How to create an effective pharmaceutical marketing strategy in 2024

 

By Gavin Llewellyn 

Combine opportunity, strategy, and action to identify the right approach for your pharma brand

The last few years have been a rollercoaster ride for many of us, not least those within the pharmaceutical sector. Digital transformation is rapidly altering the way pharma brands communicate and deliver their products: consumers are becoming more knowledgeable and proactive, whilst businesses are having to adapt to an increasingly digital-first world.

And this has all happened alongside broader trends which KPMG believes will have a significant impact on revenues and business and operating models:

“The pharmaceutical sector is at a crossroads. In a heavily disrupted marketplace, characterized by shifting payer attitudes and patient empowerment, neither incremental adjustments nor steady evolution are likely to halt the decline of the traditional pharmaceutical business model.”

As a result of these different trends, it’s important for businesses to evaluate their digital communications and value delivery. Within the pharma sector, brands need to continue innovating and make their online experiences and customer engagement communications more effective. Moving into 2024, it will be crucial to have a clear, coherent and joined-up marketing strategy to compete. 

But why is it important to have a marketing strategy? I’ve highlighted below some of the key elements from Smart Insights’ marketing strategy definition to help explain why?

“A proactive, data-driven approach to marketing and communication activity across all channels and touchpoints. The marketing strategy informs all marketing activity taking place for the business since all marketing plans stem from this overarching structure and vision. Once the strategy is set and communicated, marketers use tactics to put into place their actions that drive to the result.”

Smart Insights has a wide range of strategic marketing guides, resources, and templates for pharmaceutical and healthcare companies looking to develop their marketing strategies. As we know, to succeed in pharma marketing today, marketers and managers must apply a data-driven, customer-focused approach to their strategy and planning, such as our popular RACE Framework lifecycle.

Take steps to optimize your marketing by joining Smart Insights as a Free Member today. Gain instant access to a wealth of digital marketing tools, some of which are mentioned in the article below, designed to help you convert more customers.

Within this post, we’re going to look at some of the key steps you can take to create a pharma marketing strategy that will set you up for success in 2024. 

3 key trends in the pharmaceutical sector

Although many strategic marketing models, frameworks, and plans can be applied to companies across different sectors, we need to first acknowledge the key trends in the pharma sector that will underpin your marketing strategy.

1. The impact of digital transformation and AI on content marketing

Whilst increased global use of AI is no doubt the major trend impacting the pharmaceuticals sector, it’s worth exploring how it has influenced marketing. The pandemic has forced many pharma operations to move online and provided new options to communicate with customers. This has given pharma brands an opportunity to create content for both healthcare professionals (HCP) and patients, and distribute this across different channels.

Now, we see generative AI used to increase content production, although at Smart Insights we recommend taking a blended approach. You can read our Free ChatGPTprompt cheatsheet for more tips on working collaboratively with AI to improve your work.

Needless to say, it’s become more important than ever to have a clear idea of the customer/ patient information journey and how to communicate with them at different stages:

2. The consumerization of healthcare

Pharmaceutical consumers want to have the same experience in healthcare that they have from other businesses. The presence of tech brands like Amazon, Netflix and Meta has exposed us to new information and levels of service that weren’t present 10-15 years ago. This has influenced what we expect from other brands, meaning marketers must take a customer-first approach to their marketing planning.

If you're not already familiar with it, don't miss this handy infographic on 15 uses of Machine Learning, Propensity Modeling, and AI. Even if you're not using all elements depicted in the lifecycle, you can use this structure to plan improvements to your customers' online experiences of your brand.

3. Enhancing consumer engagement

Competitiveness within the pharma sector and the consumerization of healthcare mean that engaging and retaining healthcare customers is crucial. This trend requires pharma companies to review and invest in platforms that they can use to create a consistent view of the customer across many touchpoints, including targeting, segmentation, and performance management:

Use RACE to structure your pharma company's digital strategy

Find out more about how your company can benefit from utilizing the RACE Growth System to strategize each component of your marketing funnel.

From strategy and planning to reaching customers and getting known, from encouraging interaction to increasing conversions, and keeping hold of loyal customers and advocates, the RACE Framework has everything your pharmaceutical marketing team needs to achieve your goals.

What's more, all our marketing tools, training, and templates are integrated across RACE, meaning you can adapt and prioritize key customer journeys to target high-value customers, using data and insights to confidently make decisions about your marketing strategy. Download your free digital marketing plan template to start today.

Optimize your marketing strategy using the Opportunity > Strategy > Action Framework

Opportunity, Strategy, Action is a perfect starting point for pharmaceutical marketing leaders looking to audit and reinforce their marketing strategies. This simple 3-step approach empowers marketers to adapt and react to internal and external factors influencing their customers' lifecycles:

1. Opportunity
2. Strategy
3. Action

To help bring this further to life, let’s look at each section of the OSA framework with some examples of how it can be used to create an effective pharmaceutical marketing strategy.

Opportunity

This stage is about evaluating the current contribution of marketing channels by reviewing your data and setting future objectives.

The consumerization of healthcare will give you a macro view of the current situation and where to explore further. The trend of people influencing and controlling their medical and wellness care is here to stay and is only likely to grow:

The consumerization of healthcare means that there is an opportunity for providers to develop strategies and market offerings that fulfill customer needs and preferences while fully engaging them in an end-to-end customer experience. 

With this background insight in mind consider the following:

• Your current performance and business contribution from digital channels
• The digital maturity of your organization - are you currently set up to tackle the new challenges?
• Review employees marketing skill-set to ensure they have the right tools and capabilities 
• Benchmark your company vs. the competition 
• Set SMART objectives informed by your audience and marketplace analysis

Strategy

Once you have a clear idea of what you want to achieve (objectives), you must define a strategy for how you will get there and which tactics you will use for customer acquisition (Reach), conversion (Act and Convert), and customer retention/loyalty (Engage).

The objective you’ve set in the first stage will help you shape your strategy. For example, if you are developing a new product offering, you may need to focus on Reach to build awareness of your brand and grow your audience. But if you are already established you may instead want to focus on Act to prompt interaction, subscribers, and leads.

The key steps to consider at this stage include:

• Review new business model options
• Update your brand positioning, including your value proposition
• Ensure you have a content marketing strategy 
• Brand governance, including new planning processes or new controls on communications
• A long-term roadmap - it’s likely that your strategic initiatives won’t be achieved within 6 months or even a year 

Action

Finally, you will need to define how your team will execute the strategy and the methods you will use to measure and track success.

In this stage, you will take your objectives and strategy and translate them into an action plan. If your pharma strategy is going to take a very content-oriented approach to inform and engage consumers, for example, Smart Insights’ Content Marketing Blueprint provides a structure and workflow for planning a content marketing program:

Once you’ve established the key actions, the next step is to define the metrics and KPIs to determine success.

The key steps to consider at this stage include:

• Create 90-day action plans for each quarter that show the focus on different techniques across paid, owned, and earned media
• Structure different strategic initiatives and improvements for 90-day plans across RACE (Reach, Act, Convert, Engage)
• Create a detailed budget for prioritized activities across paid, owned, and earned media
• Define dashboards and KPIs to review progress against your targets
• Create a resourcing and development plan to ensure your team has the right skills 

Pharmaceutical marketing bottom line

Pharmaceutical marketing leaders looking for pharma/healthcare marketing solutions need to consider their marketing strategies in the context of the RACE Framework and OSA. By applying a practical, data-driven approach, you can streamline your marketing activities and focus on your patients' customer journeys.

Interested? Discover new opportunities for your company and implement quick changes to start optimizing your marketing funnel. Join as a Free Member to find out more.

https://bitly.ws/3gB5j

Trends in using AI for marketing: 2023-2024

 

What AI-based tools and digital marketing techniques should businesses from small to large be considering?

I’ve been fortunate to be involved in digital marketing for over 25 years now. The opportunities presented by AI recently are the most exciting developments that I have seen in this time, since the early days, where everything from organic search, a website and email marketing seemed a similarly huge opportunity.

This range of projections of advancement in AI presented at Technology for Marketing by Implement AI highlights that we are at a relatively early stage of adoption of AI, with the biggest advancements yet to come recently hinted by the rapid adoption of ChatGPT.

In this post, I’ll summarize trends in real-world applications of AI and tools to consider in each of these categories of AI that are open to any business from small to large. Some of the hottest marketing applications and trends in AI we’ll cover are in these five categories

1. Generative AI
2. Autonomous AI
3. Causal AI
4. Conversational
5. Predictive Analytics

For each technology, we’ll look at how they can be used in marketing and recommend some of the best free and paid tools to consider. Apart from the techniques and tools, in the last section, I’ll also review the issues of governance and management - What actions should businesses be taking to improve to their use of AI.

Generative AI is currently at the peak of interest according to the latest Gartner Hype Cycle on emerging AI technology. This means that in theory, it will soon enter the ‘trough of disillusionment’ and evidence of this is the comments in subreddits such as r/ChatGPT where power users complain of new limitations caused by legal and ethical concerns. There are also recommendations for other ‘Personalized AI’ competitors which I’ll cover below which given the growing popularity of these and specific paid marketing solutions such as Jasper and Writesonic suggest to me that this category is still ‘on the up’.

Of course, the applications of AI in marketing aren’t new. In 2017 we shared these use-cases for Artificial Intelligence (AI) in marketing

Our visual shows the wide array of applications for Machine Learning and AI for marketing across the RACE customer lifecycle, all of which can be put in place today.

None of the technology is speculative or on the horizon, these are current marketing techniques already being utilized by many successful companies) across our customer lifecycle. You can read more about how to integrate new or existing MarTech into your digital marketing strategy in our 2024 edition of The Future of Digital Marketing Trends guide, which is a free download produced in partnership with Technology for Marketing.

A good place to start to review the latest trends in AI is the latest Gartner Hype Cycles which chart which new technologies are ‘on the rise’ and forecast when they may reach their respective plateaus.

1. Generative AI

Developments in Generative AI or Gen AI which produces text, visual and video content from prompts has seen many new features introduced into ChatGPT this year, with ChatGPT now being able to listen and respond to audio prompts, read visuals and with DALL-E integrated, create visuals.

AI image and video generators

This move from text to richer content is one of the trends in this category, with AI now even producing video based on audio or transcripts delivered by human-like avatars through tools like Synthesia  and HeyGen, in which you can overlay pictures of your team to implement avatars based on real people (think ABBA Voyager)!

This year there has been a huge investment in Generative AI, with Microsoft’s investment and collaboration with OpenAI, the obvious example. With Amazon recently betting $4 billion in Anthropic, the developer of Claude we can expect the Amazon re-branded version of Claude to do well in the years ahead.

Fine-tuned Gen AI

You'll know that prompting in ChatGPT works best when Prompts are more specific to what you're looking to achieve, so, in marketing, this means the context for the copy or imagery you're looking to create. In our Quick Win on Using AI for Copywriting I explain a Templating approach which is the most efficient way to define your communications goals, target audience, brand, creative and channels. I also explain how to use ChatGPT's relatively new Custom Instructions feature to re-use supplying the context of your brand, products, customers and brand tone of voice.

While Custom Instructions are a huge improvement to tailor ChatGPT copy to a specific business and audience, they are still limited, so there is a trend to other specific tools for marketers building in fine-tuning. One such is Jasper Brand Voice feature which enables you to upload brand style guidelines or other company information such as campaign briefs to tailor the AI's responses.

Topical Gen AI

Another trend within Gen AI, is that we can expect more regular updates to Large Language models which enable us to work with more topical information than the 2021 currently offered by OpenAI GPT-3 and GPT-4. Although OpenAI doesn’t seem to have cracked this problem yet, Google seems to. You can ask Bard for a summary of the main developments in digital marketing in 2023 and it does a decent job - great for seeing what you may have missed. You can even ask it about trends in AI within marketing for 2024, but the results there are generic compared to this article since it can’t extrapolate as well as a human!

We can also expect that the release of Google’s new Search Generated Experience (SGE) will dramatically increase use of Generative AI when it goes live, which is expected to happen in 2024. This will give Google users an AI conversational response like Bing AI. It’s currently being tested in the US, India and Japan and although Google is testing many changes to balance usability and monetization through Ads, it seems likely it will launch in 2024. Some SEOs such as Eli Schwartz are forecasting an SEO apocalypse as clickthroughs to sites decline as the AI in the SERP answers the user's query.

Personal Gen AI

Finally, another trend within Generative AI is illustrated by Pi from Inflection (founded by ex-Google Deepmind developer Mustafa Suleyman (CEO)). In 2023 Inflection AI announced $1.3 billion of funding led by current investors, Microsoft, and NVIDIA.

Billed as a Personal AI, this currently has a more user-friendly conversational style than ChatGPT which can be voice-enabled and some have compared to the AI in the movie: ‘She’. For me, it’s impressive since it provides a genuine conversation where the AI leads to step you through an issue towards solutions. Compare this to ChatGPT where you have to lead with intelligent prompts to get the most from it…

2. Autonomous AI agents

The future of Autonomous AI agents was highlighted in 2023 when AutoGPT was released. Note that this isn’t an official OpenAI release, although much of the superficial commentary on it suggested it was. Rather it involves clever innovation from one developer to add a coding ‘wrapper’ around ChatGPT via the API. So, it's only available to developers who manually install it from the GitHub code repository. However, it has engaged many developers with its potential, becoming the top trending download on Github.

Microsoft Jarvis is another example showcasing the potential of autonomous agents. Like AutoGPT it can only be setup by developers downloading code - it’s not a service yet. This article on How to Set Up and Try Microsoft Jarvis / HuggingGPT shows the approach through this visual.

So, AutoGPT and Jarvis can connect to and control other web services using APIs and perform actions such as web searching, web forms, and API interactions. AutoGPT works by self-generating the necessary prompts to reach a desired goal. It does this by breaking down the goal into sub-tasks to generate prompts for each sub-task. It then executes the prompts and gathers data to refine or validate its prompts and their outputs. The application then iterates until it completes the tasks and the top-level goal.

For marketers, the impact of AutoGPT is more in showing what AI the future will offer in the future, such as autonomous bots that can be set a task to research a topic and select and buy products, such as the cheapest flight from X toY. In fact, Paul Smith and I wrote about this in our first 2001 edition of Digital Marketing Excellence as a future option, to me it’s still years into the future for widespread adoption.

AutoGPT and Microsoft Jarvis highlights these features of autonomous AI agents. It can :

• Work through a series of steps to achieve a goal
• Chain a series of actions based on prompts
• Take decisions based on the results of previous prompts

More general applications of autonomous AI are self-driving cars and robotic automation.

3. Causal AI

Causal AI is the other category of AI identified by Gartner – see What’s New in Artificial Intelligence from the 2023 Gartner Hype Cycle.

Causal AI will possess more human-like intelligence and will be able to assist in analysis and decision-making. Its aim is to uncover the cause-and-effect relationships between marketing efforts and outcomes. The article above gives these examples of the type of question that can be answered: what if we had only targeted Group A instead of all of Group B? What if we spent an extra $20,000 on TikTok instead of Instagram? How many additional conversions would that deliver? In other words, It lets us go beyond predictive accuracy and get insights into the incrementality of our marketing dollars.

As an innovative technology, there are few competitors in this space. One is Causal Lens which offers to support decision-making by understanding the drivers of behaviour as this case study of retention drivers for an insurance company shows.

4. Conversational AI

For the last two key categories of AI, we return to more established AI marketing capabilities which don’t feature as an emerging AI according to Gartner, but with innovation happening apace in this sector.

Conversational AI is where AI supports direct customer interactions of which there are two types:

• Customer-driven inbound customer enquiries which are sent via web contact forms
• Company-driven outbound communications such as email welcome and nurture sequences which are for promotion and engagement

The main development amongst vendors in this sector relates back to Generative AI where solutions are now less based on rigid templates, but more relevant responses based on prompting with the customer query and tuned to the relevant business question. Autonomous agents will increasingly replace simple questions, but human oversight is mostly still required.

Vendors in this sector include services like Intercom which we use and Drift which offer inbound and outbound capabilities with AI-based engines such as Fin in Intercom and others such as Genesys and Zendesk which focus more on inbound communications.

Another approach relating to the Fine-tuning I mentioned earlier is exemplified by MyAsk AI is to use a standalone AI knowledgebase to which you can upload company documents to answer customer queries using a GPT engine. HubSpot is using this on there site.

5. Predictive Analytics

I’m covering predictive analytics last since in large businesses with business intelligence teams this is one of the longest-established technologies with numerous applications across marketing including

1. Customer Segmentation: Predictive analytics is used to segment customers based on various attributes, such as demographics, behavior, lifetime value and purchase history.
2. Lead Scoring: By analyzing historical data and identifying patterns, predictive analytics can assign scores to leads, indicating their likelihood to convert into customers. This helps marketing and sales teams prioritize their efforts on high-potential leads, leading to more efficient lead management.
3. Churn Prediction: Predictive models forecast which customers are at risk of churning (leaving) based on their behavior and interactions. Marketers can implement retention strategies to reduce customer churn.
4. Personalization and Recommendation Engines: E-commerce and content platforms use predictive algorithms to suggest products, services, or content to users based on their past behaviors and preferences. This enhances the user experience and drives sales or engagement.
5. Marketing Campaign Optimization: Predictive analytics can help optimize marketing campaigns by predicting which channels, messages, and timing are most likely to yield the highest conversion rates. This maximizes the return on investment (ROI) of marketing efforts.

All these types of machine learning applications based on analysis of historical customer interaction data will continue, but supported by the other types of AI innovations we have reviewed such as Causal and Generative AI. The opportunity here is the fast rate of development of these tools, and the practical application of data to improve marketing performance. Of course, with so many different possible areas, it's important to prioritize optimizations that will make the biggest difference to your current marketing objectives. Our guide gives recommendations for planning and reporting too.

Trends in managing AI and governance

Businesses are reviewing the opportunities of AI, but they also need to manage the downside. Implement AI identifies these negative factors of AI that need to be managed in their article on The AI-Assisted Organisation - a blueprint for Small and Medium Businesses.

• Job Displacement. Workers performing repetitive analytical and mechanical tasks face displacement through automation.
• Data Privacy. Concerns collecting, processing and securing ever-growing datasets raises concerns around consent, transparency and misuse that can erode customer trust if not intelligently managed.
• Digital Ethics. As automated systems impact people’s lives, proactively developing ethical frameworks needs to be guided by principles of transparency and accountability.
• Security Risks. Increasing reliance on AI and interconnected systems means system
  security must be considered.

AI policy for marketing communications

We believe that more organisations developing is a major trend given the impact that Generative AI in particular, has had, so we have a separate section on this.

In this podcast, Implement AI reviews further recommendations to AI policy summarized how to manage these challenges for these types of business:

For Large, Enterprise Businesses:

• Create an AI policy framework to provide guidelines on ethics, data privacy, security, and explainability of AI systems across the organisation
• Form an AI committee with cross-functional leaders to govern and continuously review the AI policy and strategy
• Provide comprehensive AI training to employees on using new tools responsibly and optimizing workflows
• Appoint a Chief AI Officer to own and drive the AI strategy and roadmap forward
• Engage stakeholders like customers and employees on AI plans to retain trust and talent

For SMEs:

• Draft an AI policy even if basic to start aligning business goals with AI adoption
• Assign AI responsibility to a senior leader even if part-time to drive strategy
• Evaluate customer data handling and security practices required for AI systems
• Explore AI opportunities to gain competitive advantage through faster task completion
• Be transparent about AI plans with staff to ease uncertainty and align on the vision

By Dave Chaffey

How Enterprises Are Using AI for Email Marketing

 Are enterprise marketers using artificial intelligence to help with their email campaigns? If so, what are the most common uses and challenges?

To find out, RPE Origin and Ascend2 surveyed 110 marketers who work for organizations with 500+ employees.

Some 24% of respondents say they're already using AI extensively in their email marketing campaigns, 33% say they're using it to some extent, 25% are not using it yet but have plans to, 14% have no plans to, and 4% are unsure.

Among enterprise marketers who are using or plan to use AI as part of their email campaigns, 50% are doing so for content personalization, 47% for email retargeting, and 47% for subject line optimization.

Respondents say the top challenges they've faced when implementing AI in email marketing have been data/customer privacy concerns (45% cite), data quality or availability issues (42%), and a lack of internal expertise (40%).

Some 45% of enterprise marketers believe AI will play a central role in email marketing in the future, 47% believe it will have a significant impact but won't replace human creativity, 5% believe it will have limited applications, and 3% are not sure.

About the research: The report was based on data from a survey of 110 marketers who work for organizations with 500+ employees.

https://www.marketingprofs.com/

The Top Generative AI Tools for B2B Marketers

 Which generative AI tools should you be considering?

For B2B marketers, this question has become increasingly difficult to answer as more and more solutions have come to market.

To help sort through the many options available, TopRank Marketing created an infographic (below) that covers more than 30 of the best generative AI tools for B2B marketers.

It looks at top solutions across a range of different areas, including visual content creation, text content creation, and SEO.

https://www.marketingprofs.com/

How to Develop a Strategy for Artificial Intelligence

 

by David Wilsey

Leaders I talk to are expressing a common concern that keeps them up at night: what to do about artificial intelligence (AI). Since the launch of ChatGPT, Google Bard, AI-supported Bing, and other competitors, it has become clear that the strategic environment for most of us is about to be upended by this new technology. A common problem these leaders face is forming a coherent strategy for AI; compounding the problem – they don’t know where to start.

The good news is that formulating a strategy for harnessing the potential of AI and avoiding being left behind is very similar to formulating strategy for any other area of strategic inquiry. Like any other planning effort, the balanced scorecard and the first six steps of the Nine Steps for Success™ can provide a disciplined framework for organizing your team’s efforts. This blog highlights the most important elements to consider. For a more comprehensive understanding of the Nine Steps, I’d recommend reading The Institute Way or taking our Balanced Scorecard Professional Certification class.

Program Launch

Any Nine Steps effort begins with a program launch, where we plan the planning effort itself. The program is launched by a project champion(s) and key stakeholders. Any existing material related to this effort is examined, a gap analysis is completed, key stakeholders are interviewed, and other assessment activities are completed.

Stakeholder Engagement

Part of the program launch is determining who should take part in the strategy formulation. If you want your teams to buy into the strategy, they need to be a part of the process. For an AI effort, this could include executives, department heads, data scientists, IT personnel, legal and compliance teams, and end-users. The goal of any outreach and inclusion is to solicit their input, address concerns, and build strategic alignment.

Step 1: Assessment

Like any other strategy development effort, strategic thinking starts with an assessment of our current situation. Beyond the typical internal and external environmental analysis, the organization will want to do an AI Readiness Assessment.

AI Readiness Assessment

Any strategy formulation effort will require an assessment of organizational readiness. This is even more critical with disruptions like AI. Most organizations will want to evaluate existing technology infrastructure, IT capabilities, and skills gaps. This might include a data assessment to determine the gaps and limitations in data collection, storage, and quality as well as the related data infrastructure, governance, and security. This is when you’ll start the conversation around the rationale for adopting an AI strategy. Depending on the strategy selected later, there might be a need for additional resources, tools, or partnerships to successfully implement AI initiatives. Some superficial training will likely be in order, as everyone will come into the conversation with a different understanding of what AI is and what the possibilities include.

Picture of the Future

Instead of the standard organizational Vision and Mission statement development that would typically happen during Step One, AI strategy will require clearly articulating the organization’s picture for future AI adoption success. Why is the organization going down this path? Is it simply because it is trendy, or is it because of a clearly perceived desired benefit, such as improved efficiency, enhanced decision-making, cost savings, or competitive advantage?

Environmental Analysis

The strategic environment for AI is then assessed, where the team discusses all the known internal and external factors influencing AI adoption. The team should consider technological readiness, data availability and quality, regulatory and ethical considerations, market trends, potential risks/challenges, and competitive landscape.

Step 2: Strategy

Building on the assessment, organizations formulate/clarify strategy in the Strategy step. In an organizational strategy effort this would be about creating high level Strategic Themes, Results, and Perspectives. If we are more narrowly defining an AI strategy, this is where we articulate how AI is influencing our current strategy or define one or more new themes. The strategic themes and results selected will provide high level context for the specific objectives that will be selected and mapped in the next steps related to AI.

Step 3: Strategic Objectives

High level themes are broken down into continuous improvement activities in Step 3. This is where we outline specific objectives related to AI, such as increased revenue growth, improved customer satisfaction, increased operational efficiency, and/or improved innovation. Again, the specifics will depend on the exact AI strategy. Are we incorporating machine learning into our product offerings or are we simply using AI internally to create operational efficiencies?

Step 4: Strategy Map

The objectives identified in Step 3 should work together to tell a coherent cause-effect story. This story is illustrated with a strategy map, which is a graphic that shows the cause-and-effect relationships of objectives across the four perspectives, telling a story of how the organization will achieve the results desired.

Step 5: Performance Measures and Targets

No strategy implementation is effective if you don’t have a way to measure success. In Step 5, metrics/KPIs are defined to measure the effectiveness and impact of the AI initiative. The emphasis in this step is on helping you develop the critical leading and lagging measures needed to manage strategy execution. As measures are implemented, progress is monitored, outcomes are evaluated, and adjustments are made as necessary, using feedback loops to learn from pilot projects and continuously improve AI capabilities.

Step 6: Strategic Initiatives

In Step 6, the projects that are critical to success of the AI strategy are developed, prioritized, and implemented. Initiatives help close performance gaps in performance to hit targets. It is important to focus the organization on the execution of the most prioritized strategic projects versus creating a long list of potential actions and projects. In AI implementations, pilot projects are often developed and implemented to validate AI use cases.

Program Rollout and Strategy Execution Considerations

Once Step 6 is complete, the AI strategy is ready to be rolled out. The goal of this part of the process is to create more internal fans and build a coalition of employees that understand and support the new AI strategy. Once the strategy is ready for implementation, care must be made to manage common strategy execution challenges, such as:

• Implementation: Create a detailed roadmap for AI implementation. Define the sequence of initiatives, resource allocation, timelines, dependencies, and milestones. Consider an iterative and agile approach to accommodate evolving technologies and organizational needs.
• Change Management and Training: Develop a comprehensive change management plan to address the organizational and cultural aspects of AI adoption. Identify training needs for employees to acquire AI-related skills and competencies. Foster a culture of continuous learning and innovation.
• Governance and Ethics: Establish a governance framework for AI to ensure responsible and ethical use. Define guidelines for data privacy, bias mitigation, transparency, and compliance with relevant regulations. Establish mechanisms for ongoing monitoring, evaluation, and accountability.
• Risk Management: Identify potential risks associated with AI implementation, such as cybersecurity threats, algorithmic biases, legal and regulatory compliance, and job displacement. Develop strategies to mitigate these risks and establish contingency plans.
• Collaboration and Partnerships: Explore opportunities for collaboration with external partners, such as AI vendors, research institutions, and industry experts. Leverage their expertise, resources, and best practices to accelerate AI adoption and stay updated with the latest advancements.
• Budget and Resource Allocation: Develop a detailed budget for AI initiatives, including infrastructure, software, talent acquisition, training, and ongoing maintenance. Ensure sufficient resources are allocated to support the implementation and scaling of AI projects.
• Ongoing Strategy Review: Establish a regular strategy review cycle to impose discipline to the execution process, improve performance, create accountability, and adapt strategy to ongoing new developments.

Conclusion

A comprehensive AI strategy is essential for organizations to harness the potential of AI, drive innovation, improve efficiency, and maintain a competitive advantage in today’s rapidly changing business environment. While a more coherent strategic plan might not prevent oncoming AI disruptions, a more disciplined approach to AI planning will communicate the organization’s commitment to continuous improvement, innovation, and responsible AI adoption.

https://balancedscorecard.org/