SHARP is an enterprise AI strategy framework that helps leaders decide where AI belongs in their organisation and how to adopt it responsibly. It stands for Strategic alignment, Human accountability, Architecture and assurance, Readiness and resourcing, and Prioritisation and proof. Each letter is a test that an AI initiative should pass before it gets funding, people and executive attention.
Who this is for: executives, board members, transformation and risk leaders, and anyone responsible for an organisation’s portfolio of AI initiatives.
Why leaders need an enterprise AI strategy framework
Most organisations now have more AI ideas than they can sensibly pursue. Vendors pitch new features, teams experiment with tools, and boards ask what the organisation is doing about AI. Without a shared way of judging initiatives, decisions get made on enthusiasm, fear of missing out or whoever argues loudest.
The result is familiar: a scattered set of pilots that never scale, unclear ownership, and risks that only surface after something goes wrong. An enterprise AI strategy framework gives leaders a consistent set of questions to ask of every initiative, so the portfolio reflects strategy rather than noise.
The SHARP framework, letter by letter
S: Strategic alignment
Does this initiative serve a clear business objective? AI is a means, not an end. The strongest initiatives are tied to a specific outcome the organisation already cares about, such as faster loan turnaround, lower cost to serve, better customer experience or reduced risk.
Leaders should ask: Which strategic objective does this support? How will we know it has made a difference? What happens if we do not do it?
H: Human accountability
Who is responsible for the outcome? Every AI initiative needs a named business owner who is accountable for its results, its risks and its effect on customers and staff. Accountability cannot be handed to a vendor, a data science team or the tool itself.
Leaders should ask: Who owns this? Which decisions will people continue to make, and which will AI support? Who can pause or stop it?
A: Architecture and assurance
Is it built and governed properly? This covers data quality and access, security, privacy, integration with existing systems, testing, monitoring and the ability to explain how the AI reached its output. In regulated sectors, it also covers how the initiative fits existing obligations.
Leaders should ask: What data does it use, and are we allowed to use it this way? How will we test it before launch and monitor it afterwards? Could we explain its outputs to a customer, auditor or regulator?
R: Readiness and resourcing
Are the organisation and its people ready? Many AI initiatives fail not because the technology is weak but because processes, skills and budgets are not in place. Readiness includes training, change management, process redesign and the ongoing cost of running and maintaining the solution.
Leaders should ask: Do the people who will use this know how to use it well? Have we funded the whole lifecycle, not just the build? Which processes need to change?
P: Prioritisation and proof
Is this the right initiative now, and can we prove it works? Leaders should rank initiatives by value, risk and effort, start small, and set clear evidence thresholds before scaling. Proof means measured results against a baseline, not anecdotes or vendor claims.
Leaders should ask: How does this compare with our other options? What would a small, measurable pilot look like? What evidence do we need before scaling?
How SHARP and TRACE work together
SHARP operates at the portfolio and leadership level. It helps decide which AI initiatives to pursue and how to govern them. TRACE (Task, Restrict, Augment, Check, Evidence) operates at the team level. It helps people use AI safely in their daily work.
Used together, they connect board decisions with frontline practice. SHARP sets the direction and the guardrails; TRACE turns those guardrails into everyday habits. Both share one principle: if you can’t trace it, you can’t defend it.
Using SHARP in practice
Here is a simple way to apply this enterprise AI strategy framework across your portfolio:
- List every AI initiative, including pilots, vendor features being switched on and informal team experiments.
- Score each against the five SHARP tests, for example on a scale of one to five, with a short note explaining each score.
- Look for gaps. Initiatives that score well on strategic alignment but poorly on accountability or readiness need fixing before they proceed.
- Prioritise. Fund a small number of high-value, well-governed initiatives rather than many thin pilots.
- Review quarterly. Re-score initiatives as evidence comes in, and stop the ones that are not delivering.
A quick example of the enterprise AI strategy framework
A lender is considering AI to summarise loan applications for assessors. On SHARP: it aligns strongly with the goal of faster turnaround (S); the head of credit owns it and decisions stay with assessors (H); data use and testing need work before launch (A); assessors need training and the process needs a mandatory source check (R); and a three-month pilot with one team, measured against current turnaround times, will provide the proof (P). The framework shows not only whether to proceed, but what has to be in place first. Our article on AI training for banks covers the readiness side in more detail.
SHARP and the changing regulatory picture
Expectations on organisations using AI are rising. In Australia, the Government has announced plans for mandatory national AI standards, as covered in our article on Australian AI Standards. The Australian Government’s Guidance for AI Adoption also emphasises accountability, risk management and human oversight. An organisation that applies SHARP consistently will already have much of the governance evidence that regulators, auditors and boards are likely to ask for.
Common mistakes SHARP helps avoid
- Technology-first projects with no clear business outcome.
- Orphaned pilots with no accountable owner.
- Underfunded rollouts that pay for the build but not the training or ongoing running costs.
- Scaling on hope rather than measured evidence.
- Governance as an afterthought, added only after a problem appears.
Key takeaways
- SHARP is an enterprise AI strategy framework built on five tests: Strategic alignment, Human accountability, Architecture and assurance, Readiness and resourcing, Prioritisation and proof.
- It helps leaders choose fewer, better AI initiatives and govern them properly.
- SHARP works at leadership level; TRACE turns it into daily practice for teams.
- Scoring and reviewing initiatives quarterly keeps the portfolio aligned with strategy.
Frequently asked questions
What does SHARP stand for?
Strategic alignment, Human accountability, Architecture and assurance, Readiness and resourcing, and Prioritisation and proof.
Who should use SHARP?
Executives, boards and leaders responsible for deciding which AI initiatives to fund and how to govern them.
Is SHARP only for large organisations?
No. Smaller organisations can apply the same five tests in a single conversation before investing in an AI tool or project.
How is SHARP different from an AI policy?
An AI policy sets rules for how staff use AI. SHARP helps leaders decide which AI initiatives to pursue and what governance each one needs.
Bring SHARP to your leadership team
Talentreon runs leadership sessions and workshops that apply SHARP to your organisation’s real AI initiatives, in person in Sydney or virtually worldwide. See our services or get in touch to discuss a session.
About the author: Sharrda Mani is the founder of Talentreon. She has more than 15 years’ experience in business, technology and complex project delivery, has worked with ASX-listed companies, and delivered ANZ’s online credit card application under the National Consumer Credit reforms.