterday’s teams and thinking about how they will solve tomorrow’s problems. Don’t be afraid of acknowledging that you will need a different workforce and new thinking, for business needs outside of today’s core business. And a rapidly changing world demands new solutions, new data sets and new capabilities that few companies can call upon within their organizations. Plug-and-play and single-point systems that automatically create solutions, based on our unique needs and data sets, simply don’t exist today — and the features that seemed cool last year can become stale just months later.

 

A robust ecosystem of alliances helps your company fill those gaps quickly — and flexibly — while evolving with the changing landscape. Defining a strategy for what you need and how to accelerate it is crucial — whether your organization is a pharmaceutical giant using its highly valued drug trial data that needs to stay out of the public domain, or a consumer products company looking for greater automation for its service agents. Is proprietary data your core market differentiator that you’ll use to train a model? Asking this question is key because the strategic path becomes more clear if your proprietary data offers a competitive advantage.

 

Think of alliances in four buckets:

  • Technology partners. Lean on them for guidance, innovation and even experimental funding.
  • Academics. These partners can be effective for working through hypothesis testing, research, guidance and thought leadership.
  • Professional services organizations. They can help you navigate the entire landscape, identify strategies and sources of value, or even help build the solutions to capitalize on that value.
  • Data partners. Your sector as a whole may collectively gain more through a data consortium, in an area that isn’t differentiating for your organization specifically. (For example, in patient data across health systems, mindful of privacy regulation.)
6

Pillar 6

Drive focused data maturity to be AI-ready

Data quality is more important than ever — but if you wait to make all your data perfect and trusted before testing your use cases, you could be waiting forever.

Focused data maturity is key: each piece of data doesn’t need to be pristine to belong in your GPT. That distinction is important as we confront regulation.

You need an enterprise-wide, fit-for-purpose data strategy under a tight governance based on how data will be utilized. For example, if you will have a GenAI application that customers will use to get information about products, that product data will need to be highly managed and governed — but plenty of data will fall outside of that remit. Your data strategy should inform how you invest effectively, targeted to your highest priorities, instead of trying to “boil your data ocean.” Here are the key needs to address:

  • Accessibility, at scale: High volume of historical information available for exploration and production usage with fast processing
  • Visibility: Ability to understand the data, both technically and in business context, while easily finding data across systems and sources
  • Timeliness: Up-to-date data files with reasonable data latency providing what’s needed for each use case (such as in real time or in batches)
  • Openness: Multiple file types and formats (including text, numbers and images) while using consistent tools across the platform
  • Reliability: Data pipelines that minimize breakage, limit the amount of missing data and maintain consistency from one update to the next
  • Expansiveness: Consistency across the global enterprise, from one region and one business unit to another, so that customers and products can be linked
  • Trust and security: Ability to execute AI use cases responsibly with data that is secured for only those who need it

Summary

With an AI control tower, you empower everyone to contribute but task one group with driving focus, oversight and business alignment for reusable assets, while emphasizing responsible AI practices. Technology is one piece of the puzzle: you also must upskill personnel, secure alliance partnerships and get your data AI-ready. And never lose sight of how you must reimagine yourself for tomorrow, not just today.

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