Today’s businesses cannot afford to ignore AI — it has become a matter of survival. Those who won’t adopt AI are in danger of becoming irrelevant in sectors where intelligent systems are playing a leading role in innovation, productivity, and competitive advantage.
Consider Kodak, which missed the digital revolution, or retail empires grappling to withstand Amazon’s AI-driven logistics and personalization engines.
Strategy is becoming something that boards are making the final decision on. But many are still on the back foot and not being proactive in dealing with challenges such as measuring AI’s ROI, handling ethical risks, and gaining the skills for extracting value from it.
Without a solid AI strategy in place, boards risk being blindsided by rivals that are employing AI to automate their business processes, personalize customer experiences, and unlock fresh revenue opportunities. The question is not whether to just start employing AI, but how to go about developing strategies for the sustainable, responsible, and competitive use of it.
Building Blocks of a Strong AI Strategy
An AI strategy is not a set of discrete technical projects; it’s an intentional and comprehensive plan for commercializing AI at the pace of business, while maintaining accountability and responsibility. Eularis have been creating these in biopharma and life sciences agencies for over a decade and these have evolved into cutting-edge strategic AI plans that deliver results.
1. Vision and Objectives
AI should be a strategic enabler, not something that emerges as a technology test in its own private silo. A solid AI strategy begins with a clear articulation of the organizational strategic goals that the AI should be leveraged to enable. Whether the objective is enhancing drug launches, cutting time to market, increasing operational efficiency, delivering hyper-personalized customer experiences, or generating additional revenue – AI’s purpose needs to be nested in the context of what matters most: that organization’s mission, value proposition and organizational goals.
Every industry will have different needs and even every company within an industry will have slightly different needs. For example, a retail company may concentrate on AI-based inventory optimization and personalized marketing, while a healthcare organization could choose early disease detection tools and patient care enhancement. Two pharmaceutical companies may have vastly different pipeline challenges requiring entirely different AI approaches.
Strategic Approach:
At Eularis, we’ve developed a proprietary framework that aligns AI initiatives directly with board-level objectives. This involves sophisticated analysis techniques that go beyond simply copying AI use cases from competitors – an approach we see inexperienced AI strategists taking that typically leads to failure. Our methodology ensures each AI initiative is specifically tailored to your company’s unique challenges and opportunities.
For organizations interested in learning how we apply this framework to their specific context, we recommend scheduling a consultation to discuss your strategic objectives.
2. Governance & Accountability
Effective AI governance requires a multi-layered ownership structure that balances innovation with appropriate oversight. Boards need clear accountability frameworks that define who owns AI decisions at various organizational levels – from tactical implementation through strategic direction.
KPI: “Time-to-Governance” (time required for new AI initiatives to pass ethics/compliance checks).
Eularis has developed governance models specifically designed for pharmaceutical and life sciences organizations that address the unique regulatory and ethical considerations in these industries.
3. Technology Infrastructure
Infrastructure decisions determine scale, cost, and competitive advantage. The right technology stack depends on factors including your data architecture maturity, existing systems, scalability requirements, and whether you’re building proprietary solutions or integrating third-party tools.
An infrastructure decision determines scale, cost, and competitive moat.
- Data architecture: Breaking silos with cloud data lake (Snowflake/Databricks) and real-time pipelines (Apache Kafka).
MLOps foundation: Automate deployment of models (MLflow/Kubeflow), monitoring (Evidently AI), and drift detection. Without it, the project may fail in the pilot stage.
Key considerations include breaking down data silos, establishing robust deployment processes, and implementing monitoring systems to ensure AI models perform reliably over time. Our team can help assess your current infrastructure and develop a roadmap aligned with your strategic priorities.
4. Talent and Skills Development
AI success is fundamentally a people challenge. Organizations need to invest in developing AI capabilities across multiple levels:
- Technical teams need hands-on training with relevant tools and frameworks
- Business stakeholders require AI literacy to identify opportunities and collaborate effectively with technical teams
Leadership must understand strategic implications to make informed investment decisions
AI success is no longer a technology problem but a people problem. “Invest in training your workforce to narrow the gap between what AI can do and how AI applies in practice:
- Technical Education: Train IT professionals and data scientists to use he latest AI tools, programming languages, machine learning frameworks, and other needed tools.
- AI Literacy For Non-Technical Employees: Train non-technical employees on basic concepts to be able to effectively work with technical teams and learn how to effectively use AI for their challenges and also scope opportunities for AI in their role. For instance, the marketing team can learn how to leverage AI for customer segmentation and personalization.
Leadership Development: Educate executive leadership and board members about the strategic implications of AI so they can make informed choices and become advocates for bringing AI across their institutions.
Eularis provides tailored training programs that accelerate your team’s AI maturity while building sustainable internal capabilities.
Collaboration with External Experts
No company or organization can develop on its own an AI capability at a world-class level completely alone. Strategic alliances are crucial to ensure access to state-of-the-art know-how and speed up:
- Outside talent: AI consultants, industry experts, and vendors can offer a focused view on best practices and learning curves for existing personnel.
Public-Private partnership: Engaging in government initiatives or AI ethics and innovation consortia can provide organizations with the opportunity to shape policy and tap into resources that are available.
Fostering a sense of continuous learning and collaborativeness will allow companies to keep their employees nimble, prepared to solve new issues that arise when AI integration starts.
The Board’s Checklist
- Does our vision of AI make us choose which business results to prioritize?
- Do board charters specify governance responsibilities?
- Can 70% of models be re-deployed in <3 Months in our infrastructure?
Do we have a talent strategy beyond hiring? (Hint: Reskilling beats recruiting.)
How Artificial Intelligence Strategy Is Shaped By Outside Experts
In the age of high-stakes AI adoption, boards are caught in a two-front challenge: realizing the possibilities around AI for new operating models and markets while also mastering how to minimize risks.
Given the breakneck speed at which AI technology evolves, boards need not only technical acumen but a strategic vision — something that many companies’ directors lack inside the boardroom. To bridge this void, outside AI thought leaders are essential for creating strong AI strategies. They bring new thinking, market insight, and cutting-edge intelligence that enable boards to make well-informed decisions that reflect long-term business strategy.
1. Why Boards Need External Insights
AI is a complicated stew of data science, machine learning, regulatory issues, and ethical considerations. The board is great at governance, finance, and business strategy, but very few of them have the technical literacy to evaluate AI efforts.
- The Problem: Without this base of AI literacy, boards might approve strategies or initiatives they do not fully comprehend, as well as reject highly transformative opportunities merely out of fear for the unknown.
- The Solution: Outside experts act as educators, demystifying the complexity of AI to give practical advice. For instance, an AI consultant might clarify the definition of machine learning bias or how they calculated the return-on-investment (ROI) of predictive analytics tools in their AI strategy financial model in plain language suitable for business.
- Knowledge Translation: They translate AI models into board language: “This reduces customer acquisition costs by 30% with hyper-personalization.”
Anti-Bias Safeguards: In addition, neutral third parties can pick up on misguided assumptions that internal teams overlook (for example, an insurer’s risk model over-penalizing certain zip codes).
2. Types of External Experts to Consult
3. Structuring Advisory Boards or Committees
Instituting AI boards or advisory committees specific to AI provides continued access to outside counsel.
- Composition: Must include representation of various AI consultants, industry experts, ethicists, and jurists to ensure that a variety of views on approaches concerning AI are represented.
- Function: The purpose of the advisory council is to advise on.
- Tactical alignment of AI initiatives.
- Framework for ethical use of a risk assessment.
Keeping abreast of new trends and other changes in law.
Best Practice: Stick regularly together the right people who take a fresh perspective in decision making, and align skills with the business requirements.
Collaborating on Pilot Projects
Pilots are a low-liability opportunity to play with AI programs through external expertise.
- Approach: Work with consultants and experts to initiate small AI pilots based on high-impact opportunities.
Example: A retailer might partner with an AI consultant to deploy a chatbot for customer service, and track the impact of that approach on resolution time and customer satisfaction.”
A manufacturing company might engage vendors of AI systems to install predictive maintenance on a single production line, then roll it out across the enterprise.
- Iterative Learning: Employ pilot projects to pull in data, fine-tune algorithms, and build internal capabilities in preparation for broader AI adoption.
- Key Metric: Measure pilot programs against defined KPIs, including cost savings, increased efficiency, or enhanced customer satisfaction.
Assessment of Risks and Opportunities of AI: A Strategic Angle
Assessing AI opportunities should ideally start with identifying high-potential use cases, which are suited for business objectives and address risk while showing ROI. AI is transforming how organisations across industries operate: predictive analytics are decreasing equipment downtime by an average of 40% in manufacturing; digital humans that can automatically handle customer inquiries/ claims have reduced resolution times by 50%, supply chain optimisation allows real-time demand forecasting and saved millions through inventory carrying costs. But, it’s about priorities.
Boards also need to be realistic about short-term wins tempered by the long arc of transformation. For instance, a financial institution could start with algorithms to detect fraud more accurately (reducing the false positive rate by 30%) and yet inspire customer confidence before rolling AI out in other domains, such as credit or investment portfolio management.
Just as important is risk scenario planning and mitigation, as the unchecked use of AI can present organisations with a number of potential new pitfalls. Data security and privacy challenges continue to top the list, especially with laws such as GDPR and the imminent implications of the EU AI Act.
Boards should take care to establish strong governance structures with which to enforce compliance, encryption of the most sensitive data, and tracking access to AI systems. And when it comes to bias and fairness in AI algorithms, the stakes are high—after all, biased models can perpetuate discrimination (for example, bias in hiring tools or credit scoring systems) just as well as human ones. Regular audits, varied data sets, and explainable AI (XAI) tools are all key to reducing these risks.
Additionally, there is reputational risk management, which is mandated. One AI blunder can cost a loss of customer confidence and public opinion. In a lecture I gave at a Business School to Board Directors about what they needed to know about AI I gave the example of Zillow, which is a real estate company that leveraged AI to price properties. Their flawed property valuation algorithm caused Zillow to write down the estimated value of homes purchased in Q3 and Q4 of 2021 by over $500 million, leading to $304 million in Q3 losses and planned workforce reductions of 25%. The company’s algorithms overestimated home values during a period of aggressive expansion when Zillow acquired more properties in two quarters than it had in the previous two years. They then had approximately two-thirds of purchased homes valued below their purchase price. Despite being unaware of the algorithmic problems as they developed, the board of directors remained legally and financially responsible for the resulting financial catastrophe and its impact on shareholders and employees. This highlights how boards need to be AI-literate and aware of what is going on with AI in their organization. It is also important to take pre-emptive action, such as creating ethical AI policies and ensuring transparency around decision-making, to protect the company.
By focusing the laser on high-impact, ROI-positive use cases and being proactive about managing risks, boards can reach for AI’s transformative capabilities while protecting their firm’s competitive edge as well as its ethical reputation.
How to Measure and Communicate AI ROI to Your Board: Transforming Insights into Impact
The crucial part of measuring and communicating effective AI ROI is using detailed and understandable metrics that tie to business priorities and ideally board-level expectations. AI success measures should be in terms of both financial outcomes and operational results.
Revenue, cost, and efficiency gains are basic measures – such as a retail company that can utilize AI-based demand forecasting could see ~10% percent sales increase and 20% reduction in excess inventory costs, or a manufacturer who uses predictive maintenance could get 30% lower equipment downtime and 15% less on repairs.
Beyond financial measures, boards also increasingly expect figures on customer satisfaction (e.g., NPS up since the chatbot came in) and employee productivity (e.g., hours saved by removing manual workflows like cutting manual data entry time by 50%). These are the metrics that turn AI from this abstract thing into something purely tangible, a business performer.
Sustained accountability through regular board reports is an artful mix of actionable insights with intuitive presentation. AI impact dashboards and visualizations are necessary for monitoring the influence of AI in near-real time – successful dashboards must surface KPIs such as ROI, adoption rates, and risk signals through trendlines, heatmaps, and benchmarks.
For example, a financial services board could be shown a quarterly dashboard that highlights how the increase in fraud detection and subsequent cost-savings are versus what was originally predicted.
Quarterly updates should balance the frequency with depth, reports with quarterly perspectives of strategy, and monthly snapshots of operational metrics and emerging risks.
Content formats have to be adapted to board preferences – the combination of summarizing executives’ spreadsheeted conclusions with interactive drill-down visuals allows directors to get the overview but also look into details when required. By providing boards with clear, data-driven updates, they can make well-informed decisions about whether and how to scale AI initiatives, reallocate resources, or close any gaps.
Ethical and Responsible AI: Building Trust and Long-Term Value
The scalable integration of AI is necessary – with a strong focus on ethical and responsible principles that prioritize using the technology to serve humanity, without widening existing gaps or creating new ones. At the root of it all, ethical AI principles come down to transparency, fairness, and accountability. Transparency means that AI decision-making processes are explainable to the stakeholders — Boards, regulators, and end users.
For example, in financial services, AI lending models need to have transparent reasons for why the credit was granted or denied so that they are under less regulatory scrutiny and consumers experience trust. Justice requires that AI treat all individuals fairly, and no one is a victim of discrimination due to biased data.
We can start addressing bias and discrimination by ensuring datasets represent a diverse set of populations – rather than overrepresenting the dominant groups or underrepresenting the marginalized ones. That’s because AI built specifically for healthcare, and optimized on Western data, can deliver inferior diagnoses for individuals across the developing world. Stringent pre-deployment audits, adversarial testing, and real-time monitoring for algorithmic drift are essential precautions.
Sustainable AI Beyond the societal issue of fairness, sustainable AI also has to deal with environmental damage caused by advanced AI technologies: e.g., massive models such as GPT and DALL-E use a tremendous amount of energy. Efficiency in training and inference must be at the forefront of the company’s R&D efforts, using developments such as model distillation, edge computing, and renewable energy-powered data centers.
For example, DeepMind’s AlphaFold advance (which transformed protein structure prediction) was designed with a focus on minimizing compute costs and yet created fundamental leaps forward – although it has been improved upon by BaseFold by BaseCamp Research who achieve 6 times the accuracy of AlphFold due to the rich diversity of species data they include. Boards should require AI sustainability metrics – such as carbon emissions per training cycle – just as they do for broader ESG goals.
Conclusion
Overarching any good AI strategy is alignment with what the organization is trying to achieve, ethical considerations, and the right mix of internal capabilities and external relationships – whether that’s as a company JV, or expert consultants. Boards need to focus not just on adopting AI, but on the integration of AI thinking in corporate decision-making and resource allocation to achieve their objectives.
Through foresight, boards can guide their organizations to become sector leaders and achieve breakthrough results and sustainable value. It is time to lead with AI.
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