Tag: technology

Forward Deployed Entrepreneurs

Posted on by simongterry in Future of Work

Forget Forward Deployed Engineers. The real challenges of your most valuable AI projects aren’t in the engineering. The most complex AI use cases to realise will demand Forward Deployed Entrepreneurs in your organisation who are able to identify, test and learn and transform business models. Brace yourself for an onslaught of intrapreneurs redesigning your business models, value chains and business processes powered by AI. This activity will challenge your organisation to govern learning at scale.

Are you ready to redesign your governance to enable the investments, the exponential value creation and the new risks this creates for businesses based on stable economic models?

From Engineers to Entrepreneurs

The Australian Financial Review had a great piece today on how the AI vendors are assisting clients to leverage their models with Forward Deployed Engineers. This takes the technical expertise into the client to enable them to better leverage the power of AI models in organisations. The Engineer joins into client teams to help them see and execute the potential to do better.

As noted in my recent piece on Clusters of AI Use Cases, the Unlock and Reinvent phases realise significant value because they leverage AI to start tackling change to business models, value chains and customer propositions. Your team members who work with a Forward Deployed Engineer need to be Forward Deployed Entrepreneurs to be able to make the changes required to transform your organisation.

The Entrepreneur part of this is relatively easy. Most organisations can find the creative and motivated individuals who want to test and learn a better way of doing business. Hiring them and keeping them is a much bigger issue. It’s not the capabilities or the challenge of the work. Entrepreneurs get a buzz from learning and overcoming challenges. 

Lifting the valleys of the sea
my father moved through griefs of joy;
praising a forehead called the moon
singing desire into begin

ee cummings. my father moved through dooms of love

Entrepreneurs change things a lot

The biggest barrier to success of a Forward Deployed Entrepreneur and the key issue with their retention is whether you will let them make the changes needed.

The single biggest blockage is likely to be your long standing static, safety first, no loss, plan-based internal governance processes. The Forward Deployed Entrepreneurs need to be able to make changes to systems, processes, business models, customer relationships, products and more. They need to make those changes fast, iteratively and with a willingness to fail. They need investment, access to specialists and supportive stakeholders who are willing to take a risk to find much better.

Business model transformation happens iteratively. The business part of this adventure is far harder than the technology part because it involves not just doing something but all the considerations that your organisation makes any change work its way through such as implications on other lines of business and customers, risk considerations, sustainability considerations, IT security, financial business cases, investment hurdles and many many more. All these are designed around incremental changes like launching a new product or fixing a broken process. The work required to pass through these for a fast moving and transformational change driven by new AI technology is likely to be exhausting, especially if every iteration has to follow through every step.

If you want Forward Deployed Entrepreneurs to work on the big issue of Unlock and Reinvent then you need to support them with investment for the learning required, the autonomy to make big changes iteratively and also a governance process that adapts to the very different challenges that come from entrepreneurship. 

csince feeling is first
who pays any attention 
to the syntax of things
will never wholly kiss you;

ee cummings, [since feeling is first]

Redesigning Governance for Entrepreneurs

I was asked recently the biggest difference between governance in a fintech and governance in a large organisation. My response was that the governance in a fintech was all focused on the lessons from the activities and how they informed decisions to pivot or persevere and what those lessons were revealing about the future opportunities and value of the organisation.

Because there was an expectation of learning the governance understood that a lot might change from one governance meeting to the next. Autonomy was baked into the way the organisation ran and was an expectation in all roles. Risk was an accepted part of this learning process and the risks were managed actively. The plan was a guide to that learning experience and a measure of runway.

In most cases, large organisations use governance as a backwards looking exercise to understand variances to plan, reduce volatility, and what that means for the forecast. Risks were to be mitigated or avoided. Autonomy is within agreed plans. Investments and business decisions were slow and long processes designed around an annual planning cycle. This difference in governance makes test and learn business model transformation a challenge and will drive Forward Deployed Entrepreneurs (& their Engineering colleagues) to despair. Because your existing business model is proven and relatively stable you have built governance to secure that stability, not for rapid learning.

To explore the opportunities to Unlock and Reinvent your business models, value chains and customer propositions, you need to have entrepreneurship and an environment in your organisation for that learning to thrive. How are you enabling your people to learn at scale, to make the changes required for success and how are you changing your governance to support them?

For whatever we lose(like a you or a me)
it’s always ourselves we find in the sea

ee cummings, maggie and milly and molly and may

Simon Terry is a consultant, advisor, and non-executive director who focused on how organisations can better leverage technology, collaboration, leadership and learning to achieve innovation and business growth, particularly in financial services, healthcare and education.

Five Clusters of AI Use Cases

Posted on by simongterry in Future of Work

AI presents a unique transformational opportunity for organisations. Hand wringing has begun about potential job losses as a result of the implementation of AI capabilities. However, it is already clear that AI goes beyond the potential of efficiency. The purpose of this piece is to provide a framework for AI options that might grow organisations and opportunities for your people. 

In periods of rapid technological change, shared sense making is important so that we can build on the experiences of others. This post doesn’t seek to provide answers or even exact models, just some signposts to where your organisation might want to go next. It is important to note that one organisation may pursue one or more of these clusters of use cases at any time

I shall be telling this with a sigh
Somewhere ages and ages hence:
Two roads diverged in a wood, and I—
I took the one less traveled by,
And that has made all the difference.

Robert Frost, The Road Not Taken  (because you may travel all roads with AI)

Five Clusters of AI Use Cases

Work so far around the world by organisations implementing AI highlights five different use case clusters that have wildly varying benefits and implications for organisations. 

These clusters are

  • Ignore – do nothing and wait for more information or capabilities
  • Deploy – rollout AI to one or more employees
  • Replace – leverage AI to replace human tasks
  • Unlock – Use AI to unlock constraints in current business models
  • Reinvent – Use AI to do something new, different or innovative

Here is each of those AI Use Case Clusters described in a handy table:

Till even the comforting barn grows far away
And my heart owns a doubt
Whether ’tis in us to arise with day
And save ourselves unaided.

Robert Frost - Storm Fear

Observations on these Patterns

Doing Nothing is Not An Option: New technology takes time to be mastered. Making sense of the capabilities requires testing and learning and making sense of its application and risks in your context. Without experimentation you are likely to be at the mercy of disruption by others or only benefit from generic capabilities that flow to everyone.

Reducing People is Not Inevitable. Changing what People Do is: The Unlock and Reinvent use cases may both lead to increased level of people in your organisation supporting expansion of new levels of activity and new work. However, it is likely that simple, repetitive tasks, content creation, and review, transcription, and analysis of information will be automated using AI. Human activity will move to higher value and likely higher paid tasks involving governance, discretion and person-to-person interaction.

The Highest Value is the Hardest Work & Risk: Not surprisingly, the greatest return comes from exercises to explore the uncertainty created by the opportunities of new technology. That means great risk of failure but also greater potential returns. Realising greater returns is going to require investment, governance and effort. Accidental success is always possible but systemic effort produces better consistent outcomes.

All at once: While there are different capabilities required to execute each of the clusters above, this is not a maturity model. As noted above your organisation is likely to need to consider a little bit of everything from the use case patterns above. There will be areas where wait and see is wise. You may also have areas where it is urgent today to unlock your business model or reinvent it. What is clear is that almost everyone will explore the opportunities to remove tasks that are mundane and repetitive using AI.

Get ready to be surprised: New technology offers entrepreneurs new ways to explore business processes, value chains and business models. Every industry will have some form of new entrant and new model to consider. Not all of these models will succeed but there are likely to be shocks and adjustments along the way. Organisations need to invest in their capabilities to learn, experiment, adapt and react.

Best of luck with your adoption of AI in your organisation. I hope you found these clusters useful in your efforts to make sense of AI adoption. Let me know your thoughts in the comments. Particularly, let me know what I missed.

The woods are lovely, dark and deep.
But I have promises to keep,
And miles to go before I sleep,
And miles to go before I sleep.

Robert Frost - Stopping by Woods on a Snowy evening

Simon Terry is a consultant, advisor, and non-executive director who focused on how organisations can better leverage technology, collaboration, leadership and learning to achieve innovation and business growth, particularly in financial services, healthcare and education.

AI Effectiveness

Posted on by simongterry in Future of Work

Routine tasks have been automated for centuries. Achieving greater efficiency with AI is an inevitable outcome of the ongoing automation of routine work. The greater challenge that few organisations are organised to realise is leveraging AI for greater effectiveness of purpose by serving more customers, doing so better than ever before or through innovation.

Human Limitations

Humans are terrible at forecasting in times of non-linearity. Our mental models do not cope well with the pace of change and the discontinuities that flow from rapid changes in scale. One can see that inability to predict in a recent set of scenarios from the US Federal Reserve shared in the Financial Times. AI is either going to deliver nirvana, destroy us or deliver 2% better growth. Within that range lies all of human history. The Federal Reserve economist are doing their best but with unpredictable non-linear outcomes forecasting is not much value. Remember this when you see any AI forecasts.

When our mental models don’t stretch to imagine a non-linear future, we are tempted to revert to safe and secure heuristics. One reason so much of the AI discourse has been around job losses is that many managers default to ‘new technology = efficiency = job losses’. For organisations that have long believed the goal of business is to deliver a consistent repeatable process and then remove cost through efficiency, at first flush, AI looks like a potential massive cost efficiency. There will undoubtedly be significant cost efficiencies to be realised.

The recent McKinsey State of AI Global Survey highlights that effiency is in the forefront of management considerations. 80%+ of respondents are setting efficiency targets for their organisations in the US of AI. The reality is that the automation of routine work began in the Industrial Revolution and the use of AI to automate even more complex routine tasks is inevitable.

The AI Effectiveness Challenge

The same McKinsey report notes that the best performing organisations in realising value from AI are setting goals beyond efficiency. They are also pursuing AI’s role in Growth and Innovation. Oraganisations can leverage their current cost base to achieve more, though management’s preference for the predictable certainties of cost saving can mean this gets lost in discussion.

A key point to remember is that the model of driving scale efficiencies in a predictable process is one that began long before the internet, let alone AI. Much of the gains of that model came as consumer capitalism spread scale of distribution to global markets. Routine work was in decline in growing mature economies well before AI. With the arrival of a connected global market with real-time information, many organisations have found an efficiency only model more challenging to sustain, either having to re-engineer themselves offshore to lower cost markets or building complex supply chains from global vendors. With these actions comes new threats, such as new distruptions come from those scaled global supply chains or new competitors leveraging global supply in new ways using digital technology. AI is going to drive new levels of threat to the disruption and disaggregation of those predictable business models. Financial services executives are already pondering the implications of consumer AI agents moving money and arranging services at the speed real time finance. No steady scaled franchise is safe in that world. Threats like this one will develop over time into all industries. Just cutting cost won’t be enough to survive. Winning organisations will leverage AI to rethink their customer experiences and their entire value chain using the new economics of AI.

Aaron Levie, CEO of Box, in a long tweet on 7 November 2025 highlighted that an opportunity he is seeing in Box’s client base is leveraging AI to pursue opportunities that organisations would not have seen as economic to pursue with labour. Organisations have let assets like data, content, channels and relationships lie fallow because they were uneconomic to pursue with traditional labour intensive models. Constrained management made people choose the obvious high value use cases to pursue. The rest were deprioritised and simply ignored. That ignored long tail of opportunities is now more in reach for innovation using AI’s capabilities to analyse, sythesize, predict, recommend decisions and automate actions.

What was once uneconomic to do for customers, employees or partners, will slowly become expectations, particularly as other organisations drive expectations of service by deploying AI against those opportunities. AI will drive new competition at new pace, whether startups nipping at corporate heals, international organisations developing solutions with the new economics for the Bottom of the Pyramid or competitors using Clayton Christensen’s disruption to attack from the tail.

Organising for Effectiveness

Being aware of the opportunity or threat is one thing, being able to respond to an opportunity to be more effective is another thing entirely. Most organisations have made it almost impossible for employees or executives to prioritise increasing effectiveness through innovation and growth. Budgets don’t exist for that work as we are used to funding investment through the safety of efficiency. Employees don’t have the freedom to radically redesign business processes or policies to leverage the new capabilities of AI. Even organisational structures (and their power bases) established around existing customer segments and processes will get in the way.

Innovation, growth and effectiveness require agility, entrepreneurship, experimentation and change, not stable consistent scaled processes. To meet the coming AI Effectiveness challenge organisations are going to have to build new capabilities, learn new skills, change systems and unlearn a great deal of ‘modern management theory’. Organisations that do not challenge themselves to be more effective for their customers, communities, employees and partners will find themselves left behind. No matter how few employees they have in their efficient organisations, a more effective organisation will take their customers away with innovation and enhanced experiences.

The time to start radically rethinking propositions, the value chains and priorities of your organisation around the capabilities is now while your competitors are pursuing efficiency. Efficiency is fast follower territory. You can always copy what works later and technology vendors will eagerly assist you to do so by building the easy common use cases into platforms. What is much harder and takes new and unique organisational capabilities are the innovations and growth you will drive with AI. Start the experiments to explore that work today. Investment into this non-linear innovation will yield valuable insights and potentially enduring advantage. Your success will be much harder to replicate and may even be invisible to competitors stuck in traditional management mindsets.